🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
GPL-3.0 License
Bot releases are visible (Hide)
update export.py to extract v5lite onnx model with mnne&mnnd (for mnn infer) head. @ppogg
update export.py to extract v5lite onnx model with end2end (for onnxruntime infer) head. @ppogg
repair export.py to extract v5lite onnx model with concat head. @ppogg
update the mnn sdk infer https://zhuanlan.zhihu.com/p/672633849
update the onnxruntime sdk infer (with end2end decode)
Thanks for all the contributors and user of YOLOv5-Lite!
Published by ppogg over 2 years ago
Published by ppogg about 3 years ago
add openvino demo
add v5lite-c.pt
add v5lite-c IR model link
Published by ppogg about 3 years ago
add v5lite-g.pt
add mnn demo
add model zoo link
Published by ppogg about 3 years ago
# evaluate in 320×320:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.362
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.206
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.049
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.197
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.373
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.216
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.339
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.597
# evaluate in 416×416:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.244
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.413
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.246
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.244
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.238
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.380
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.412
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626
# evaluate in 640×640:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.271
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.457
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.274
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.297
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.364
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.254
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.422
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.460
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.272
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.616
Published by ppogg about 3 years ago
About
shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~