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YOLO ModelCompression MultidatasetTraining
| Date | Stars |
|---|---|
| 2026-07-24 | 442 |
| 2026-07-25 | 442 |
| 2026-07-28 | 442 |
| 2026-07-30 | 442 |
| 2026-08-06 | 442 |
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# YOLOv3-ModelCompression-MultidatasetTraining This project mainly include three parts. 1.Provides training methods for multiple mainstream object detection datasets(coco2017, coco2014, BDD100k, Visdrone, Hand) 2.Provides a mainstream model compression algorithm including pruning, quantization, and knowledge distillation. 3.Provides multiple backbone for yolov3 including Darknet-YOLOv3，Tiny-YOLOv3，Mobilenetv3-YOLOv3 Source using Pytorch implementation to [ultralytics/yolov3](https://github.com/ultralytics/yolov3) for yolov3 source code. Pruning method based on BN layer by [coldlarry/YOLOv3-complete-pruning](https://github.com/coldlarry/YOLOv3-complete-pruning), thanks to both of you. **If you can't download weights file and datasets from BaiDu, please send e-mail([email protected]) to me, I will rely as soon as I can.** # Update January 4, 2020. Provides download links and training methods to the Visdrone dataset. January 19, 2020. Dior, Bdd100k and Visdrone training will be provided, as well as the converted weights file. March 1, 2020. Provides Mobilenetv3 backbone. April 7, 2020. Implement two models based on Mobilenetv3: Yolov3-Mobilenet, and Yolov3tin-Mobilene-small, provide pre-training weights, extend the normal pruning methods to the two Mobilenet-based models. April 27, 2020. Update mobilenetv3 pre-training weights, add a layer pruning method, methods from the [tanluren/yolov3-channel-and-layer-pruning/yolov3](https://github.com/tanluren/yolov3-channel-and-layer-pruning), Thanks for sharing. May 22, 2020. Updated some new optimizations from [ultralytics/yolov3](https://github.com/ultralytics/yolov3), update cfg file and weights of YOLOv4. May 22, 2020. The 8-bit quantization method was updated and some bugs were fixed. July 12, 2020. The problem of mAP returning to 0 after pruning in yolov3-mobilenet was fixed. See issue#41 for more details. September 30, 2020. The BN_Fold training method was updated to reduce the precision loss caused by BN fusion, and the POW (2) quantization method targeted at FPGA was updated. See the quantization section for details. # Requirements Our project based on [ultralytics/yolov3](https://github.com/ultralytics/yolov3), see [ultralytics/yolov3](https://github.com/ultralytics/yolov3) for details. Here is a brief explanation: - `numpy` - `torch >= 1.1.0` - `opencv-python` - `tqdm` # Current support |<center>Function</center>|<center></center>| | --- |--- | |<center>Multi-Backbone training</center>|<center>√</center> | |<center>Multi-Datasets</center>|<center>√</center> | |<center>Pruning</center>|<center>√</center> | |<center>Quantization</center>|<center>√</center> | |<center>Knowledge Distillation</center>|<center>√</center> | # Training `python3 train.py --data ... --cfg ... `For training model command, the -pt command is required when using coco pre-training model. `python3 test.py --data ... --cfg ... ` For testing model command `python3 detect.py --data ... --cfg ... --source ...` For detecting model command, the default address of source is data/samples, the output result is saved in the /output, and the detection resource can be pictures and videos. # Multi-Datasets This project provides preprocessed datasets for the YOLOv3, configuration files (.cfg), dataset index files (.data), dataset category files (.names), and anchor box sizes (including 9 boxes for YOLOv3 and 6 boxes for tiny- YOLOv3) that are reclustered using the K-means algorithm. mAP |<center>Dataset</center>|<center>YOLOv3-640</center>|<center>YOLOv4-640</center>|<center>YOLOv3-mobilenet-640</center>| | --- |--- |--- |--- | |<center>Dior</center>|<center>0.749</center>| |<center>bdd100k</center>|<center>0.543</center>| |<center>visdrone</center>|<center>0.311</center>|<center>0.383</center>|<center>0.348</center>| Datasets, download and unzip to /data. - [COCO2017](https://pan.baidu.com/s/1KysFL6AmdbCBq4tHDebqlw) Extract code：hjln - [COCO2014](https://pan.baidu.com/s/1EoXOR77
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ee14f3febea5a359, topic:object-detection, topic:yolo, readme:object detection
matched fp:ee14f3febea5a359, topic:pruning, readme:quantization, readme:8-bit