metadata license: agpl-3.0
library_name: edgefirst
pipeline_tag: object-detection
datasets:
- VisDrone2019-DET
tags:
- edge-ai
- npu
- tflite
- onnx
- int8
- yolo
- edgefirst
- visdrone
- aerial
- small-object-detection
- nxp
- hailo
- jetson
- qualcomm
- embedded
model-index:
- name: visdrone-yolov8-det
results:
- task:
type: object-detection
dataset:
name: VisDrone2019-DET val
type: visdrone
split: val
metrics:
- name: APs (Nano 640 ONNX FP32)
type: map_small
value: 10.14
- name: AP (Nano 640 ONNX FP32)
type: map
value: 18.6
- name: AP50 (Nano 640 ONNX FP32)
type: map_50
value: 32.57
- name: AP75 (Nano 640 ONNX FP32)
type: map_75
value: 18.11
- name: APm (Nano 640 ONNX FP32)
type: map_medium
value: 27.89
- name: APl (Nano 640 ONNX FP32)
type: map_large
value: 45.53
- name: AR@1 (Nano 640 ONNX FP32)
type: mar_1
value: 8.64
- name: AR@10 (Nano 640 ONNX FP32)
type: mar_10
value: 23.31
- name: AR@100 (Nano 640 ONNX FP32)
type: mar_100
value: 31.12
- name: ARs (Nano 640 ONNX FP32)
type: mar_small
value: 21.21
- name: ARm (Nano 640 ONNX FP32)
type: mar_medium
value: 44.61
- name: ARl (Nano 640 ONNX FP32)
type: mar_large
value: 63.16
- name: APs (Small 640 ONNX FP32)
type: map_small
value: 13.78
- name: AP (Small 640 ONNX FP32)
type: map
value: 22.28
- name: AP50 (Small 640 ONNX FP32)
type: map_50
value: 38.47
- name: APs (Medium 640 ONNX FP32)
type: map_small
value: 15.89
- name: AP (Medium 640 ONNX FP32)
type: map
value: 25.14
- name: AP50 (Medium 640 ONNX FP32)
type: map_50
value: 42.14
- name: APs (Nano 1280 ONNX FP32)
type: map_small
value: 20.02
- name: AP (Nano 1280 ONNX FP32)
type: map
value: 29.41
- name: AP50 (Nano 1280 ONNX FP32)
type: map_50
value: 48.26
- name: AP75 (Nano 1280 ONNX FP32)
type: map_75
value: 29.97
- name: APm (Nano 1280 ONNX FP32)
type: map_medium
value: 40.91
- name: APl (Nano 1280 ONNX FP32)
type: map_large
value: 49.15
- name: AR@1 (Nano 1280 ONNX FP32)
type: mar_1
value: 11.54
- name: AR@10 (Nano 1280 ONNX FP32)
type: mar_10
value: 33.66
- name: AR@100 (Nano 1280 ONNX FP32)
type: mar_100
value: 44.38
- name: ARs (Nano 1280 ONNX FP32)
type: mar_small
value: 35.81
- name: ARm (Nano 1280 ONNX FP32)
type: mar_medium
value: 56.6
- name: ARl (Nano 1280 ONNX FP32)
type: mar_large
value: 64.29
- name: APs (Small 1280 ONNX FP32)
type: map_small
value: 24.05
- name: AP (Small 1280 ONNX FP32)
type: map
value: 32.77
- name: AP50 (Small 1280 ONNX FP32)
type: map_50
value: 53.01
- name: APs (Medium 1280 ONNX FP32)
type: map_small
value: 26.69
- name: AP (Medium 1280 ONNX FP32)
type: map
value: 35.88
- name: AP50 (Medium 1280 ONNX FP32)
type: map_50
value: 56.96
- name: APs (Nano 1344x768 ONNX FP32)
type: map_small
value: 21.6
- name: AP (Nano 1344x768 ONNX FP32)
type: map
value: 30.29
- name: AP50 (Nano 1344x768 ONNX FP32)
type: map_50
value: 49.23
- name: AP75 (Nano 1344x768 ONNX FP32)
type: map_75
value: 30.87
- name: APm (Nano 1344x768 ONNX FP32)
type: map_medium
value: 41.61
- name: APl (Nano 1344x768 ONNX FP32)
type: map_large
value: 53.41
- name: AR@1 (Nano 1344x768 ONNX FP32)
type: mar_1
value: 12
- name: AR@10 (Nano 1344x768 ONNX FP32)
type: mar_10
value: 34.8
- name: AR@100 (Nano 1344x768 ONNX FP32)
type: mar_100
value: 46.09
- name: ARs (Nano 1344x768 ONNX FP32)
type: mar_small
value: 37.2
- name: ARm (Nano 1344x768 ONNX FP32)
type: mar_medium
value: 59.07
- name: ARl (Nano 1344x768 ONNX FP32)
type: mar_large
value: 71.32
- name: APs (Small 1344x768 ONNX FP32)
type: map_small
value: 25.91
- name: AP (Small 1344x768 ONNX FP32)
type: map
value: 34.5
- name: AP50 (Small 1344x768 ONNX FP32)
type: map_50
value: 55.59
- name: APs (Medium 1344x768 ONNX FP32)
type: map_small
value: 28.46
- name: AP (Medium 1344x768 ONNX FP32)
type: map
value: 36.89
- name: AP50 (Medium 1344x768 ONNX FP32)
type: map_50
value: 58.87
- name: APs (Nano tiled ONNX FP32)
type: map_small
value: 19.98
- name: AP (Nano tiled ONNX FP32)
type: map
value: 28.48
- name: AP50 (Nano tiled ONNX FP32)
type: map_50
value: 47.82
- name: AP75 (Nano tiled ONNX FP32)
type: map_75
value: 28.65
- name: APm (Nano tiled ONNX FP32)
type: map_medium
value: 39.52
- name: APl (Nano tiled ONNX FP32)
type: map_large
value: 48.53
- name: AR@1 (Nano tiled ONNX FP32)
type: mar_1
value: 11.45
- name: AR@10 (Nano tiled ONNX FP32)
type: mar_10
value: 33.82
- name: AR@100 (Nano tiled ONNX FP32)
type: mar_100
value: 43.71
- name: ARs (Nano tiled ONNX FP32)
type: mar_small
value: 35.04
- name: ARm (Nano tiled ONNX FP32)
type: mar_medium
value: 56.23
- name: ARl (Nano tiled ONNX FP32)
type: mar_large
value: 58.42
- name: APs (Small tiled ONNX FP32)
type: map_small
value: 23.69
- name: AP (Small tiled ONNX FP32)
type: map
value: 32.42
- name: AP50 (Small tiled ONNX FP32)
type: map_50
value: 53.39
- name: APs (Medium tiled ONNX FP32)
type: map_small
value: 26.32
- name: AP (Medium tiled ONNX FP32)
type: map
value: 34.69
- name: AP50 (Medium tiled ONNX FP32)
type: map_50
value: 56.34
YOLOv8 Detection on VisDrone — EdgeFirst Model Zoo
YOLOv8 detection models trained on VisDrone2019-DET (10 classes: pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, motor) and validated on its val split (548 images) on real edge hardware with the EdgeFirst Profiler, which runs the pipeline and computes the accuracy on the device itself. Each row in the tables below cites the EdgeFirst Studio validation session (v-XXXX) that produced the measurement.
VisDrone is drone footage: most objects are a few pixels across, so small-object AP (APs) leads every table, followed by the full COCO-style metric set. Every model is published in four input variants that trade time per image for small-object accuracy. For the same family trained on COCO, see EdgeFirst/yolov8-det .
Part of the EdgeFirst Model Zoo . New here? Introducing the EdgeFirst Model Zoo
Training experiment : View on EdgeFirst Studio — dataset, training configuration, metrics, and exported artifacts.
Input variants
Each model and size ships in four variants, one folder each. They differ in the input the model sees and in the weights it was trained with, and each one buys small-object AP with time per image:
640 is the baseline: one 640×640 pass over the whole frame.1280 shows what a bigger input alone buys: one 1280×1280 pass over the whole frame.1344x768 gives the best result: weights trained on 640×640 tiles run natively on the whole frame at 1344×768, where memory allows.tiled (SAHI) slices the frame into 640×640 tiles, runs every tile, and merges the detections. It serves constrained targets, and images larger than VisDrone's ~8 MP, while keeping the benefit of tiled training.
Variant
Folder
Input
Weights
Inference
640
640/640×640
Full-frame
One pass over the whole frame
1280
1280/1280×1280
Full-frame
One pass over the whole frame
1344x768
1344x768/1344×768
Tiled
One pass over the whole frame
tiled
tiled/640×640 tiles
Tiled
Sliced inference (SAHI): every tile, then merge
Time per image
Every latency and FPS figure on this card is per image , whatever the image strategy: one 640² pass, one 1280² pass, one 1344×768 pass, or all the 640² tiles of an image plus slicing and merging. The four variants therefore compare directly.
For the tiled variant, time per image is roughly the number of tiles times the time per tile when tiles run one at a time, plus the cost of slicing the frame and merging the detections. Running tiles concurrently helps when the accelerator has spare capacity or when CPU pre- and post-processing can overlap NPU inference; it does not help when one tile already saturates a single NPU, or when memory is the limit (for example the NPU memory pool on the Toradex Verdin i.MX 95). Tiles overlap so that an object cut at a tile border is detected whole in the neighbouring tile; the overlap adds tiles, and therefore time, and produces duplicates that the merge step removes.
Reference accuracy — ONNX FP32
Accuracy ceiling for each variant and size, measured against VisDrone2019-DET val (548 images) with COCO-style metrics. Quantized and compiled artifacts are graded against the reference of the same variant.
Variant
Size
Params
APs
AP
AP50
AP75
APm
APl
AR@100
Source
640
Nano
3.2M
10.14%
18.60%
32.57%
18.11%
27.89%
45.53%
31.12%
v-1966
640
Small
11.2M
13.78%
22.28%
38.47%
21.95%
32.13%
45.41%
35.20%
v-196a
640
Medium
25.9M
15.89%
25.14%
42.14%
25.71%
36.96%
46.11%
37.97%
v-196e
1280
Nano
3.2M
20.02%
29.41%
48.26%
29.97%
40.91%
49.15%
44.38%
v-1967
1280
Small
11.2M
24.05%
32.77%
53.01%
33.97%
44.22%
51.08%
47.64%
v-196b
1280
Medium
25.9M
26.69%
35.88%
56.96%
37.44%
47.77%
57.21%
50.66%
v-1970
1344x768
Nano
3.2M
21.60%
30.29%
49.23%
30.87%
41.61%
53.41%
46.09%
v-1969
1344x768
Small
11.2M
25.91%
34.50%
55.59%
35.61%
46.20%
53.02%
49.93%
v-196d
1344x768
Medium
25.9M
28.46%
36.89%
58.87%
38.61%
48.80%
58.07%
51.96%
v-1972
tiled
Nano
3.2M
19.98%
28.48%
47.82%
28.65%
39.52%
48.53%
43.71%
v-1968
tiled
Small
11.2M
23.69%
32.42%
53.39%
33.52%
44.01%
47.30%
47.42%
v-196c
tiled
Medium
25.9M
26.32%
34.69%
56.34%
36.16%
46.29%
53.24%
49.84%
v-1971
On-target validation results
Each row is one EdgeFirst Studio validation session. Click the Source link to inspect the full session — model artifact, dataset version, parameters, per-stage Perfetto trace, and the host hardware description.
Row conventions in the tables below:
Rows whose Δ APs cell reads ref are the ONNX FP32 reference runs each quantized or compiled measurement is graded against.Δ APs vs FP32 is the small-object AP change against the same variant and size's reference, in percentage points.Rows whose Δ APs cell carries a ⚠ measure more than 10 percentage points of APs below the same variant's float reference: below our expectations for that platform. The numbers are real measurements, reproducible from the linked Studio session, and we are investigating them.Not supported: …Runtime is the inference stack that executed the model; Decode is what the exported graph emits (Smart lifts the operations that quantize worst out of the graph and needs a decoder implementing that cut, the EdgeFirst HAL in EdgeFirst deployments; Logical keeps the head's outputs separate and named; — means no quantized decode split). Full converter documentation: EdgeFirst model conversion .Platform-label suffixes. (EVK) / (FRDM) / (Phytec) / (Verdin) / (Ezurio) name the development board a session ran on. — latency / — throughput mark the two pipeline configurations of the NXP i.MX 95 Neutron and NXP Ara240 targets: serial inference for the lowest latency, or several inference workers for the highest FPS.Time per image (ms) is the end-to-end latency for one image, from the moment the frame is available to the moment its results are ready — preprocess, inference (every tile, for the tiled variant), postprocess and merge. Image acquisition (file load and JPEG decode) and writing results to disk are excluded. Realized FPS is the measured steady-state rate of finished images over the full validation pipeline.The full metric set (AP, AP50, AP75, APs, APm, APl and AR at 1, 10 and 100 detections and per object size) for every row is under Full metrics below each table.
