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