YoloLiteV2 now pip installable
I posted last week about an upgrade to my repo YoloLite. I have now decided to launch V2 directly via PyPI! You can test it out right now with a simple pip install yololite and help me find bugs and benchmark the models.
Everything is Apache 2.0, and the weights are automatically downloaded from GitHub on demand.
You can either use the API directly via Python or run everything via the CLI:
yololite mode=predict model=yololite_cs3_m.pt source=test.jpg conf=0.4 save=True
yololite mode=train model=yololite_mnv4_s.pt data="data.yaml" epochs=30 workers=4
I have pretrained a total of 9 models across 3 different lightweight backbones:
- CS3Darknet backbone:
yololite_cs3_n.pt|yololite_cs3_s.pt|yololite_cs3_m.pt - MobileNetV4 backbone:
yololite_mnv4_n.pt|yololite_mnv4_s.pt|yololite_mnv4_m.pt - HGNetV2 backbone:
yololite_hg2_n.pt|yololite_hg2_s.pt|yololite_hg2_m.pt
The models have been pretrained on the official COCO-minitrain_25k dataset. (Check out their official repo for more info on the Pearson correlation coefficients between full COCO and minitrain).
Currently supported export formats include ONNX and TensorRT. The framework also supports post-export validation to ensure stability and mAP consistency after deployment.
Would love to get your feedback and bug reports!
PyPI: pip install yololite