640 — 640×640, full-frame weights
Size
Platform
Runtime
Decode
APs
Δ APs vs FP32 (pp)
AP
AP50
Time per image (ms)
Realized FPS
Source
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
10.14%
ref
18.60%
32.57%
9.51
335.9
v-1966
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
10.03%
-0.11
18.53%
32.50%
7.72
348.5
v-19bd
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
3.18%
-6.96
10.18%
21.10%
288.75
4.6
v-1b70
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
3.18%
-6.96
10.18%
21.10%
75.28
14.9
v-1a7c
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
3.18%
-6.96
10.18%
21.10%
75.32
14.9
v-1a7e
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
8.65%
-1.49
16.74%
29.91%
85.12
16.2
v-1a7f
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
3.48%
-6.66
10.74%
21.95%
91.92
14.8
v-1bf4
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
8.99%
-1.15
17.28%
31.03%
48.22
47.2
v-1702
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
8.99%
-1.15
17.28%
31.03%
49.56
60.3
v-19d7
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
8.70%
-1.44
16.91%
30.35%
34.80
50.6
v-1a01
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
2.61%
-7.53
9.46%
19.93%
41.66
29.7
v-19fd
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
3.10%
-7.04
10.29%
21.30%
42.88
28.6
v-19fb
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
8.70%
-1.44
16.91%
30.35%
53.73
80.1
v-1a03
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
2.61%
-7.53
9.46%
19.93%
60.76
75.7
v-19ff
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
3.10%
-7.04
10.29%
21.30%
58.82
81.8
v-19fc
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
8.70%
-1.44
16.91%
30.35%
53.33
78.7
v-175d
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
8.99%
-1.15
17.28%
31.03%
50.42
45.7
v-16f5
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
8.99%
-1.15
17.28%
31.03%
54.16
53.8
v-19ea
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
5.41%
-4.73
13.58%
25.94%
17.51
104.4
v-159d
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
5.40%
-4.74
13.58%
25.93%
18.52
191.4
v-1901
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
9.34%
-0.80
17.67%
31.78%
17.85
76.5
v-15d9
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
10.30%
+0.16
18.70%
32.87%
34.48
183.1
v-15b0
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
8.10%
-2.04
16.51%
30.20%
32.51
187.8
v-15b1
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
3.84%
-6.30
11.44%
22.90%
11.29
499.1
v-1536
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
9.20%
-0.94
17.58%
31.49%
10.56
295.9
v-1537
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
10.13%
-0.01
18.58%
32.55%
16.90
254.3
v-1539
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
9.01%
-1.13
17.19%
30.76%
10.82
298.2
v-1d2c
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
3.37%
-6.77
10.67%
21.68%
11.29
490.0
v-1d2d
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
9.87%
-0.27
18.42%
32.37%
17.91
249.0
v-1d2e
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
9.54%
-0.60
17.97%
32.13%
13.01
247.2
v-1d30
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
9.87%
-0.27
18.42%
32.43%
11.19
248.5
v-1d2f
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
13.78%
ref
22.28%
38.47%
11.62
244.6
v-196a
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
13.68%
-0.10
22.18%
38.37%
9.32
308.2
v-19c2
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
4.52%
-9.26
12.13%
25.46%
356.61
4.4
v-1b9b
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
4.52%
-9.26
12.14%
25.46%
131.25
8.1
v-1b0c
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
4.52%
-9.26
12.14%
25.46%
131.30
8.1
v-1b14
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
12.12%
-1.66
20.40%
35.98%
139.76
8.5
v-1b17
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
4.74%
-9.04
12.82%
26.59%
148.14
8.0
v-1c0f
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
12.19%
-1.59
20.72%
36.75%
68.58
29.2
v-171d
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
12.19%
-1.59
20.72%
36.75%
159.31
31.7
v-19ed
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
11.87%
-1.91
20.08%
35.94%
56.54
28.5
v-1a6f
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
4.60%
-9.18
12.32%
25.35%
66.08
17.0
v-1a6d
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
4.59%
-9.19
12.31%
25.32%
66.81
16.8
v-1a6a
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
11.87%
-1.91
20.08%
35.94%
148.73
31.6
v-1a70
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
4.60%
-9.18
12.31%
25.35%
134.78
30.5
v-1a6e
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
4.59%
-9.19
12.31%
25.32%
133.84
31.7
v-1a6b
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
11.87%
-1.91
20.08%
35.94%
148.93
31.6
v-1874
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
12.19%
-1.59
20.72%
36.75%
69.07
29.1
v-172d
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
12.19%
-1.59
20.72%
36.75%
160.56
31.4
v-19f4
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
7.50%
-6.28
16.05%
30.51%
21.20
83.3
v-15b7
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
7.50%
-6.28
16.05%
30.51%
19.54
144.1
v-1909
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
12.73%
-1.05
20.81%
36.75%
38.71
29.3
v-15f5
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
13.94%
+0.16
22.55%
38.85%
42.41
130.9
v-15bc
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
10.71%
-3.07
19.58%
34.84%
31.09
182.5
v-15be
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
5.21%
-8.57
13.08%
26.97%
11.60
432.0
v-1552
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
12.89%
-0.89
21.38%
37.52%
12.25
324.6
v-1553
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
13.79%
+0.01
22.22%
38.32%
21.40
200.6
v-1554
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
12.66%
-1.12
20.95%
36.87%
11.70
300.5
v-1d6a
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
4.94%
-8.84
12.36%
25.84%
12.93
431.6
v-1d6b
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
13.57%
-0.21
22.07%
38.18%
21.24
200.7
v-1d6c
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
12.83%
-0.95
21.37%
37.84%
14.43
187.4
v-1d6e
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
13.57%
-0.21
22.05%
38.22%
15.51
187.1
v-1d6d
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
15.89%
ref
25.14%
42.14%
20.56
165.7
v-196e
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
15.79%
-0.10
25.02%
41.99%
13.73
214.1
v-19c8
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
4.10%
-11.79
12.22%
24.93%
488.89
3.9
v-1bd5
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
4.10%
-11.79
12.22%
24.93%
235.60
4.4
v-1b33
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
4.10%
-11.79
12.22%
24.93%
235.67
4.4
v-1b37
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
11.36%
-4.53
20.77%
37.19%
244.22
4.5
v-1b40
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
12.14%
-3.75
21.76%
38.93%
131.54
12.0
v-1734
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
12.14%
-3.75
21.76%
38.93%
365.92
12.4
v-19f7
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
11.82%
-4.07
21.28%
38.27%
103.40
11.9
v-1a95
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
4.12%
-11.77
12.49%
25.62%
114.55
9.2
v-1a92
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
4.12%
-11.77
12.49%
25.62%
114.53
9.2
v-1a8e
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
11.82%
-4.07
21.29%
38.27%
340.52
12.5
v-1a97
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
4.12%
-11.77
12.49%
25.61%
329.44
12.2
v-1a94
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
4.12%
-11.77
12.49%
25.62%
327.42
12.5
v-1a90
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
11.82%
-4.07
21.29%
38.27%
341.33
12.5
v-187d
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
12.14%
-3.75
21.76%
38.93%
132.43
12.0
v-1746
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
12.14%
-3.75
21.76%
38.93%
364.79
12.4
v-1a06
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
6.29%
-9.60
15.42%
29.99%
32.24
44.0
v-15d6
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
6.29%
-9.60
15.42%
29.99%
28.96
54.2
v-1917
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
13.23%
-2.66
22.51%
39.62%
66.69
16.2
v-160c
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
16.05%
+0.16
25.47%
42.76%
65.64
69.7
v-192b
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
11.19%
-4.70
20.82%
36.89%
49.64
102.6
v-192d
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
5.87%
-10.02
14.74%
29.57%
12.04
247.6
v-1581
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
13.90%
-1.99
23.61%
40.94%
15.95
259.6
v-1582
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
15.81%
-0.08
25.16%
42.14%
26.41
109.2
v-1583
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
12.60%
-3.29
22.04%
39.48%
15.06
259.7
v-1dc6
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
4.90%
-10.99
13.42%
27.77%
12.45
248.7
v-1dc7
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
15.37%
-0.52
24.85%
41.98%
29.44
108.7
v-1dc8
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
14.89%
-1.00
24.44%
41.70%
26.68
90.9
v-1dca
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
15.40%
-0.49
24.82%
41.96%
26.95
90.0
v-1dc9
Full metrics — 640
Size
Platform
Runtime
Decode
Precision
APs
AP
AP50
AP75
APm
APl
AR@1
AR@10
AR@100
ARs
ARm
ARl
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
10.14%
18.60%
32.57%
18.11%
27.89%
45.53%
8.64%
23.31%
31.12%
21.21%
44.61%
63.16%
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
10.03%
18.53%
32.50%
18.03%
27.86%
45.53%
8.61%
23.20%
31.00%
21.02%
44.63%
62.99%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
3.18%
10.18%
21.10%
8.83%
16.59%
36.45%
5.89%
15.47%
21.27%
11.47%
34.62%
48.75%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
3.18%
10.18%
21.10%
8.83%
16.59%
36.45%
5.89%
15.47%
21.27%
11.47%
34.62%
48.75%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
3.18%
10.18%
21.10%
8.83%
16.59%
36.45%
5.89%
15.47%
21.27%
11.47%
34.62%
48.75%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
8.65%
16.74%
29.91%
16.14%
25.31%
40.37%
7.90%
21.42%
28.62%
19.23%
41.25%
52.89%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
3.48%
10.74%
21.95%
9.35%
17.59%
40.41%
6.19%
16.14%
22.23%
12.10%
36.38%
53.92%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
8.99%
17.28%
31.03%
16.74%
26.40%
43.56%
8.08%
22.17%
29.67%
19.68%
43.49%
59.34%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
8.99%
17.28%
31.03%
16.74%
26.40%
43.56%
8.08%
22.17%
29.67%
19.68%
43.49%
59.34%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
8.70%
16.91%
30.35%
16.40%
25.77%
40.71%
8.07%
21.74%
29.00%
19.63%
42.09%
53.02%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
2.61%
9.46%
19.93%
8.03%
16.09%
35.92%
5.75%
14.75%
20.25%
10.83%
33.08%
46.78%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
3.10%
10.29%
21.30%
8.92%
17.10%
36.77%
6.10%
15.71%
21.57%
11.59%
35.55%
48.85%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
8.70%
16.91%
30.35%
16.40%
25.77%
40.71%
8.07%
21.74%
29.00%
19.63%
42.09%
53.02%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
2.61%
9.46%
19.93%
8.03%
16.09%
35.92%
5.75%
14.75%
20.25%
10.83%
33.08%
46.78%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
3.10%
10.29%
21.30%
8.91%
17.10%
36.77%
6.10%
15.71%
21.57%
11.59%
35.55%
48.85%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
8.70%
16.91%
30.35%
16.40%
25.77%
40.71%
8.07%
21.74%
29.00%
19.63%
42.09%
53.02%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
8.99%
17.28%
31.03%
16.74%
26.40%
43.56%
8.08%
22.17%
29.67%
19.68%
43.49%
59.34%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
8.99%
17.28%
31.03%
16.74%
26.40%
43.56%
8.08%
22.17%
29.67%
19.68%
43.49%
59.34%
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
5.41%
13.58%
25.94%
12.56%
21.83%
38.40%
7.02%
18.42%
25.07%
15.79%
37.46%
49.12%
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
5.40%
13.58%
25.93%
12.56%
21.83%
38.41%
7.02%
18.42%
25.07%
15.79%
37.46%
49.12%
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
9.34%
17.67%
31.78%
17.05%
26.88%
43.82%
8.31%
22.63%
30.22%
20.32%
43.58%
57.91%
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
10.30%
18.70%
32.87%
18.14%
28.02%
45.20%
8.63%
23.68%
31.47%
21.46%
45.17%
64.34%
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
8.10%
16.51%
30.20%
15.83%
25.53%
44.32%
7.97%
21.22%
28.59%
18.93%
41.88%
60.85%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
3.84%
11.44%
22.90%
10.27%
18.82%
42.05%
6.37%
16.77%
22.95%
12.71%
37.43%
55.99%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
9.20%
17.58%
31.49%
16.92%
26.78%
45.19%
8.16%
22.27%
29.72%
20.09%
43.10%
61.16%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
10.13%
18.58%
32.55%
18.09%
27.85%
44.72%
8.61%
23.38%
31.09%
21.32%
44.60%
60.01%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
9.01%
17.19%
30.76%
16.74%
26.04%
44.90%
7.99%
21.91%
29.35%
19.71%
42.41%
59.72%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
3.37%
10.67%
21.68%
9.50%
17.69%
39.36%
6.01%
16.12%
22.23%
12.56%
35.61%
52.03%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
9.87%
18.42%
32.37%
17.82%
27.84%
45.10%
8.61%
23.23%
30.84%
20.92%
44.55%
61.05%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
9.54%
17.97%
32.13%
17.21%
27.19%
44.09%
8.41%
22.62%
30.17%
20.57%
43.02%
58.68%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
