Vision Analysis
All Hardware

NVIDIA Jetson Orin Nano Super 8GB

NVIDIA Ampere GPU (Jetson Orin)

VRAM

8 GB

RAM

8 GB

Power (TDP)

15W

Runtime:
Highest Accuracy
Top 5 models by mAP@50-95
61.4%
61.3%
61.1%
60.1%
60.0%
Fastest
Top 5 models by throughput
26 FPS
26 FPS
23 FPS
23 FPS
22 FPS

Results are contributor-reported. Validation checks the submission format and supported configuration; it does not independently reproduce a run. Compare accuracy on the same dataset and latency with the software versions recorded in each run JSON.

All Models
78 models benchmarked on NVIDIA Jetson Orin Nano Super 8GB (PyTorch FP32)
#ModelDatasetCredit / sourcemAPFPSLatencyParams
1
dfine-x
mini500 (500 images)61.4%2.9350.0ms62.6M
2
deimv2-x
mini500 (500 images)61.3%2.7370.1ms51.2M
3
ec-x
mini500 (500 images)61.1%2.9349.5ms49.9M
4
ec-l
mini500 (500 images)60.1%3.4296.0ms33.0M
5
rtdetrv4-x
mini500 (500 images)60.0%2.8351.8ms62.6M
6
dfine-l
mini500 (500 images)60.0%4.7211.7ms31.2M
7
deim-x
mini500 (500 images)59.6%2.9349.8ms62.6M
8
deimv2-l
mini500 (500 images)58.6%3.3303.8ms32.5M
9
rfdetr-l
mini500 (500 images)58.5%4.0248.5ms33.9M
10
ec-m
mini500 (500 images)58.4%5.0200.8ms19.4M
11
rtdetr-x
mini500 (500 images)57.9%3.0338.1ms67.4M
12
deim-l
mini500 (500 images)57.8%4.8209.3ms31.2M
13
dfine-m
mini500 (500 images)57.8%7.0143.3ms19.6M
14
rtdetrv4-l
mini500 (500 images)57.8%4.7210.8ms31.2M
15
rfdetr-m
mini500 (500 images)57.4%5.7174.5ms33.7M
16
yolov9c
mini500 (500 images)57.1%6.4155.6ms25.5M
17
rtdetr-r101
mini500 (500 images)56.8%2.8359.6ms76.6M
18
rtdetrv2-r101
mini500 (500 images)56.8%2.8358.7ms76.6M
19
rtdetrv4-m
mini500 (500 images)56.5%7.0143.1ms19.6M
20
yolo26x
mini500 (500 images)56.5%5.2193.6ms55.7M
21
yolonas-l
mini500 (500 images)56.3%5.0200.2ms67.0M
22
yolox-x
mini500 (500 images)56.3%3.5289.6ms99.1M
23
yolov9m
mini500 (500 images)56.1%8.0124.8ms20.1M
24
deimv2-m
mini500 (500 images)56.0%4.7213.5ms18.4M
25
rtdetr-r50
mini500 (500 images)55.9%4.3234.6ms42.9M
26
rtdetr-l
mini500 (500 images)55.8%4.9206.0ms32.9M
27
rtdetrv2-r50
mini500 (500 images)55.7%4.3233.4ms42.9M
28
yolonas-m
mini500 (500 images)55.5%6.6152.0ms51.2M
29
deim-m
mini500 (500 images)55.4%7.0143.7ms19.6M
30
yolox-l
mini500 (500 images)55.4%5.8171.7ms54.2M
31
rfdetr-s
mini500 (500 images)55.1%6.9144.6ms32.1M
32
rtdetrv2-r50m
mini500 (500 images)54.8%5.1196.8ms36.6M
33
ec-s
mini500 (500 images)54.3%6.0168.2ms9.9M
34
rtdetr-r50m
mini500 (500 images)53.8%5.0198.3ms36.6M
35
yolo26l
mini500 (500 images)53.8%9.3107.2ms24.8M
36
yolo11x
mini500 (500 images)53.6%5.1196.1ms56.9M
37
dfine-s
mini500 (500 images)53.4%10.396.8ms10.3M
38
rtdetrv2-r34
mini500 (500 images)53.2%8.1123.1ms31.4M
39
yolov8x
mini500 (500 images)53.0%5.4184.8ms68.2M
