Vision Analysis
All Hardware

NVIDIA A100

NVIDIA A100-PCIE-40GB

VRAM

40 GB

FP32 Performance

19.5 TFLOPS

Power (TDP)

250W

Highest Accuracy
Top 5 models by mAP@50-95
59.3%
57.3%
55.1%
52.7%
Fastest
Top 5 models by throughput
28 FPS
27 FPS
26 FPS
25 FPS
24 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
13 models benchmarked on NVIDIA A100 (PyTorch FP32)
#ModelDatasetCredit / sourcemAPFPSLatencyParams
1
dfine-x
full (5,000 images)59.3%15.464.7ms62.6M
2
dfine-l
full (5,000 images)57.3%17.158.3ms31.2M
3
dfine-m
full (5,000 images)55.1%21.746.1ms19.6M
4
rtdetr-r101
full (5,000 images)53.9%18.653.6ms76.6M
5
rtdetr-r50
full (5,000 images)52.7%22.744.1ms42.9M
6
yolonas-l
full (5,000 images)51.2%16.660.4ms67.0M
7
rtdetr-r50m
full (5,000 images)50.8%25.139.8ms36.6M
8
dfine-s
full (5,000 images)50.7%24.041.7ms10.3M
9
yolonas-m
full (5,000 images)50.5%17.058.9ms51.2M
10
rtdetr-r34
full (5,000 images)48.2%27.836.0ms31.4M
11
yolonas-s
full (5,000 images)46.5%16.660.1ms19.1M
12
rtdetr-r18
full (5,000 images)45.6%27.436.5ms20.2M
13
dfine-n
full (5,000 images)42.8%25.639.1ms3.8M

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