9.87%
18.42%
32.43%
17.81%
27.84%
45.06%
8.61%
23.20%
30.81%
20.87%
44.48%
61.30%
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
13.78%
22.28%
38.47%
21.95%
32.13%
45.41%
9.64%
26.73%
35.20%
26.38%
47.46%
57.38%
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
13.68%
22.18%
38.37%
21.90%
32.10%
45.16%
9.62%
26.63%
35.10%
26.29%
47.44%
57.34%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
4.52%
12.13%
25.46%
10.38%
19.90%
38.71%
6.72%
17.71%
24.18%
14.37%
37.69%
50.91%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
4.52%
12.14%
25.46%
10.38%
19.90%
38.72%
6.72%
17.71%
24.18%
14.37%
37.69%
50.91%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
4.52%
12.14%
25.46%
10.38%
19.90%
38.72%
6.72%
17.71%
24.18%
14.37%
37.69%
50.91%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
12.12%
20.40%
35.98%
19.94%
29.58%
42.70%
9.03%
24.94%
33.08%
24.23%
45.29%
55.23%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
4.74%
12.82%
26.59%
11.18%
21.31%
41.50%
7.06%
18.70%
25.64%
15.24%
40.33%
56.04%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
12.19%
20.72%
36.75%
20.29%
30.60%
44.30%
9.31%
25.94%
34.40%
25.14%
47.45%
60.26%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
12.19%
20.72%
36.75%
20.29%
30.60%
44.30%
9.31%
25.94%
34.40%
25.14%
47.45%
60.26%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
11.87%
20.08%
35.94%
19.61%
29.69%
40.79%
9.23%
25.00%
33.28%
24.59%
45.60%
54.83%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
4.60%
12.32%
25.35%
10.93%
20.02%
37.81%
7.01%
18.11%
24.67%
14.92%
38.69%
51.58%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
4.59%
12.31%
25.32%
10.90%
20.01%
37.78%
6.99%
18.10%
24.66%
14.94%
38.64%
51.59%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
11.87%
20.08%
35.94%
19.61%
29.69%
40.79%
9.23%
25.00%
33.28%
24.59%
45.60%
54.83%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
4.60%
12.31%
25.35%
10.93%
20.02%
37.81%
7.01%
18.11%
24.67%
14.92%
38.69%
51.58%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
4.59%
12.31%
25.32%
10.90%
20.01%
37.78%
6.99%
18.10%
24.66%
14.94%
38.64%
51.59%
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
11.87%
20.08%
35.94%
19.61%
29.69%
40.79%
9.23%
25.00%
33.28%
24.59%
45.60%
54.83%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
12.19%
20.72%
36.75%
20.29%
30.60%
44.30%
9.31%
25.94%
34.40%
25.14%
47.45%
60.26%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
12.19%
20.72%
36.75%
20.29%
30.60%
44.30%
9.31%
25.94%
34.40%
25.14%
47.45%
60.26%
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
7.50%
16.05%
30.51%
15.10%
25.21%
41.77%
8.09%
21.36%
29.04%
20.29%
41.04%
53.43%
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
7.50%
16.05%
30.51%
15.10%
25.21%
41.77%
8.09%
21.36%
29.04%
20.29%
41.04%
53.43%
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
12.73%
20.81%
36.75%
20.43%
30.65%
42.78%
9.23%
25.42%
33.67%
24.83%
46.09%
56.71%
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
13.94%
22.55%
38.85%
22.52%
32.64%
46.24%
9.86%
27.04%
35.71%
26.79%
48.59%
58.92%
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
10.71%
19.58%
34.84%
19.28%
29.29%
44.70%
9.04%
24.66%
33.15%
24.11%
45.75%
56.72%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
5.21%
13.08%
26.97%
11.33%
20.96%
39.71%
7.07%
18.62%
25.35%
15.85%
38.89%
52.31%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
12.89%
21.38%
37.52%
21.05%
31.21%
44.31%
9.41%
25.85%
34.28%
25.45%
46.63%
56.10%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
13.79%
22.22%
38.32%
21.89%
32.01%
45.15%
9.63%
26.75%
35.22%
26.56%
47.30%
56.66%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
12.66%
20.95%
36.87%
20.73%
30.67%
43.06%
9.21%
25.52%
34.06%
25.41%
46.15%
54.78%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
4.94%
12.36%
25.84%
10.74%
20.53%
38.78%
6.72%
18.09%
24.80%
15.09%
38.59%
49.95%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
13.57%
22.07%
38.18%
21.64%
31.94%
45.07%
9.56%
26.51%
34.89%
26.15%
47.09%
56.76%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
12.83%
21.37%
37.84%
20.68%
31.36%
44.12%
9.39%
25.73%
33.94%
25.26%
45.62%
55.00%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
13.57%
22.05%
38.22%
21.57%
31.91%
45.02%
9.55%
26.49%
34.86%
26.14%
47.05%
56.81%
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
15.89%
25.14%
42.14%
25.71%
36.96%
46.11%
10.60%
29.12%
37.97%
29.14%
50.93%
59.24%
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
15.79%
25.02%
41.99%
25.60%
36.76%
46.14%
10.60%
28.99%
37.78%
28.92%
50.84%
59.23%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
4.10%
12.22%
24.93%
10.98%
21.13%
39.82%
6.97%
18.06%
24.81%
14.52%
39.79%
51.33%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
4.10%
12.22%
24.93%
10.98%
21.13%
39.82%
6.97%
18.06%
24.81%
14.52%
39.79%
51.33%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
4.10%
12.22%
24.93%
10.98%
21.13%
39.82%
6.97%
18.06%
24.81%
14.52%
39.79%
51.33%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
11.36%
20.77%
37.19%
20.37%
32.05%
44.38%
9.29%
25.41%
33.84%
24.54%
47.15%
55.96%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
12.14%
21.76%
38.93%
21.26%
33.75%
49.14%
9.66%
26.51%
35.08%
25.61%
49.30%
63.79%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
12.14%
21.76%
38.93%
21.26%
33.75%
49.14%
9.66%
26.51%
35.08%
25.61%
49.30%
63.79%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
11.82%
21.28%
38.27%
20.93%
32.92%
44.32%
9.59%
25.70%
34.25%
25.26%
47.27%
55.81%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
4.12%
12.49%
25.62%
11.19%
21.73%
39.55%
7.04%
18.23%
25.00%
14.91%
39.92%
52.48%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
4.12%
12.49%
25.62%
11.17%
21.74%
39.55%
7.04%
18.23%
25.00%
14.92%
39.89%
52.48%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
11.82%
21.29%
38.27%
20.93%
32.93%
44.32%
9.59%
25.70%
34.25%
25.26%
47.27%
55.81%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
4.12%
12.49%
25.61%
11.19%
21.73%
39.55%
7.04%
18.23%
25.00%
14.91%
39.92%
52.48%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
4.12%
12.49%
25.62%
11.17%
21.74%
39.55%
7.04%
18.23%
25.00%
14.92%
39.89%
52.48%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
11.82%
21.29%
38.27%
20.93%
32.92%
44.32%
9.59%
25.70%
34.25%
25.26%
47.27%
55.81%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
12.14%
21.76%
38.93%
21.26%
33.75%
49.14%
9.66%
26.51%
35.08%
25.61%
49.30%
63.79%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
12.14%
21.76%
38.93%
21.26%
33.75%
49.14%
9.66%
26.51%
35.08%
25.61%
49.30%
63.79%
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
6.29%
15.42%
29.99%
14.17%
25.92%
41.18%
7.94%
21.30%
29.05%
19.28%
42.77%
54.83%
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
6.29%
15.42%
29.99%
14.17%
25.92%
41.18%
7.94%
21.30%
29.05%
19.28%
42.77%
54.83%
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
13.23%
22.51%
39.62%
22.11%
34.05%
44.52%
9.65%
26.69%
35.14%
26.20%
48.26%
55.31%
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
16.05%
25.47%
42.76%
25.94%
37.61%
49.36%
10.72%
29.55%
38.53%
29.33%
52.38%
64.90%
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
11.19%
20.82%
36.89%
20.88%
32.50%
46.95%
9.32%
25.81%
33.95%
24.64%
48.18%
58.01%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
5.87%
14.74%
29.57%
13.44%
24.47%
41.23%
7.85%
20.15%
27.12%
16.78%
42.47%
54.69%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
13.90%
23.61%
40.94%
23.47%
35.85%
45.72%
10.15%
27.65%
36.19%
26.91%
50.00%
58.50%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
15.81%
25.16%
42.14%
25.70%
36.99%
45.68%
10.58%
29.06%
37.89%
28.99%
51.03%
59.46%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
12.60%
22.04%
39.48%
21.51%
33.54%
44.81%
9.66%
26.53%
34.91%
25.77%
48.02%
60.55%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
4.90%
13.42%
27.77%
11.82%
23.04%
41.07%
7.22%
19.17%
26.06%
15.83%
41.02%
53.92%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
15.37%
24.85%
41.98%
25.24%
36.97%
45.61%
10.58%
28.85%
37.50%
28.42%
51.08%
59.30%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
14.89%
24.44%
41.70%
24.63%
36.39%
45.15%
10.36%
28.12%
36.76%
27.89%
49.29%
57.71%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
15.40%
24.82%
41.96%
25.26%
36.90%
45.69%
10.56%
28.83%
37.50%
28.43%
51.04%
59.40%
1280 — 1280×1280, full-frame weights
Size
Platform
Runtime
Decode
APs
Δ APs vs FP32 (pp)
AP
AP50
Time per image (ms)
Realized FPS
Source
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
20.02%
ref
29.41%
48.26%
22.94
136.6
v-1967
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
19.77%
-0.25
29.18%
48.03%
15.76
192.2
v-19be
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
5.64%
-14.38
14.06%
28.19%
1157.15
1.1
v-1b77
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
5.64%
-14.38
14.06%
28.19%
290.24
3.6
v-1a81
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
5.64%
-14.38
14.06%
28.19%
289.64
3.6
v-1a84
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
18.35%
-1.67
27.22%
45.40%
300.10
4.1
v-1a88
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
5.79%
-14.23
14.47%
28.85%
331.43
3.6
v-1bfa
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
18.54%
-1.48
27.45%
45.80%
133.21
16.8
v-1704
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
18.54%
-1.48
27.45%
45.80%
278.69
19.1
v-19d9
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
18.56%
-1.46
27.51%
46.02%
106.92
16.6
v-1a0c
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
5.78%
-14.24
14.36%
28.77%
165.30
6.7
v-1a08
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
5.78%
-14.24
14.36%
28.77%
166.46
6.7
v-1a04
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
18.56%
-1.46
27.52%
46.02%
260.01
18.8
v-1a0d
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
5.78%
-14.24
14.36%
28.77%
241.56
17.7
v-1a0b
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
5.78%
-14.24
14.36%
28.77%
243.54
17.6
v-1a07
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
18.56%
-1.46
27.52%
46.02%
260.43
18.9
v-1865
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
18.54%
-1.48
27.45%
45.80%
133.05
16.5
v-16f8
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
18.54%
-1.48
27.45%
45.80%
285.73
18.8
v-19eb
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
9.72%
-10.30 ⚠
19.33%
34.71%
50.71
36.5
v-161a
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
9.72%
-10.30 ⚠
19.33%
34.71%
40.28
70.6
v-1902
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
19.21%
-0.81
28.63%
47.37%
45.30
31.6
v-15dc
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
19.83%
-0.19
29.26%
48.17%
75.01
77.5
v-15b2
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
18.11%
-1.91
27.21%
46.06%
66.78
103.5
v-15b3
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
6.40%
-13.62
16.33%
31.09%
18.79
198.6
v-153a
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
19.58%
-0.44
28.89%
47.82%
24.70
129.3
v-153b
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
19.96%
-0.06
29.32%
48.21%
54.46
65.4
v-153e
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
18.83%
-1.19
28.03%
46.68%
24.54
128.8
v-1d31
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
5.48%
-14.54
14.90%
29.25%
16.31
202.6
v-1d32
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
19.88%
-0.14
29.29%
48.19%
55.00
65.2
v-1d33
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
19.51%
-0.51
29.06%
47.98%
43.07
59.3
v-1d35
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
19.91%
-0.11
29.31%
48.24%
42.11
59.5
v-1d34
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
24.05%
ref
32.77%
53.01%
35.27
105.0
v-196b
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
23.75%
-0.30
32.51%
52.80%
23.11
148.4
v-19c4
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
6.04%
-18.01
13.70%
29.55%
1438.82
1.1
v-1ba4
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
6.04%
-18.01
13.70%
29.55%
523.13
2.0
v-1b1b
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
6.04%
-18.01
13.70%
29.55%
522.13
2.0
v-1b21
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
19.87%
-4.18
28.64%
48.38%
529.62
2.1
v-1b29
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
6.18%
-17.87
13.92%
30.06%
568.80
1.9
v-1c17
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
20.18%
-3.87
28.71%
48.49%
251.50
6.6
v-171f
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
20.18%
-3.87
28.71%
48.49%
677.25
6.9
v-19ee
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
20.17%
-3.88
28.64%
48.39%
194.44
6.6
v-1a76
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
6.11%
-17.94
13.50%
29.37%
251.91
4.2
v-1a74
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
6.11%
-17.94
13.50%
29.37%
256.22
4.2
v-1a72
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
20.17%
-3.88
28.64%
48.39%
618.73
6.9
v-1a77
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
6.11%
-17.94
13.50%
29.38%
597.36
6.8
v-1a75
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
6.11%
-17.94
13.50%
29.37%
600.24
6.8
v-1a73
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
20.17%
-3.88
28.64%
48.39%
619.15
6.9
v-1875
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
20.17%
-3.88
28.70%
48.49%
254.00
6.6
v-1730
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
20.17%
-3.88
28.70%
48.49%
661.10
6.9
v-19f5
