Releases: ppogg/YOLOv5-Lite
Releases · ppogg/YOLOv5-Lite
YOLOv5-Lite-v1.5
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!
YOLOv5-Lite-v1.4
- update export.py to extract v5lite onnx model with concat head. @ppogg
- add tensorrt inference sdk thanks for @ChaucerG
- add onnxruntime inference sdk,thanks for @hpc203
- add gcnet model , thanks for @315386775
- undate yolo.py @ChaucerG @Alexsdfdfs @315386775
- undate model.py @ppogg @Alexsdfdfs @315386775
Now YOLOv5-Lite support android, ncnn, mnn, tnn, onnxruntime, tensorrt, openvino, tflite. May be the repo will support more in the future~
Thanks for all the contributors of YOLOv5-Lite!
YOLOv5-Lite-v1.3
add openvino demo
add v5lite-c.pt
add v5lite-c IR model link
YOLOv5-Lite-v1.2
add v5lite-g.pt
add mnn demo
add model zoo link
YOLOv5-Lite-v1.1
- Remove some redundant code
- Add the example of Android development
- Release the first version of Android apk
- Add lighter baseline
- Add eval.py
- Update baseline
# 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
YOLOv5ss-v1.0
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~