This project is inspired by Stronger-Yolo. I reimplemented with Pytorch and continue improving yolov3 with latest papers.
This project will also try out some model-compression approaches(e.g. channel-pruning).
python3.6, pytorch1.2(1.0+ should be ok), ubuntu14/16/18 tested.
1 . run the following command to start training, see yacs for more instructions.
python main.py --config-file configs/voc.yaml EXPER.experiment_name voc_512 devices 0,
2 . run the following command to test
python main.py --config-file configs/voc.yaml EXPER.resume best do_test True EXPER.experiment_name voc_512 devices 0,1,
| model | mAP50 | mAP75 | configs |
|---|---|---|---|
| baseline(with GIOU) | 0.765 | 0.391 | voc.yaml |
| + focal loss | 0.772 | 0.438 | voc_clsfocal.yaml |
| + kl loss | 0.778 | 0.449 | voc_kl_clsfocal.yaml |
| + var vote | 0.781 | 0.464 | voc_kl_clsfocal.yaml |
Note:
1.Set EVAL.varvote=True to enable varvote in KL-loss.
1 . training with sparse regularization
python main.py --config-file configs/voc.yaml EXPER.experiment_name voc_512_sparse Prune.sparse True Prune.sr 0.01
2 . Pruning and Finetune, check MobileV2 Pruning for a simplified example.
python main_prune.py --config-file configs/voc_prune.yaml EXPER.experiment_name voc_512_sparse Prune.sparse True Prune.pruneratio 0.3
Check MNN-yolov3.
| Model | MAP | Flops(G) | Params(M) |
|---|---|---|---|
| Yolov3 | 0.765 | 4.33 | 6.775 |
| Yolov3-sparsed | 0.750 | 4.33 | 6.775 |
| Yolov3-Pruned(35% pruned) | 0.746 | 3.00 | 2.815 |
Note:
1.All experiments are trained for 60 epochs.
2.All experiments tested with threshold 0.1 in 512 resolution.