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
10.90%
-13.15 ⚠
18.76%
35.33%
74.28
19.6
v-15b9
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
10.90%
-13.15 ⚠
18.76%
35.33%
61.68
25.3
v-190b
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
20.36%
-3.69
29.41%
49.77%
82.63
15.0
v-15fa
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
23.91%
-0.14
32.70%
52.99%
105.93
45.2
v-15c0
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
19.52%
-4.53
28.62%
48.19%
71.01
76.5
v-15c2
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
6.83%
-17.22
16.66%
32.58%
24.61
118.2
v-1556
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
22.43%
-1.62
31.53%
51.87%
33.15
136.0
v-1558
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
24.16%
+0.11
32.78%
53.00%
70.25
47.6
v-155a
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
20.75%
-3.30
29.47%
49.53%
32.20
134.7
v-1d6f
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
6.26%
-17.79
14.37%
30.62%
22.58
118.5
v-1d70
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
24.03%
-0.02
32.72%
52.94%
71.17
46.7
v-1d71
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
23.58%
-0.47
32.43%
52.73%
54.10
43.2
v-1d73
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
23.45%
-0.60
32.33%
52.77%
54.81
42.7
v-1d72
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
26.69%
ref
35.88%
56.96%
83.58
53.6
v-1970
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
26.38%
-0.31
35.60%
56.84%
55.49
86.7
v-19c9
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
5.20%
-21.49
10.24%
23.26%
2004.13
1.0
v-1bef
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
5.25%
-21.44
10.24%
23.33%
973.73
1.0
v-1b43
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
5.20%
-21.49
10.24%
23.25%
971.71
1.0
v-1b4f
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
14.37%
-12.32 ⚠
23.45%
42.67%
980.86
1.1
v-1b62
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
14.47%
-12.22 ⚠
23.88%
43.07%
448.49
2.9
v-1738
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
14.52%
-12.17 ⚠
23.87%
43.09%
1453.99
3.0
v-19fa
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
14.80%
-11.89 ⚠
23.64%
43.08%
382.81
2.9
v-1aa1
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
5.40%
-21.29
10.70%
24.01%
448.90
2.3
v-1a9b
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
5.45%
-21.24
10.73%
24.08%
445.83
2.3
v-1a98
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
14.83%
-11.86 ⚠
23.73%
43.14%
1367.38
3.0
v-1aa3
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
5.39%
-21.30
10.69%
24.04%
1349.23
3.0
v-1a9f
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
5.43%
-21.26
10.77%
24.16%
1345.29
3.0
v-1a9a
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
14.76%
-11.93 ⚠
23.70%
43.14%
1368.00
3.0
v-187f
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
14.57%
-12.12 ⚠
23.87%
43.19%
446.33
2.9
v-1777
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
14.56%
-12.13 ⚠
23.90%
43.15%
1433.30
3.0
v-1a09
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
9.06%
-17.63 ⚠
14.31%
29.11%
132.93
9.2
v-15e1
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
9.11%
-17.58 ⚠
14.34%
29.11%
121.74
10.2
v-1920
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
26.41%
-0.28
35.64%
56.92%
202.80
21.4
v-192f
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
11.59%
-15.10 ⚠
19.81%
36.20%
134.61
35.4
v-1933
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
8.22%
-18.47
18.16%
35.18%
36.27
66.0
v-15bd
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
25.33%
-1.36
34.52%
55.94%
53.66
62.3
v-15bf
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
26.76%
+0.07
35.89%
56.97%
113.09
24.5
v-15c3
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
19.05%
-7.64
29.16%
49.28%
54.01
63.4
v-1dad
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
5.78%
-20.91
13.85%
28.68%
37.57
65.7
v-1dae
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
26.72%
+0.03
35.86%
56.89%
113.69
24.3
v-1daf
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
26.14%
-0.55
35.52%
56.73%
92.09
23.6
v-1db1
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
26.69%
+0.00
35.80%
56.91%
91.18
23.7
v-1db0
Sizes
Platform
Runtime
Decode
Status
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
Not supported: Hailo-8L: insufficient on-chip memory for this model and input size
Full metrics — 1280
Size
Platform
Runtime
Decode
Precision
APs
AP
AP50
AP75
APm
APl
AR@1
AR@10
AR@100
ARs
ARm
ARl
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
20.02%
29.41%
48.26%
29.97%
40.91%
49.15%
11.54%
33.66%
44.38%
35.81%
56.60%
64.29%
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
19.77%
29.18%
48.03%
29.80%
40.76%
49.15%
11.47%
33.41%
44.07%
35.39%
56.48%
64.34%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
5.64%
14.06%
28.19%
12.49%
23.31%
39.10%
7.84%
20.67%
28.71%
18.80%
43.17%
52.14%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
5.64%
14.06%
28.19%
12.49%
23.31%
39.10%
7.84%
20.67%
28.71%
18.80%
43.17%
52.14%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
5.64%
14.06%
28.19%
12.49%
23.31%
39.10%
7.84%
20.67%
28.71%
18.80%
43.17%
52.14%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
18.35%
27.22%
45.40%
27.56%
37.91%
43.79%
11.14%
32.01%
42.25%
33.88%
53.86%
60.18%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
5.79%
14.47%
28.85%
12.67%
24.20%
43.16%
7.71%
20.94%
29.24%
19.26%
44.04%
57.98%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
18.54%
27.45%
45.80%
27.81%
38.40%
45.42%
11.02%
31.91%
42.19%
33.78%
53.94%
61.37%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
18.54%
27.45%
45.80%
27.81%
38.40%
45.42%
11.02%
31.91%
42.19%
33.78%
53.94%
61.37%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
18.56%
27.51%
46.02%
27.90%
38.46%
47.10%
11.12%
31.97%
42.25%
34.04%
53.77%
60.56%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
5.78%
14.36%
28.77%
12.51%
23.82%
44.12%
7.90%
20.89%
29.09%
19.27%
43.67%
55.96%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
5.78%
14.36%
28.77%
12.51%
23.82%
44.12%
7.90%
20.89%
29.09%
19.27%
43.67%
55.96%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
18.56%
27.52%
46.02%
27.90%
38.47%
47.10%
11.12%
31.97%
42.25%
34.04%
53.77%
60.56%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
5.78%
14.36%
28.77%
12.51%
23.82%
44.12%
7.90%
20.89%
29.09%
19.27%
43.67%
55.96%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
5.78%
14.36%
28.77%
12.50%
23.82%
44.12%
7.90%
20.89%
29.09%
19.27%
43.67%
55.96%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
18.56%
27.52%
46.02%
27.90%
38.47%
47.10%
11.12%
31.97%
42.25%
34.04%
53.77%
60.56%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
18.54%
27.45%
45.80%
27.81%
38.40%
45.42%
11.02%
31.91%
42.19%
33.78%
53.94%
61.37%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
18.54%
27.45%
45.80%
27.81%
38.40%
45.42%
11.02%
31.91%
42.19%
33.78%
53.94%
61.37%
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
9.72%
19.33%
34.71%
19.53%
30.57%
42.75%
8.92%
24.72%
34.48%
25.94%
46.14%
54.73%
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
9.72%
19.33%
34.71%
19.53%
30.56%
43.53%
8.92%
24.72%
34.48%
25.94%
46.14%
54.73%
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
19.21%
28.63%
47.37%
29.01%
40.30%
46.43%
11.33%
32.67%
43.15%
34.18%
55.76%
61.39%
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
19.83%
29.26%
48.17%
29.85%
40.89%
49.08%
11.51%
33.51%
44.18%
35.38%
56.68%
64.09%
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
18.11%
27.21%
46.06%
27.44%
38.20%
46.76%
10.89%
32.03%
42.62%
33.72%
55.34%
63.63%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
6.40%
16.33%
31.09%
15.48%
27.47%
44.83%
8.41%
22.46%
31.27%
20.87%
46.75%
56.85%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
19.58%
28.89%
47.82%
29.23%
40.32%
48.23%
11.42%
33.24%
43.85%
35.09%
56.38%
62.74%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
19.96%
29.32%
48.21%
29.88%
40.74%
49.32%
11.45%
33.57%
44.31%
35.73%
56.54%
63.06%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
18.83%
28.03%
46.68%
28.68%
39.07%
46.60%
11.23%
32.59%
43.15%
34.47%
55.23%
61.98%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
5.48%
14.90%
29.25%
13.45%
25.77%
41.80%
7.97%
21.33%
29.57%
19.40%
44.44%
56.35%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
19.88%
29.29%
48.19%
29.75%
40.82%
49.06%
11.41%
33.55%
44.27%
35.56%
56.70%
63.46%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
19.51%
29.06%
47.98%
29.59%
40.72%
49.39%
11.36%
33.10%
43.72%
35.10%
55.82%
62.72%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
19.91%
29.31%
48.24%
29.75%
40.74%
48.94%
11.41%
33.56%
44.28%
35.58%
56.66%
63.25%
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
24.05%
32.77%
53.01%
33.97%
44.22%
51.08%
12.62%
36.31%
47.64%
39.91%
58.82%
66.68%
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
23.75%
32.51%
52.80%
33.59%
44.05%
50.85%
12.58%
36.07%
47.35%
39.56%
58.58%
66.14%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
6.04%
13.70%
29.55%
10.75%
24.40%
46.48%
7.73%
21.40%
30.48%
20.67%
45.08%
55.31%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
6.04%
13.70%
29.55%
10.75%
24.40%
46.49%
7.73%
21.40%
30.48%
20.67%
45.08%
55.31%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
6.04%
13.70%
29.55%
10.75%
24.40%
46.49%
7.73%
21.40%
30.48%
20.67%
45.08%
55.31%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
19.87%
28.64%
48.38%
29.18%
39.77%
51.38%
11.52%
33.65%
45.11%
37.13%
56.22%
63.03%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
6.18%
13.92%
30.06%
10.71%
24.82%
43.97%
7.73%
21.51%
30.53%
20.73%
45.30%
55.94%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
20.18%
28.71%
48.49%
29.27%
39.93%
46.68%
11.56%
33.92%
45.50%
37.67%
56.72%
63.13%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
20.18%
28.71%
48.49%
29.27%
39.93%
46.68%
11.56%
33.92%
45.50%
37.67%
56.72%
63.13%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
20.17%
28.64%
48.39%
29.18%
39.94%
45.40%
11.35%
33.71%
45.34%
37.46%
56.56%
62.24%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
6.11%
13.50%
29.37%
10.40%
24.31%
39.18%
7.51%
21.21%
30.28%
20.71%
44.95%
51.74%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
6.11%
13.50%
29.37%
10.40%
24.31%
39.18%
7.51%
21.21%
30.28%
20.71%
44.95%
51.74%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
20.17%
28.64%
48.39%
29.18%
39.93%
45.40%
11.35%
33.71%
45.34%
37.46%
56.56%
62.24%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
6.11%
13.50%
29.38%
10.40%
24.31%
39.18%
7.51%
21.21%
30.28%
20.71%
44.95%
51.74%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
6.11%
13.50%
29.37%
10.40%
24.31%
39.18%
7.51%
21.21%
30.28%
20.71%
44.95%
51.74%
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
20.17%
28.64%
48.39%
29.19%
39.94%
45.40%
11.35%
33.71%
45.34%
37.46%
56.56%
62.24%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
20.17%
28.70%
48.49%
29.27%
39.93%
46.68%
11.56%
33.92%
45.50%
37.67%
56.72%
63.13%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
20.17%
28.70%
48.49%
29.27%
39.93%
46.68%
11.56%
33.92%
45.50%
37.67%
56.72%
63.13%
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
10.90%
18.76%
35.33%
18.09%
30.55%
46.21%
8.87%
26.12%
37.62%
29.62%
49.34%
57.49%
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
10.90%
18.76%
35.33%
18.09%
30.54%
46.21%
8.87%
26.12%
37.62%
29.62%
49.34%
57.49%
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
20.36%
29.41%
49.77%
29.60%
40.58%
48.34%
11.69%
33.89%
44.95%
37.09%
55.93%
64.24%
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
23.91%
32.70%
52.99%
33.86%
44.17%
51.11%
12.63%
36.18%
47.58%
39.90%
58.67%
66.87%
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
19.52%
28.62%
48.19%
29.00%
40.51%
45.70%
11.53%
32.95%
43.71%
35.80%
55.03%
62.42%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
6.83%
16.66%
32.58%
15.20%
27.90%
45.81%
8.86%
23.36%
32.25%
22.38%
47.27%
58.84%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
22.43%
31.53%
51.87%
32.46%
43.01%
51.13%
12.21%
35.42%
46.70%
38.87%
57.98%
67.87%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
24.16%
32.78%
53.00%
33.90%
44.19%
51.22%
12.61%
36.47%
47.76%
40.06%
58.88%
66.82%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
20.75%
29.47%
49.53%
30.00%
40.76%
49.24%
11.69%
34.10%
45.71%
37.73%
57.20%
65.62%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
6.26%
14.37%
30.62%
11.52%
25.52%
44.66%
7.86%
21.59%
30.67%
20.66%
45.74%
56.25%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
24.03%
32.72%
52.94%
33.73%
44.15%
51.06%
12.63%
36.33%
47.66%
39.87%
58.85%
66.69%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
23.58%
32.43%
52.73%
33.20%
44.03%
51.56%
12.50%
35.95%
47.09%
39.29%
58.01%
66.64%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
23.45%
32.33%
52.77%
33.05%
43.96%
51.30%
12.53%
35.80%
47.00%
39.14%
58.05%
65.19%
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
26.69%
35.88%
56.96%
37.44%
47.77%
57.21%
13.53%
38.79%
50.66%
42.74%
62.02%
68.55%
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
26.38%
35.60%
56.84%
37.14%
47.53%
57.39%
13.46%
38.52%
50.31%
42.38%
61.71%
68.83%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
5.20%
10.24%
23.26%
7.61%
18.35%
39.44%
6.82%
19.67%
29.37%
20.60%
42.92%
51.46%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
5.25%
10.24%