40
deimv2-s
mini500 (500 images)53.0%5.7175.8ms9.8M
41
rtdetrv4-s
mini500 (500 images)52.8%10.199.3ms10.3M
42
yolov5xu
mini500 (500 images)52.3%5.0200.2ms97.2M
43
yolo11l
mini500 (500 images)52.3%9.1109.9ms25.3M
44
yolo26m
mini500 (500 images)52.2%11.388.6ms20.4M
45
rtdetr-r34
mini500 (500 images)52.2%8.0125.8ms31.4M
46
deim-s
mini500 (500 images)52.1%10.298.1ms10.3M
47
yolov8l
mini500 (500 images)52.0%8.3120.3ms43.7M
48
yolonas-s
mini500 (500 images)51.8%10.298.0ms19.1M
49
yolox-m
mini500 (500 images)51.7%9.1110.1ms25.3M
50
yolov5lu
mini500 (500 images)51.5%8.6116.9ms53.2M
51
rfdetr-n
mini500 (500 images)51.4%10.298.3ms30.5M
52
rtdetrv2-r18
mini500 (500 images)50.8%10.397.4ms20.2M
53
yolo11m
mini500 (500 images)50.6%11.190.3ms20.1M
54
yolov9s
mini500 (500 images)50.5%9.9101.2ms7.2M
55
rtdetr-r18
mini500 (500 images)49.8%10.298.3ms20.2M
56
yolov8m
mini500 (500 images)49.4%12.282.1ms25.9M
57
yolov5mu
mini500 (500 images)48.2%12.877.9ms25.1M
58
yolo26s
mini500 (500 images)47.4%20.149.9ms9.5M
59
deim-n
mini500 (500 images)46.8%11.388.5ms3.8M
60
deimv2-n
mini500 (500 images)46.7%11.189.8ms3.6M
61
dfine-n
mini500 (500 images)45.8%11.785.5ms3.8M
62
yolo11s
mini500 (500 images)45.7%20.748.3ms9.4M
63
yolox-s
mini500 (500 images)44.3%16.959.2ms9.0M
64
yolov8s
mini500 (500 images)44.2%22.145.1ms11.2M
65
picodet-l
mini500 (500 images)44.1%11.190.1ms3.3M
66
yolov5su
mini500 (500 images)42.4%22.844.0ms9.1M
67
deimv2-pico
mini500 (500 images)42.2%14.171.1ms1.5M
68
yolov9t
mini500 (500 images)41.8%9.9101.2ms2.0M
69
yolo26n
mini500 (500 images)40.1%21.646.4ms2.4M
70
yolo11n
mini500 (500 images)38.7%22.943.8ms2.6M
71
picodet-m
mini500 (500 images)37.9%12.580.3ms2.1M
72
yolov8n
mini500 (500 images)36.7%26.437.8ms3.1M
73
yolox-tiny
mini500 (500 images)35.5%20.349.2ms5.1M
74
deimv2-femto
mini500 (500 images)34.5%14.867.5ms1.0M
75
yolov5nu
mini500 (500 images)33.8%26.138.3ms2.6M
76
picodet-s
mini500 (500 images)30.4%13.872.2ms1.0M
77
yolox-nano
mini500 (500 images)28.8%18.454.5ms0.9M
78
deimv2-atto
mini500 (500 images)27.5%15.464.9ms0.5M

Run any model with one line

LibreYOLO has the best catalogue of state-of-the-art detectors, all behind one MIT-licensed Python API.

from libreyolo import LibreYOLO, SAMPLE_IMAGE

# LibreYOLO has the best catalogue of state-of-the-art models.
model = LibreYOLO("LibreRFDETRl.pt")           # RF-DETR-L (transformer flagship)
results = model(SAMPLE_IMAGE, save=True)        # run inference, save the annotated image

# Swap in any other model, same one-line API (weights auto-download):
#   LibreYOLO("LibreYOLO9c.pt")      # YOLO9-C
#   LibreYOLO("LibreYOLOXx.pt")      # YOLOX-X
#   LibreYOLO("LibreDFINEx.pt")      # D-FINE-X
#   LibreYOLO("LibreRTDETRr50.pt")   # RT-DETR-R50