23.33%
7.52%
18.31%
39.44%
6.82%
19.67%
29.37%
20.60%
42.92%
51.46%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
5.20%
10.24%
23.25%
7.66%
18.36%
39.44%
6.82%
19.67%
29.37%
20.60%
42.92%
51.46%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
14.37%
23.45%
42.67%
22.97%
36.20%
48.75%
10.26%
30.03%
41.47%
33.35%
53.29%
62.14%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
14.47%
23.88%
43.07%
23.62%
36.90%
54.33%
10.51%
30.65%
42.08%
33.83%
54.15%
65.22%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
14.52%
23.87%
43.09%
23.60%
36.78%
54.26%
10.45%
30.61%
42.05%
33.84%
54.07%
65.19%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
14.80%
23.64%
43.08%
23.58%
35.92%
53.37%
10.44%
30.39%
41.91%
33.88%
53.55%
63.67%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
5.40%
10.70%
24.01%
8.20%
18.91%
43.27%
6.91%
20.03%
29.83%
20.89%
43.74%
56.14%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
5.45%
10.73%
24.08%
8.07%
18.91%
43.34%
6.99%
20.05%
29.84%
20.90%
43.74%
56.03%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
14.83%
23.73%
43.14%
23.73%
35.97%
53.45%
10.44%
30.44%
41.93%
33.86%
53.64%
63.75%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
5.39%
10.69%
24.04%
8.17%
18.87%
43.21%
6.90%
20.01%
29.82%
20.89%
43.74%
56.04%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
5.43%
10.77%
24.16%
8.05%
18.92%
43.49%
6.92%
19.99%
29.81%
20.80%
43.75%
56.04%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
14.76%
23.70%
43.14%
23.64%
36.00%
53.34%
10.43%
30.41%
41.93%
33.85%
53.65%
63.65%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
14.57%
23.87%
43.19%
23.56%
36.72%
54.17%
10.45%
30.64%
42.12%
33.93%
54.14%
65.11%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
14.56%
23.90%
43.15%
23.79%
36.84%
54.24%
10.48%
30.61%
42.03%
33.78%
54.13%
65.20%
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
9.06%
14.31%
29.11%
12.47%
25.59%
39.91%
8.03%
23.79%
35.33%
29.05%
46.09%
50.08%
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
9.11%
14.34%
29.11%
12.52%
25.56%
39.91%
8.03%
23.79%
35.33%
29.05%
46.09%
50.08%
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
26.41%
35.64%
56.92%
37.06%
47.49%
57.55%
13.45%
38.53%
50.32%
42.42%
61.63%
68.06%
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
11.59%
19.81%
36.20%
19.46%
32.26%
38.39%
8.74%
25.74%
35.68%
26.96%
49.72%
49.01%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
8.22%
18.16%
35.18%
16.64%
29.42%
50.31%
9.51%
25.17%
34.61%
24.51%
49.76%
63.25%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
25.33%
34.52%
55.94%
35.70%
46.16%
56.59%
13.13%
37.85%
49.61%
41.80%
60.81%
67.19%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
26.76%
35.89%
56.97%
37.41%
47.54%
56.44%
13.48%
38.83%
50.69%
42.92%
61.79%
67.34%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
19.05%
29.16%
49.28%
29.71%
42.12%
52.16%
11.74%
34.12%
45.67%
37.34%
57.78%
64.00%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
5.78%
13.85%
28.68%
11.37%
23.95%
47.94%
7.93%
22.26%
31.77%
22.04%
46.32%
62.08%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
26.72%
35.86%
56.89%
37.39%
47.60%
56.48%
13.54%
38.77%
50.62%
42.75%
61.86%
67.28%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
26.14%
35.52%
56.73%
36.78%
47.51%
56.58%
13.41%
38.45%
50.14%
42.20%
61.23%
67.30%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
26.69%
35.80%
56.91%
37.37%
47.54%
56.72%
13.52%
38.74%
50.59%
42.73%
61.88%
67.64%
1344x768 — 1344×768, tiled weights
Size
Platform
Runtime
Decode
APs
Δ APs vs FP32 (pp)
AP
AP50
Time per image (ms)
Realized FPS
Source
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
21.60%
ref
30.29%
49.23%
17.39
187.3
v-1969
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
21.37%
-0.23
30.08%
49.14%
12.78
253.7
v-19c0
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
6.88%
-14.72
16.17%
30.87%
745.26
1.8
v-1b91
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
6.88%
-14.72
16.17%
30.87%
193.39
5.5
v-1afd
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
6.88%
-14.72
16.17%
30.87%
193.36
5.6
v-1b02
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
17.74%
-3.86
26.38%
45.20%
202.37
6.2
v-1b07
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
6.93%
-14.67
16.48%
31.36%
232.03
5.5
v-1c09
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
18.04%
-3.56
26.65%
45.78%
101.31
23.6
v-171b
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
18.04%
-3.56
26.65%
45.78%
185.06
28.7
v-19ec
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
17.82%
-3.78
26.55%
45.71%
75.54
24.4
v-1a68
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
6.85%
-14.75
16.29%
31.04%
110.33
10.3
v-1a66
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
6.87%
-14.73
16.34%
31.11%
112.76
10.0
v-1a63
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
17.82%
-3.78
26.55%
45.71%
171.61
29.0
v-1a69
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
6.85%
-14.75
16.29%
31.04%
159.50
27.2
v-1a67
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
6.87%
-14.73
16.34%
31.11%
151.53
29.1
v-1a64
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
17.82%
-3.78
26.55%
45.71%
171.67
29.0
v-1872
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
18.04%
-3.56
26.65%
45.78%
107.55
22.5
v-172b
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
18.04%
-3.56
26.65%
45.78%
169.37
28.0
v-19f3
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
11.15%
-10.45 ⚠
21.11%
37.26%
33.89
54.7
v-15d2
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
11.14%
-10.46 ⚠
21.10%
37.25%
29.70
124.6
v-1907
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
18.83%
-2.77
27.27%
46.75%
37.06
37.5
v-15f1
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
21.48%
-0.12
30.12%
49.18%
68.12
105.7
v-190d
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
18.52%
-3.08
27.29%
46.02%
62.32
116.9
v-190f
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
8.96%
-12.64
18.10%
34.31%
12.54
301.1
v-154f
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
20.47%
-1.13
29.04%
48.07%
19.64
156.7
v-1550
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
21.53%
-0.07
30.14%
49.20%
33.70
117.7
v-1551
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
18.94%
-2.66
27.15%
46.26%
19.55
156.2
v-1d5b
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
7.44%
-14.16
16.67%
31.61%
14.35
302.6
v-1d5c
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
21.38%
-0.22
30.01%
48.97%
34.86
117.7
v-1d5d
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
21.16%
-0.44
29.90%
49.05%
27.33
104.2
v-1d5f
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
21.36%
-0.24
29.98%
48.98%
29.81
103.2
v-1d5e
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
25.91%
ref
34.50%
55.59%
23.64
142.9
v-196d
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
25.75%
-0.16
34.32%
55.43%
15.42
204.0
v-19c7
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
8.63%
-17.28
18.30%
35.44%
934.43
1.7
v-1bc8
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
8.64%
-17.27
18.30%
35.44%
341.99
3.0
v-1b24
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
8.64%
-17.27
18.30%
35.44%
341.85
3.0
v-1b28
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
22.40%
-3.51
31.03%
51.73%
352.00
3.2
v-1b30
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
22.80%
-3.11
31.49%
52.43%
164.25
10.4
v-172e
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
22.80%
-3.11
31.49%
52.43%
431.33
11.0
v-19f1
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
22.51%
-3.40
31.11%
52.22%
131.72
10.4
v-1a8b
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
8.74%
-17.17
18.12%
35.59%
166.52
6.5
v-1a89
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
8.74%
-17.17
18.12%
35.59%
169.34
6.3
v-1a86
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
22.51%
-3.40
31.11%
52.22%
394.00
11.2
v-1a8d
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
8.74%
-17.17
18.12%
35.58%
375.04
10.8
v-1a8a
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
8.74%
-17.17
18.12%
35.58%
369.94
11.2
v-1a87
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
22.51%
-3.40
31.11%
52.22%
395.28
11.1
v-187a
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
22.80%
-3.11
31.49%
52.43%
171.19
10.5
v-1740
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
22.80%
-3.11
31.49%
52.43%
438.11
11.1
v-19f8
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
14.20%
-11.71 ⚠
23.75%
42.26%
53.36
29.2
v-15c5
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
14.22%
-11.69 ⚠
23.75%
42.26%
46.32
37.7
v-1913
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
23.67%
-2.24
31.64%
53.51%
64.18
18.8
v-1607
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
25.70%
-0.21
34.29%
55.43%
79.88
65.0
v-15c8
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
21.22%
-4.69
30.46%
50.81%
60.29
104.4
v-15ca
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
9.77%
-16.14
20.37%
37.95%
13.91
216.2
v-157d
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
24.22%
-1.69
33.03%
54.22%
22.08
179.9
v-157f
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
25.87%
-0.04
34.48%
55.56%
42.03
81.9
v-1580
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
23.33%
-2.58
31.73%
52.79%
22.22
156.4
v-1d99
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
8.89%
-17.02
17.54%
34.61%
16.92
217.4
v-1d9a
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
25.82%
-0.09
34.45%
55.51%
41.71
80.9
v-1d9b
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
25.42%
-0.49
34.13%
55.43%
34.93
73.1
v-1d9d
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
25.53%
-0.38
34.23%
55.41%
35.32
72.8
v-1d9c
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
28.46%
ref
36.89%
58.87%
58.28
80.7
v-1972
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
28.29%
-0.17
36.75%
58.68%
39.12
125.9
v-19cb
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
5.67%
-22.79
11.83%
25.11%
1288.98
1.5
v-1c25
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
5.66%
-22.80
11.82%
25.09%
618.11
1.7
v-1b94
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
5.66%
-22.80
11.82%
25.07%
618.03
1.7
v-1ba0
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
15.58%
-12.88 ⚠
24.55%
44.10%
624.35
1.7
v-1ba5
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
15.63%
-12.83 ⚠
25.41%
44.75%
313.02
4.6
v-175a
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
15.59%
-12.87 ⚠
25.37%
44.73%
947.97
4.7
v-1a0a
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
15.33%
-13.13 ⚠
25.07%
44.33%
254.92
4.6
v-1ad9
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
5.77%
-22.69
11.92%
25.26%
296.18
3.6
v-1acf
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
5.78%
-22.68
11.94%
25.29%
300.10
3.5
v-1acb
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
15.31%
-13.15 ⚠
25.07%
44.32%
876.82
4.8
v-1add
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
5.74%
-22.72
11.92%
25.25%
856.46
4.7
v-1ad7
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
5.76%
-22.70
11.92%
25.30%
859.19
4.8
v-1acd
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
15.31%
-13.15 ⚠
25.07%
44.32%
877.38
4.8
v-1889
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
15.59%
-12.87 ⚠
25.38%
44.74%
316.21
4.6
v-1764
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
15.59%
-12.87 ⚠
25.38%
44.74%
940.25
4.7
v-1a14
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
9.14%
-19.32 ⚠
16.51%
31.20%
100.86
13.5
v-15fc
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
8.92%
-19.54 ⚠
16.40%
31.08%
92.70
15.1
v-1925
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
21.12%
-7.34
30.99%
52.35%
118.24
9.3
v-1625
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
28.31%
-0.15
36.72%
58.74%
142.59
32.3
v-1939
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
14.90%
-13.56 ⚠
24.99%
44.56%
95.98
52.4
v-193b
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
9.89%
-18.57
20.41%
38.37%
23.55
106.3
v-162b
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
23.07%
-5.39
32.38%
54.25%
32.27
113.4
v-1630
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
28.35%
-0.11
36.80%
58.70%
71.92
39.7
v-1632
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
17.64%
-10.82 ⚠
26.41%
46.49%
33.35
116.1
v-1dda
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
6.94%
-21.52
13.00%
27.42%
26.99
108.3
v-1ddb
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
28.38%
-0.08
36.82%
58.80%
71.78
39.7
v-1ddc
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
28.04%
-0.42
36.60%
58.50%
60.89
38.0
v-1dde
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
28.05%
-0.41
36.59%
58.60%
62.15
37.5
v-1ddd
Full metrics — 1344x768
Size
Platform
Runtime
Decode
Precision
APs
AP
AP50
AP75
APm
APl
AR@1
AR@10
AR@100
ARs
ARm
ARl
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
21.60%
30.29%
49.23%
30.87%
41.61%
53.41%
12.00%
34.80%
46.09%
37.20%
59.07%
71.32%
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
21.37%
30.08%
49.14%
30.60%
41.41%
53.46%
11.92%
34.54%
45.78%
36.88%
58.80%
71.40%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
6.88%
16.17%
30.87%
15.12%
26.29%
46.88%
8.62%
22.88%
31.65%
21.69%
46.28%
63.41%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
6.88%
16.17%
30.87%
15.12%
26.29%
46.88%
8.62%
22.88%
31.65%
21.69%
46.28%
63.41%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
6.88%
16.17%
30.87%
15.12%
26.29%
46.88%
8.62%
22.88%
31.65%
21.69%
46.28%
63.41%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
17.74%
26.38%
45.20%
26.57%
36.73%
51.11%
10.89%
31.18%
41.81%
33.21%
53.86%
67.85%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
6.93%
16.48%
31.36%
15.40%
27.03%
47.97%
8.75%
23.28%
32.24%
22.06%
47.37%
66.29%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
18.04%
26.65%
45.78%
26.83%
37.39%
51.65%
11.05%
31.55%
42.24%
33.43%
55.08%
68.12%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
18.04%
26.65%
45.78%
26.83%
37.39%
51.65%
11.05%
31.55%
42.24%
33.43%
55.08%
68.12%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
17.82%
26.55%
45.71%
26.95%
37.26%
50.99%
11.19%
31.47%
42.19%
33.43%
54.79%
70.35%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
6.85%
16.29%
31.04%
15.35%
26.95%
47.57%
8.71%
23.17%
31.95%
21.67%
47.24%
66.70%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
6.87%
16.34%
31.11%
15.38%
27.00%
47.87%
8.73%
23.20%
31.99%
21.68%
47.31%
67.09%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
17.82%
26.55%
45.71%
26.95%
37.26%
50.99%
11.19%
31.47%
42.19%
33.43%
54.79%
70.35%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
6.85%
16.29%
31.04%
15.35%
26.95%
47.57%
8.71%
23.17%
31.95%
21.67%
47.24%
66.70%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
6.87%
16.34%
31.11%
15.38%
27.00%
47.87%
8.73%
23.20%
31.99%
21.68%
47.31%
67.09%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
17.82%
26.55%
45.71%
26.95%
37.26%
50.99%
11.19%
31.47%
42.19%
33.43%
54.79%
70.35%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
18.04%
26.65%
45.78%
26.83%
37.39%
51.65%
11.05%
31.55%
42.24%
33.43%
55.08%
68.12%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
18.04%
26.65%
45.78%
26.83%
37.39%
51.65%
11.05%
31.55%
42.24%
33.43%
55.08%
68.12%
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
11.15%
21.11%
37.26%
21.67%
33.24%
47.35%
9.88%
27.02%
37.36%
28.50%
50.15%
64.09%
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
11.14%
21.10%
37.25%
21.67%
33.24%
47.36%
9.88%
27.02%
37.36%
28.50%
50.15%
64.09%
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
18.83%
27.27%
46.75%
27.74%
37.99%
51.63%
11.24%
32.00%
42.84%
34.26%
55.45%
65.31%
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
21.48%
30.12%
49.18%
30.75%
41.38%
53.45%
11.97%
34.51%
45.80%
37.02%
58.79%
70.89%
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
18.52%
27.29%
46.02%
27.73%
38.39%
50.69%
11.15%
32.20%
43.02%
34.20%
55.81%
66.94%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
8.96%
18.10%
34.31%
16.85%
28.88%
46.25%
9.04%
24.69%
33.95%
24.11%
48.98%
62.07%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
20.47%
29.04%
48.07%
29.71%
40.41%
49.31%
11.73%
33.39%
44.42%
35.76%
57.26%
64.94%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
21.53%
30.14%
49.20%
30.73%
41.38%
52.89%
12.07%
34.63%
45.89%
37.11%
58.73%
67.86%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
18.94%
27.15%
46.26%
27.52%
37.72%
49.90%
11.27%
31.76%
42.55%
34.15%
54.71%
65.13%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
7.44%
16.67%
31.61%
15.96%
27.16%
47.87%
8.80%
23.37%
32.34%
22.49%
47.39%
60.91%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
21.38%
30.01%
48.97%
30.77%
41.35%
52.51%
12.04%
34.46%
45.60%
36.82%
58.55%
67.38%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
21.16%
29.90%
49.05%
30.41%
41.29%
52.72%
11.98%
34.29%
45.43%
36.65%
58.02%
67.42%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
21.36%
29.98%
48.98%
30.71%
41.24%
52.53%
12.03%
34.42%
45.58%
36.81%
58.56%
67.39%
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
25.91%
34.50%
55.59%
35.61%
46.20%
53.02%
13.27%
38.23%
49.93%
42.02%
62.01%
72.51%
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
25.75%
34.32%
55.43%
35.42%
46.03%
53.13%
13.22%
38.03%
49.70%
41.77%
61.83%
72.45%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
8.63%
18.30%
35.44%
17.00%
30.54%
48.47%
9.33%
25.39%
34.93%
25.29%
50.12%
66.01%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
8.64%
18.30%
35.44%
17.00%
30.54%
48.47%
9.33%
25.39%
34.93%
25.29%
50.12%
66.01%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
8.64%
18.30%
35.44%
17.00%
30.54%
48.47%
9.33%
25.39%
34.93%
25.29%
50.12%
66.01%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
22.40%
31.03%
51.73%
32.05%
42.70%
49.27%
12.33%
35.50%
46.78%
38.81%
59.03%
68.28%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
22.80%
31.49%
52.43%
32.36%
43.27%
51.21%
12.49%
35.92%
47.52%
39.37%
59.83%
70.80%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
22.80%
31.49%
52.43%
32.36%
43.27%
51.21%
12.49%
35.92%
47.52%
39.37%
59.83%
70.80%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
22.51%
31.11%
52.22%
31.90%
42.74%
50.67%
12.15%
35.89%
47.25%
39.14%
59.52%
70.49%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
8.74%
18.12%
35.59%
16.55%
30.37%
46.77%
8.89%
25.34%
35.26%
25.79%
50.38%
65.99%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
8.74%
18.12%
35.59%
16.55%
30.37%
46.77%
8.89%
25.34%
35.26%
25.79%
50.38%
65.99%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
22.51%
31.11%
52.22%
31.90%
42.74%
50.67%
12.15%
35.89%
47.25%
39.14%
59.52%
70.49%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
8.74%
18.12%
35.58%
16.55%
30.37%
46.77%
8.89%
25.34%
35.26%
25.79%
50.38%
65.99%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
8.74%
18.12%
35.58%
16.55%
30.36%
46.77%
8.89%
25.34%
35.26%
25.79%
50.38%
65.99%
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
22.51%
31.11%
52.22%
31.90%
42.74%
50.67%
12.15%
35.89%
47.25%
39.14%
59.52%
70.49%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
22.80%
31.49%
52.43%
32.35%
43.27%
51.21%
12.49%
35.92%
47.52%
39.37%
59.83%
70.80%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
22.80%
31.49%
52.43%
32.35%
43.27%
51.21%
12.49%
35.92%
47.52%
39.37%
59.83%
70.80%
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
14.20%
23.75%
42.26%
23.86%
36.67%
47.65%
10.26%
29.45%
40.77%
32.60%
53.11%
67.61%
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
14.22%
23.75%
42.26%
23.86%
36.67%
47.65%
10.26%
29.45%
40.77%
32.60%
53.11%
67.61%
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
23.67%
31.64%
53.51%
32.28%
43.13%
50.40%
12.46%
36.49%
48.34%
40.66%
60.20%
69.93%
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
25.70%
34.29%
55.43%
35.36%
46.00%
53.92%
13.26%
38.01%
49.68%
41.68%
61.88%
72.23%
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
21.22%
30.46%
50.81%
31.05%
43.05%
53.10%
12.34%
34.89%
46.13%
37.71%
58.73%
70.67%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
9.77%
20.37%
37.95%
19.69%
33.37%
48.65%
10.01%
27.08%
36.92%
27.07%
52.31%
67.86%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
24.22%
33.03%
54.22%
33.99%
45.09%
51.88%
12.85%
37.04%
48.60%
40.33%
61.08%
72.01%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
25.87%
34.48%
55.56%
35.61%
46.21%
51.74%
13.24%
38.26%
49.98%
41.98%
62.25%
72.51%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
23.33%
31.73%
52.79%
32.51%
43.28%
50.84%
12.42%
35.89%
47.50%
39.86%
59.35%
69.39%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
8.89%
17.54%
34.61%
15.85%
29.68%
46.23%
9.16%
25.04%
35.07%
25.74%
50.17%
64.91%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
25.82%
34.45%
55.51%
35.66%
46.17%
52.11%
13.39%
38.21%
49.85%
41.90%
62.07%
72.32%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
25.42%
34.13%
55.43%
34.94%
46.05%
52.03%
13.13%
37.87%
49.41%
41.49%
61.48%
72.45%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
25.53%
34.23%
55.41%
35.17%
46.09%
52.36%
13.32%
37.93%
49.49%
41.59%
61.46%
72.14%
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
28.46%
36.89%
58.87%
38.61%
48.80%
58.07%
13.65%
39.97%
51.96%
44.62%
63.52%
73.16%
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
28.29%
36.75%
58.68%
38.66%
48.72%
58.04%
13.61%
39.80%
51.77%
44.37%
63.40%
73.10%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
5.67%
11.83%
25.11%
9.95%
21.95%
42.54%
7.06%
21.13%
32.31%
23.60%
45.68%
53.62%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
5.66%
11.82%
25.09%
9.93%
21.99%
42.54%
7.06%
21.13%
32.31%
23.60%
45.68%
53.62%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
5.66%
11.82%
25.07%
9.95%
21.98%
42.54%
7.06%
21.13%
32.31%
23.60%
45.68%
53.62%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
15.58%
24.55%
44.10%
24.06%
36.84%
53.38%
10.50%
31.32%
43.40%
35.57%
54.82%
64.47%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
15.63%
25.41%
44.75%
25.16%
37.85%
52.71%
10.81%
31.73%
43.97%
36.07%
55.27%
68.69%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
15.59%
25.37%
44.73%
25.11%
37.85%
52.71%
10.81%
31.73%
43.97%
36.07%
55.27%
68.69%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
15.33%
25.07%
44.33%
25.10%
37.67%
52.74%
10.85%
31.55%
43.80%
35.92%
55.36%
67.10%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
5.77%
11.92%
25.26%
10.01%
21.72%
45.21%
7.34%
21.86%
33.08%
24.40%
46.58%
56.75%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
5.78%
11.94%
25.29%
10.05%
21.74%
45.23%
7.36%
21.88%
33.12%
24.40%
46.69%
56.76%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
15.31%
25.07%
44.32%
25.10%
37.67%
52.74%
10.85%
31.55%
43.80%
35.92%
55.36%
67.10%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
5.74%
11.92%
25.25%
10.01%
21.70%
45.21%
7.34%
21.86%
33.08%
24.40%
46.58%
56.75%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
5.76%
11.92%
25.30%
10.03%
21.76%
45.23%
7.36%
21.88%
33.12%
24.40%
46.69%
56.76%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
15.31%
25.07%
44.32%
25.10%
37.67%
52.74%
10.85%
31.55%
43.80%
35.92%
55.36%
67.10%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
15.59%
25.38%
44.74%
25.11%
37.85%
52.71%
10.81%
31.73%
43.97%
36.07%
55.27%
68.69%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
15.59%
25.38%
44.74%
25.11%
37.85%
52.71%
10.81%
31.73%
43.97%
36.07%
55.27%
68.69%
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
9.14%
16.51%
31.20%
16.04%
28.67%
46.70%
8.70%
25.33%
37.98%
30.87%
48.58%
56.88%
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
8.92%
16.40%
31.08%
15.96%
28.72%
46.68%
8.59%
25.28%
37.98%
30.87%
48.68%
57.34%
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
21.12%
30.99%
52.35%
31.69%
44.58%
55.21%
12.60%
35.27%
46.61%
38.12%
59.32%
72.68%
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
28.31%
36.72%
58.74%
38.51%
48.62%
58.50%
13.66%
39.84%
51.73%
44.38%
63.32%
73.88%
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
14.90%
24.99%
44.56%
23.74%
39.08%
54.17%
10.66%
30.86%
41.50%
32.84%
54.40%
67.98%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
9.89%
20.41%
38.37%
19.50%
33.56%
52.44%
10.10%
27.49%
37.51%
28.06%
52.59%
68.63%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
23.07%
32.38%
54.25%
32.79%
44.75%
56.38%
12.35%
36.41%
48.44%
40.65%
60.39%
74.86%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
28.35%
36.80%
58.70%
38.55%
48.74%
57.96%
13.66%
39.94%
51.81%
44.32%
63.53%
73.66%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
17.64%
26.41%
46.49%
26.12%
39.07%
52.55%
10.94%
32.90%
45.51%
37.94%
57.15%
68.79%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
6.94%
13.00%
27.42%
10.78%
23.54%
46.48%
7.53%
22.62%
34.09%
26.01%
47.38%
57.83%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
28.38%
36.82%
58.80%
38.51%
48.69%
58.40%
13.66%
39.89%
51.86%
44.45%
63.47%
74.50%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
28.04%
36.60%
58.50%
38.34%
48.74%
57.79%
13.57%
39.70%
51.48%
43.98%
63.00%
73.22%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
28.05%
36.59%
58.60%
38.10%
48.75%
58.31%
13.57%
39.63%
51.50%
44.00%
63.08%
73.90%
tiled — 640×640 tiles, tiled weights
Size
Platform
Runtime
Decode
APs
Δ APs vs FP32 (pp)
AP
AP50
Time per image (ms)
Realized FPS
Source
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
19.98%
ref
28.48%
47.82%
33.48
58.5
v-1968
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
19.85%
-0.13
28.39%
47.80%
28.20
71.3
v-19bf
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
11.86%
-8.12
20.60%
41.34%
1476.47
0.9
v-1b86
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
11.86%
-8.12
20.60%
41.34%
372.97
2.9
v-1a8c
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
11.86%
-8.12
20.60%
41.34%
373.59
2.9
v-1a91
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
17.66%
-2.32
25.26%
45.29%
416.68
3.2
v-1a96
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
11.74%
-8.24
20.71%
41.39%
410.82
2.9
v-1c03
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
17.88%
-2.10
25.73%
45.79%
169.41
11.4
v-1706
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
17.88%
-2.10
25.74%
45.79%
212.48
7.8
v-19e9
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
18.01%
-1.97
25.96%
45.88%
164.06
11.4
v-1a17
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
11.86%
-8.12
20.98%
41.86%
219.14
5.5
v-1a13
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
11.82%
-8.16
20.96%
41.83%
223.04
5.3
v-1a11
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
18.01%
-1.97
25.96%
45.88%
180.27
10.5
v-1a1a
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
11.86%
-8.12
20.98%
41.86%
277.48
12.9
v-1a16
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
11.82%
-8.16
20.96%
41.83%
243.18
15.6
v-1a12
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
18.01%
-1.97
25.96%
45.88%
185.29
9.9
v-1866
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
17.88%
-2.10
25.74%
45.79%
178.68
11.1
v-16fb
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
17.88%
-2.10
25.73%
45.79%
215.91
7.6
v-19f2
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
14.58%
-5.40
22.89%
43.52%
83.87
21.2
v-15b4
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
14.56%
-5.42
22.86%
43.51%
68.20
54.3
v-1903
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
18.94%
-1.04
27.20%
47.30%
92.19
15.0
v-15e0
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
19.88%
-0.10
28.43%
47.80%
128.30
46.6
v-190a
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
17.33%
-2.65
25.83%
45.51%
118.93
56.0
v-190c
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
13.78%
-6.20
22.82%
44.42%
39.40
125.5
v-154a
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
18.89%
-1.09
27.41%
47.19%
48.34
66.6
v-154c
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
19.86%
-0.12
28.38%
47.75%
92.11
51.9
v-154e
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
18.16%
-1.82
26.46%
46.67%
47.69
68.7
v-1d36
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
12.15%
-7.83
21.48%
42.58%
39.42
127.1
v-1d37
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
19.67%
-0.31
28.26%
47.66%
87.46
52.2
v-1d38
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
19.51%
-0.47
28.14%
47.63%
69.20
48.9
v-1d3a
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
19.67%
-0.31
28.23%
47.67%
64.22
48.5
v-1d39
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
23.69%
ref
32.42%
53.39%
46.82
50.0
v-196c
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
23.62%
-0.07
32.41%
53.38%
34.22
65.4
v-19c5
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
13.13%
-10.56
22.37%
44.64%
1832.52
0.9
v-1bb2
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
13.13%
-10.56
22.37%
44.64%
666.98
1.6
v-1b01
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
13.13%
-10.56
22.37%
44.64%
667.20
1.6
v-1b0f
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
19.77%
-3.92
28.39%
49.12%
712.89
1.6
v-1b1e
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
13.19%
-10.50
22.26%
44.77%
708.98
1.6
v-1c27
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
20.11%
-3.58
28.43%
49.55%
300.17
5.6
v-1723
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
20.11%
-3.58
28.43%
49.55%
576.66
5.7
v-19f0
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
20.31%
-3.38
28.60%
49.74%
289.23
5.5
v-1a82
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
13.51%
-10.18
22.35%
44.83%
341.56
3.3
v-1a7d
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
13.49%
-10.20
22.32%
44.83%
348.29
3.2
v-1a79
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
20.31%
-3.38
28.59%
49.73%
652.79
6.2
v-1a83
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
13.51%
-10.18
22.34%
44.83%
675.09
5.5
v-1a80
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
13.49%
-10.20
22.32%
44.83%
647.08
6.2
v-1a7b
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
20.31%
-3.38
28.59%
49.74%
647.07
6.2
v-1879
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
20.11%
-3.58
28.43%
49.55%
304.93
5.6
v-1735
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
20.11%
-3.58
28.43%
49.55%
574.63
5.8
v-19f6
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
16.20%
-7.49
24.43%
46.18%
108.06
15.9
v-15bb
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
16.16%
-7.53
24.39%
46.15%
88.06
27.8
v-190e
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
22.09%
-1.60
29.85%
51.54%
200.10
5.8
v-15fd
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
23.61%
-0.08
32.36%
53.32%
170.50
28.7
v-15c4
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
18.42%
-5.27
27.79%
49.14%
122.68
42.7
v-15c6
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
15.74%
-7.95
25.22%
48.74%
34.85
91.4
v-155f
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
22.79%
-0.90
31.23%
52.33%
52.11
76.4
v-1561
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
23.70%
+0.01
32.40%
53.38%
101.44
39.2
v-1562
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
20.48%
-3.21
28.74%
49.55%
57.99
67.1
v-1d74
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
13.76%
-9.93
22.70%
44.91%
38.68
92.0
v-1d75
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
23.72%
+0.03
32.36%
53.31%
90.85
39.3
v-1d76
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
23.36%
-0.33
32.15%
53.14%
76.56
36.5
v-1d78
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
23.55%
-0.14
32.26%
53.16%
72.21
36.4
v-1d77
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
26.32%
ref
34.69%
56.34%
88.64
34.6
v-1971
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
26.28%
-0.04
34.70%
56.35%
59.92
46.9
v-19ca
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
9.94%
-16.38
16.68%
38.01%
2516.62
0.8
v-1c0b
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
9.86%
-16.46
16.62%
37.95%
1209.94
0.9
v-1b6e
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
9.86%
-16.46
16.62%
37.95%
1209.94
0.9
v-1b7f
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
14.03%
-12.29 ⚠
22.30%
43.78%
1247.25
0.9
v-1b89
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
14.13%
-12.19 ⚠
22.42%
44.24%
570.95
2.3
v-1747
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
14.13%
-12.19 ⚠
22.43%
44.26%
1665.52
2.4
v-1a02
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
14.78%
-11.54 ⚠
22.93%
44.83%
527.95
2.3
v-1ac0
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
10.11%
-16.21
16.80%
38.63%
586.34
1.8
v-1ab6
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
10.24%
-16.08
16.86%
38.74%
593.57
1.8
v-1aa8
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
14.73%
-11.59 ⚠
22.90%
44.82%
1653.81
2.4
v-1ac3
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
10.12%
-16.20
16.80%
38.63%
1676.50
2.3
v-1abb
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
10.18%
-16.14
16.80%
38.65%
1645.51
2.4
v-1ab2
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
14.73%
-11.59 ⚠
22.90%
44.82%
1655.29
2.4
v-1884
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
14.12%
-12.20 ⚠
22.43%
44.29%
573.63
2.3
v-1752
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
14.12%
-12.20 ⚠
22.43%
44.29%
1658.73
2.4
v-1a0f
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
12.07%
-14.25 ⚠
18.47%
39.66%
176.08
8.5
v-15ee
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
12.10%
-14.22 ⚠
18.51%
39.77%
150.94
10.6
v-1922
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
21.75%
-4.57
30.39%
53.92%
344.62
3.1
v-1619
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
26.24%
-0.08
34.68%
56.36%
342.11
14.2
v-1934
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
15.36%
-10.96 ⚠
24.80%
46.82%
209.52
21.6
v-1937
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
18.05%
-8.27
27.63%
52.27%
56.82
47.8
v-15a7
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
25.04%
-1.28
33.12%
55.18%
89.13
49.6
v-15a8
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
26.35%
+0.03
34.76%
56.28%
150.17
21.5
v-15a9
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
17.94%
-8.38
26.46%
48.48%
91.54
49.8
v-1dcb
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
12.40%
-13.92
19.87%
42.46%
57.90
48.9
v-1dcc
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
26.42%
+0.10
34.74%
56.35%
144.82
21.1
v-1dcd
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
26.04%
-0.28
34.54%
56.18%
131.28
17.8
v-1dcf
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
26.14%
-0.18
34.55%
56.24%
134.07
17.5
v-1dce
Full metrics — tiled
Size
Platform
Runtime
Decode
Precision
APs
AP
AP50
AP75
APm
APl
AR@1
AR@10
AR@100
ARs
ARm
ARl
Nano
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
19.98%
28.48%
47.82%
28.65%
39.52%
48.53%
11.45%
33.82%
43.71%
35.04%
56.23%
58.42%
Nano
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
19.85%
28.39%
47.80%
28.55%
39.44%
48.56%
11.41%
33.74%
43.61%
34.94%
56.11%
58.77%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
11.86%
20.60%
41.34%
18.31%
31.89%
45.28%
9.45%
26.26%
33.20%
23.47%
47.16%
53.59%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
11.86%
20.60%
41.34%
18.31%
31.89%
45.28%
9.45%
26.26%
33.20%
23.47%
47.15%
53.59%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
11.86%
20.60%
41.34%
18.32%
31.90%
45.28%
9.45%
26.26%
33.20%
23.47%
47.16%
53.59%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
17.66%
25.26%
45.29%
24.78%
34.83%
48.31%
10.66%
31.11%
40.07%
31.76%
51.71%
56.88%
Nano
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
11.74%
20.71%
41.39%
18.48%
32.50%
42.61%
9.39%
26.22%
33.37%
23.34%
47.81%
52.81%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
17.88%
25.73%
45.79%
25.26%
35.68%
44.66%
10.77%
31.41%
40.67%
32.15%
52.67%
55.47%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
17.88%
25.74%
45.79%
25.26%
35.68%
44.66%
10.77%
31.41%
40.67%
32.15%
52.67%
55.47%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
18.01%
25.96%
45.88%
25.67%
35.78%
46.33%
10.82%
31.50%
40.72%
32.30%
52.56%
57.30%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
11.86%
20.98%
41.86%
18.59%
32.83%
42.89%
9.57%
26.40%
33.63%
23.33%
48.21%
55.41%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
11.82%
20.96%
41.83%
18.50%
32.81%
42.57%
9.56%
26.37%
33.60%
23.30%
48.18%
55.15%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
18.01%
25.96%
45.88%
25.67%
35.78%
46.32%
10.82%
31.50%
40.72%
32.30%
52.56%
57.30%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
11.86%
20.98%
41.86%
18.59%
32.84%
42.90%
9.57%
26.40%
33.63%
23.33%
48.21%
55.41%
Nano
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
11.82%
20.96%
41.83%
18.50%
32.82%
42.57%
9.56%
26.37%
33.60%
23.30%
48.18%
55.15%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
18.01%
25.96%
45.88%
25.67%
35.78%
46.33%
10.82%
31.50%
40.72%
32.30%
52.56%
57.30%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
17.88%
25.74%
45.79%
25.26%
35.68%
44.66%
10.77%
31.41%
40.67%
32.15%
52.67%
55.47%
Nano
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
17.88%
25.73%
45.79%
25.26%
35.68%
44.66%
10.77%
31.41%
40.67%
32.15%
52.67%
55.47%
Nano
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
14.58%
22.89%
43.52%
21.35%
33.49%
42.50%
10.02%
28.58%
36.70%
27.56%
49.80%
53.27%
Nano
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
14.56%
22.86%
43.51%
21.33%
33.46%
42.49%
10.01%
28.54%
36.66%
27.54%
49.76%
53.27%
Nano
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
18.94%
27.20%
47.30%
26.79%
37.74%
47.01%
11.04%
32.33%
41.77%
33.13%
54.14%
57.59%
Nano
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
19.88%
28.43%
47.80%
28.57%
39.46%
48.60%
11.40%
33.79%
43.67%
34.95%
56.19%
59.36%
Nano
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
17.33%
25.83%
45.51%
25.35%
36.58%
42.90%
10.87%
31.37%
40.50%
31.92%
52.37%
51.74%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
13.78%
22.82%
44.42%
20.98%
34.73%
42.97%
9.96%
28.32%
36.05%
25.90%
51.06%
54.88%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
18.89%
27.41%
47.19%
27.10%
38.20%
46.03%
11.19%
32.70%
42.37%
33.59%
55.15%
57.09%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
19.86%
28.38%
47.75%
28.50%
39.41%
47.91%
11.41%
33.73%
43.60%
34.97%
56.08%
57.69%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
18.16%
26.46%
46.67%
26.08%
37.04%
44.36%
10.98%
31.74%
41.13%
32.42%
53.67%
53.90%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
12.15%
21.48%
42.58%
19.07%
33.41%
43.26%
9.77%
26.88%
34.06%
23.91%
48.47%
53.52%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
19.67%
28.26%
47.66%
28.35%
39.43%
48.13%
11.41%
33.60%
43.35%
34.59%
56.05%
58.20%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
19.51%
28.14%
47.63%
28.23%
39.34%
47.53%
11.35%
33.41%
43.11%
34.45%
55.54%
56.77%
Nano
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
19.67%
28.23%
47.67%
28.33%
39.34%
48.14%
11.40%
33.58%
43.33%
34.60%
56.01%
58.21%
Small
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
23.69%
32.42%
53.39%
33.52%
44.01%
47.30%
12.81%
36.92%
47.42%
39.40%
58.96%
61.56%
Small
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
23.62%
32.41%
53.38%
33.44%
44.07%
47.27%
12.82%
36.89%
47.38%
39.31%
59.01%
61.49%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
13.13%
22.37%
44.64%
20.36%
35.32%
38.99%
10.08%
28.19%
35.74%
26.04%
49.94%
49.80%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
13.13%
22.37%
44.64%
20.36%
35.32%
38.99%
10.08%
28.19%
35.74%
26.04%
49.94%
49.80%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
13.13%
22.37%
44.64%
20.36%
35.32%
38.99%
10.08%
28.19%
35.74%
26.04%
49.94%
49.80%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
19.77%
28.39%
49.12%
28.50%
39.62%
41.28%
11.64%
33.94%
43.71%
35.83%
55.16%
52.35%
Small
NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency
LiteRT
Logical
INT8
13.19%
22.26%
44.77%
20.16%
35.35%
40.41%
9.91%
28.23%
36.00%
26.68%
49.92%
52.94%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
20.11%
28.43%
49.55%
28.18%
39.95%
40.29%
11.65%
34.13%
44.12%
36.35%
55.75%
53.96%
Small
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
20.11%
28.43%
49.55%
28.18%
39.95%
40.29%
11.65%
34.13%
44.12%
36.35%
55.75%
53.96%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
20.31%
28.60%
49.74%
28.63%
40.30%
40.24%
11.67%
34.26%
44.37%
36.65%
56.26%
53.60%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
13.51%
22.35%
44.83%
20.11%
35.27%
38.06%
9.92%
28.37%
36.06%
26.79%
50.26%
50.57%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
13.49%
22.32%
44.83%
20.10%
35.29%
38.14%
9.91%
28.35%
36.03%
26.75%
50.25%
50.66%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
20.31%
28.59%
49.73%
28.63%
40.30%
40.24%
11.67%
34.26%
44.37%
36.65%
56.26%
53.60%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
13.51%
22.34%
44.83%
20.11%
35.27%
38.06%
9.92%
28.37%
36.06%
26.79%
50.26%
50.57%
Small
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
13.49%
22.32%
44.83%
20.10%
35.28%
38.14%
9.91%
28.35%
36.03%
26.75%
50.25%
50.66%
Small
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
20.31%
28.59%
49.74%
28.63%
40.30%
40.24%
11.67%
34.26%
44.37%
36.65%
56.26%
53.60%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
20.11%
28.43%
49.55%
28.18%
39.95%
40.29%
11.65%
34.13%
44.12%
36.35%
55.75%
53.96%
Small
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
20.11%
28.43%
49.55%
28.18%
39.95%
40.29%
11.65%
34.13%
44.12%
36.35%
55.75%
53.96%
Small
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
16.20%
24.43%
46.18%
23.22%
36.40%
40.00%
10.44%
30.47%
39.45%
31.37%
51.11%
49.33%
Small
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
16.16%
24.39%
46.15%
23.15%
36.35%
40.17%
10.44%
30.46%
39.42%
31.34%
51.12%
49.35%
Small
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
22.09%
29.85%
51.54%
29.89%
40.59%
43.98%
11.92%
35.48%
45.60%
38.17%
56.56%
57.07%
Small
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
23.61%
32.36%
53.32%
33.37%
43.98%
47.10%
12.71%
36.73%
47.32%
39.29%
58.90%
60.86%
Small
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
18.42%
27.79%
49.14%
27.18%
40.79%
44.12%
11.44%
32.98%
42.12%
33.06%
55.73%
60.24%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
15.74%
25.22%
48.74%
22.89%
38.50%
43.69%
10.85%
30.13%
37.97%
28.02%
52.71%
58.46%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
22.79%
31.23%
52.33%
32.10%
42.51%
45.11%
12.28%
36.14%
46.48%
38.67%
57.82%
60.27%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
23.70%
32.40%
53.38%
33.57%
43.95%
46.73%
12.67%
36.84%
47.38%
39.43%
58.96%
60.95%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
20.48%
28.74%
49.55%
28.72%
39.97%
40.71%
11.86%
34.59%
44.70%
36.97%
56.39%
54.81%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
13.76%
22.70%
44.91%
20.65%
35.51%
39.10%
10.28%
28.92%
36.77%
27.28%
51.24%
53.16%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
23.72%
32.36%
53.31%
33.45%
43.89%
46.42%
12.70%
36.76%
47.25%
39.28%
58.93%
60.03%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
23.36%
32.15%
53.14%
32.92%
43.93%
46.41%
12.60%
36.60%
46.96%
38.96%
58.55%
60.65%
Small
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
23.55%
32.26%
53.16%
33.18%
43.84%
46.28%
12.64%
36.64%
47.03%
39.12%
58.45%
59.93%
Medium
ONNX FP32 (CUDA · RTX 3090)
ONNX
—
FP32
26.32%
34.69%
56.34%
36.16%
46.29%
53.24%
13.35%
38.76%
49.84%
42.44%
61.07%
64.69%
Medium
ONNX FP16 (CUDA · RTX 3090)
ONNX
—
FP16
26.28%
34.70%
56.35%
36.14%
46.34%
53.23%
13.33%
38.71%
49.75%
42.32%
61.09%
64.80%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency
LiteRT
Logical
INT8
9.94%
16.68%
38.01%
12.49%
28.66%
34.77%
7.89%
25.11%
33.15%
24.70%
46.57%
44.41%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Combined
INT8
9.86%
16.62%
37.95%
12.38%
28.65%
34.77%
7.89%
25.11%
33.15%
24.70%
46.57%
44.41%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Logical
INT8
9.86%
16.62%
37.95%
12.38%
28.64%
34.77%
7.89%
25.11%
33.15%
24.70%
46.57%
44.41%
Medium
NXP i.MX 8M Plus + VeriSilicon NPU (FRDM)
LiteRT
Smart
INT8
14.03%
22.30%
43.78%
19.80%
33.46%
40.89%
9.70%
29.76%
39.01%
31.42%
51.07%
49.26%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency
LiteRT
Smart
INT8
14.13%
22.42%
44.24%
19.73%
33.98%
42.11%
9.78%
30.17%
39.62%
31.53%
52.22%
55.64%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput
LiteRT
Smart
INT8
14.13%
22.43%
44.26%
19.73%
33.98%
42.11%
9.78%
30.17%
39.62%
31.53%
52.22%
55.64%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Smart
INT8
14.78%
22.93%
44.83%
20.19%
34.59%
44.37%
9.82%
30.39%
39.83%
31.86%
52.41%
57.96%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Logical
INT8
10.11%
16.80%
38.63%
12.63%
29.43%
35.47%
7.85%
25.45%
33.59%
24.98%
47.46%
50.33%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency
LiteRT
Combined
INT8
10.24%
16.86%
38.74%
12.63%
29.47%
35.40%
7.86%
25.50%
33.64%
25.03%
47.56%
50.02%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Smart
INT8
14.73%
22.90%
44.82%
20.14%
34.60%
44.37%
9.82%
30.39%
39.83%
31.86%
52.41%
57.96%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Logical
INT8
10.12%
16.80%
38.63%
12.63%
29.43%
35.47%
7.85%
25.45%
33.59%
24.98%
47.46%
50.33%
Medium
NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput
LiteRT
Combined
INT8
10.18%
16.80%
38.65%
12.60%
29.46%
35.40%
7.86%
25.50%
33.64%
25.03%
47.56%
50.02%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput
LiteRT
Smart
INT8
14.73%
22.90%
44.82%
20.14%
34.59%
44.37%
9.82%
30.39%
39.83%
31.86%
52.41%
57.96%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency
LiteRT
Smart
INT8
14.12%
22.43%
44.29%
19.72%
33.98%
42.10%
9.78%
30.17%
39.62%
31.53%
52.22%
55.64%
Medium
NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput
LiteRT
Smart
INT8
14.12%
22.43%
44.29%
19.72%
33.98%
42.10%
9.78%
30.17%
39.62%
31.53%
52.22%
55.64%
Medium
NXP Ara240 (FRDM) — latency
Ara DVM
Smart
INT16
12.07%
18.47%
39.66%
15.25%
30.17%
32.58%
8.50%
27.40%
36.53%
28.83%
48.95%
43.98%
Medium
NXP Ara240 (FRDM) — throughput
Ara DVM
Smart
INT16
12.10%
18.51%
39.77%
15.33%
30.22%
32.79%
8.54%
27.45%
36.57%
28.85%
49.04%
44.16%
Medium
Raspberry Pi 5 + Hailo-8L NPU
Hailo HEF
—
INT8
21.75%
30.39%
53.92%
30.06%
43.04%
50.51%
12.21%
35.18%
44.64%
36.08%
57.65%
63.01%
Medium
NVIDIA Jetson Orin Nano (TensorRT FP16)
TensorRT
—
FP16
26.24%
34.68%
56.36%
36.03%
46.34%
53.21%
13.39%
38.70%
49.72%
42.27%
60.96%
64.66%
Medium
NVIDIA Jetson Orin Nano (TensorRT INT8)
TensorRT
—
INT8
15.36%
24.80%
46.82%
23.14%
38.81%
47.20%
10.75%
30.85%
39.37%
30.16%
53.49%
58.61%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — throughput preset
ONNX · QNN EP
Logical
INT8
18.05%
27.63%
52.27%
25.71%
41.38%
50.53%
11.63%
32.21%
40.55%
30.77%
55.51%
62.51%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · TFE + SeqMSE) — balanced preset
ONNX · QNN EP
Smart
INT8
25.04%
33.12%
55.18%
34.05%
44.69%
48.39%
12.93%
37.85%
48.73%
41.27%
60.37%
62.65%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard) — accuracy preset
ONNX · QNN EP
Smart
INT16
26.35%
34.76%
56.28%
36.23%
46.26%
53.31%
13.39%
38.76%
49.83%
42.44%
61.02%
64.71%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Smart
INT8
17.94%
26.46%
48.48%
25.38%
38.20%
47.22%
11.29%
32.77%
42.54%
34.31%
55.21%
58.06%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8 · Standard)
ONNX · QNN EP
Logical
INT8
12.40%
19.87%
42.46%
16.55%
32.38%
38.48%
9.30%
27.36%
35.55%
26.65%
49.81%
51.04%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Smart
INT16
26.42%
34.74%
56.35%
36.19%
46.18%
53.35%
13.44%
38.74%
49.79%
42.46%
60.90%
64.76%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · Standard)
ONNX · QNN EP
Logical
INT16
26.04%
34.54%
56.18%
35.98%
46.20%
53.71%
13.34%
38.51%
49.44%
42.01%
60.53%
64.64%
Medium
Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16 · TFE + SeqMSE)
ONNX · QNN EP
Logical
INT16
26.14%
34.55%
56.24%
35.99%
46.19%
53.55%
13.39%
38.56%
49.47%
42.07%
60.56%
64.75%
Validation pipeline
EdgeFirst Profiler runs on the target hardware, executes the full inference pipeline (capture → preprocess → inference → postprocess, with slicing and merging for the tiled variant), and scores the predictions against ground truth on the device .EdgeFirst Studio receives the metrics, predictions, charts and trace, and publishes them as the v-XXXX validation session every row above cites.EdgeFirst HAL (open source ) provides the hardware-accelerated preprocessing and post-decoding primitives used at both validation and deployment time.
Validation runs decode a JPEG per image and evaluate at a 0.001 confidence threshold to capture every detection for mAP. A camera pipeline at a deployment threshold of 0.25–0.75 carries a lighter host and postprocess load.
Downloads
Every file below is the artifact a validation session in the tables above measured. Files are organized as {variant}/{target folder}/ and named visdrone-yolov8{size}-det-{precision}[-smart|-logical]-{variant}{extension}. Each model file embeds the EdgeFirst edgefirst.json metadata (training session, dataset version, calibration artifact, converter chain), so a single file is sufficient for deployment.
640/
ONNX FP32 — Any platform with ONNX Runtime.
Size
File
Variant
Download
Nano
640/onnx/visdrone-yolov8n-det-fp16-640.onnxFP16
Download
Nano
640/onnx/visdrone-yolov8n-det-fp32-640.onnxFP32
Download
Small
640/onnx/visdrone-yolov8s-det-fp16-640.onnxFP16
Download
Small
640/onnx/visdrone-yolov8s-det-fp32-640.onnxFP32
Download
Medium
640/onnx/visdrone-yolov8m-det-fp16-640.onnxFP16
Download
Medium
640/onnx/visdrone-yolov8m-det-fp32-640.onnxFP32
Download
TFLite INT8 — CPU or NPU via runtime delegate (NXP i.MX 8M Plus VX Delegate).
Size
File
Variant
Download
Nano
640/tflite/visdrone-yolov8n-det-int8-640.tfliteLogical · INT8
Download
Nano
640/tflite/visdrone-yolov8n-det-int8-smart-640.tfliteSmart · INT8
Download
Small
640/tflite/visdrone-yolov8s-det-int8-640.tfliteLogical · INT8
Download
Small
640/tflite/visdrone-yolov8s-det-int8-smart-640.tfliteSmart · INT8
Download
Medium
640/tflite/visdrone-yolov8m-det-int8-640.tfliteLogical · INT8
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1280/
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Qualcomm Dragonwing IQ-9075 (Hexagon v73) — Offline-compiled Hexagon context binary, run through the ONNX Runtime QNN execution provider. Loads only on the Dragonwing IQ-9075 (Hexagon v73).
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tiled/qnn/visdrone-yolov8s-det-w8a8-logical-tiled.iq9075.qnn.onnxLogical · INT8 · Standard
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tiled/qnn/visdrone-yolov8s-det-w8a8-smart-tfe-seqmse-tiled.iq9075.qnn.onnxSmart · INT8 · TFE + SeqMSE
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tiled/qnn/visdrone-yolov8s-det-w8a8-smart-tiled.iq9075.qnn.onnxSmart · INT8 · Standard
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tiled/qnn/visdrone-yolov8m-det-w8a16-smart-in8-tiled.iq9075.qnn.onnxSmart · INT16 · Standard
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tiled/qnn/visdrone-yolov8m-det-w8a8-logical-tiled.iq9075.qnn.onnxLogical · INT8 · Standard
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tiled/qnn/visdrone-yolov8m-det-w8a8-smart-tfe-seqmse-tiled.iq9075.qnn.onnxSmart · INT8 · TFE + SeqMSE
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tiled/qnn/visdrone-yolov8m-det-w8a8-smart-tiled.iq9075.qnn.onnxSmart · INT8 · Standard
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Traceability
Every measurement in the tables above is reachable through the EdgeFirst Studio validation framework. The v-XXXX Source link on each row resolves to a public Studio URL of the form:
https://edgefirst.studio/public/validation/v-XXXX/details?mode=charts
From there, the full provenance chain is one click deeper: training session ID, dataset version, calibration artifact, converter chain, validation parameters, and the host hardware description. The model file you download from this repository embeds the same chain in its edgefirst.json metadata.
See also
Limitations
Aerial bias — VisDrone is drone footage from Chinese cities: the scenes, object scales, viewpoints and the 10 classes do not transfer to ground-level cameras without retraining.Quantization loss — integer quantization costs accuracy against FP32, most visibly on small objects; the Δ APs vs FP32 column shows it per platform.Memory limits — the 1280 and 1344x768 variants exceed some accelerators' on-chip memory or attention limits; those cells read Not supported with the reason.
License
Model weights in this repository are derived from Ultralytics YOLO and remain © Ultralytics Inc., licensed AGPL-3.0 — use requires AGPL-3.0 compliance or an Ultralytics Enterprise License. The VisDrone dataset is © the AISKYEYE team, Tianjin University, and is subject to its own terms.
The validation results, this model card, and its metadata are Au-Zone Technologies' own contribution, licensed CC BY-NC 4.0 LICENSE for the full text and citation requirements.
Citation
@software{edgefirst_visdrone_yolov8_det,
title = { {YOLOv8 Detection on VisDrone — EdgeFirst Model Zoo} },
author = {Au-Zone Technologies},
url = {https://huggingface.co/EdgeFirst/visdrone-yolov8-det},
year = {2026},
license = {CC-BY-NC-4.0},
}
EdgeFirst Studio · GitHub · Docs · Au-Zone Technologies ® , i.MX, eIQ® , Neutron, and Ara240 are trademarks or products of NXP Semiconductors. Hailo is a trademark of Hailo Technologies Ltd. Jetson is a trademark of NVIDIA Corporation. Qualcomm and Dragonwing are trademarks of Qualcomm Incorporated. All other trademarks are the property of their respective owners.
