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| title: YoloV3 on PASCAL VOC Dataset From Scratch (Slide for GradCam output) | |
| emoji: 🚀 | |
| colorFrom: gray | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 3.39.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # [GithubREPO](https://github.com/deepanshudashora/ERAV1/tree/master/session13) | |
| # Training Procedure | |
| #### [Experiment 1](https://github.com/deepanshudashora/ERAV1/tree/master/session13/lightning_version/Experiments) | |
| 1. The model is trained on Tesla T4 (15GB GPU memory) | |
| 2. The training is completed in two phases | |
| 3. The first phase contains 20 epochs and the second phase contains another 20 epochs | |
| 4. In the first training we see loss dropping correctly but in the second training it drops less | |
| 5. We run our two training loops separately and do not run any kind of validation on them, except for validation loss | |
| #### [Experiment 2](https://github.com/deepanshudashora/ERAV1/tree/master/session13/lightning_version) | |
| 1. The model is trained on 2 Tesla t4 GPUs, with distributed training using PyTorch lightning | |
| 2. For doing the distributed training we use the strategy ```ddp_notebook_find_unused_parameters_true``` | |
| * Later we evaluate the model and get the numbers | |
| * The lightning generally saves the model as .ckpt format, so we convert it to torch format by saving state dict as .pt format | |
| * For doing this we use these two lines of code | |
| ``` | |
| best_model = torch.load(weights_path) | |
| torch.save(best_model['state_dict'], f'best_model.pth') | |
| litemodel = YOLOv3(num_classes=num_classes) | |
| litemodel.load_state_dict(torch.load("best_model.pth",map_location='cpu')) | |
| device = "cpu" | |
| torch.save(litemodel.state_dict(), PATH) | |
| ``` | |
| * The model starts overfitting on the dataset after 30 epochs | |
| * Future Improvements | |
| 1. Train the model in 1 shot instead of two different phases | |
| 2. Keep a better batch size (Basically earn more money and buy a good GPU) | |
| 3. Data transformation also plays a vital role here | |
| 4. OneCycle LR range needs to be appropriately modified for a better LR | |
| # Data Transformation | |
| Along with the transforms mentioned in the [config file](https://github.com/deepanshudashora/ERAV1/blob/master/session13/lightning_version/config.py), we also apply **mosaic transform** on 75% images | |
| [Reference](https://www.kaggle.com/code/nvnnghia/awesome-augmentation/notebook) | |
| # Accuracy Report | |
| ``` | |
| Class accuracy is: 85.015236% | |
| No obj accuracy is: 98.522491% | |
| Obj accuracy is: 65.760597% | |
| MAP: 0.4661380648612976 | |
| ``` | |
| # [Training Logs](https://github.com/deepanshudashora/ERAV1/blob/master/session13/lightning_version/training_logs/csv_training_logs/lightning_logs/version_0/metrics.csv) | |
| #### For faster execution we run the validation step after 20 epochs for the first 20 epochs of training and after that after every 5 epochs till 40 epochs | |
| ``` | |
| lr-Adam step train_loss epoch val_loss | |
| 786 NaN 19499 4.653981 37.0 NaN | |
| 787 0.000160 19549 NaN NaN NaN | |
| 788 NaN 19549 4.864988 37.0 NaN | |
| 789 0.000160 19599 NaN NaN NaN | |
| 790 NaN 19599 5.241925 37.0 NaN | |
| 791 0.000160 19649 NaN NaN NaN | |
| 792 NaN 19649 5.020171 37.0 NaN | |
| 793 0.000161 19699 NaN NaN NaN | |
| 794 NaN 19699 4.245292 38.0 NaN | |
| 795 0.000161 19749 NaN NaN NaN | |
| 796 NaN 19749 4.541957 38.0 NaN | |
| 797 0.000161 19799 NaN NaN NaN | |
| 798 NaN 19799 3.837740 38.0 NaN | |
| 799 0.000161 19849 NaN NaN NaN | |
| 800 NaN 19849 4.239679 38.0 NaN | |
| 801 0.000161 19899 NaN NaN NaN | |
| 802 NaN 19899 4.034101 38.0 NaN | |
| 803 0.000161 19949 NaN NaN NaN | |
| 804 NaN 19949 5.010788 38.0 NaN | |
| 805 0.000161 19999 NaN NaN NaN | |
| 806 NaN 19999 3.980245 38.0 NaN | |
| 807 0.000161 20049 NaN NaN NaN | |
| 808 NaN 20049 4.641729 38.0 NaN | |
| 809 0.000161 20099 NaN NaN NaN | |
| 810 NaN 20099 4.563717 38.0 NaN | |
| 811 0.000161 20149 NaN NaN NaN | |
| 812 NaN 20149 4.422552 38.0 NaN | |
| 813 0.000161 20199 NaN NaN NaN | |
| 814 NaN 20199 4.925357 38.0 NaN | |
| 815 0.000161 20249 NaN NaN NaN | |
| 816 NaN 20249 4.788391 39.0 NaN | |
| 817 0.000161 20299 NaN NaN NaN | |
| 818 NaN 20299 4.478580 39.0 NaN | |
| 819 0.000161 20349 NaN NaN NaN | |
| 820 NaN 20349 4.624731 39.0 NaN | |
| 821 0.000161 20399 NaN NaN NaN | |
| 822 NaN 20399 4.425498 39.0 NaN | |
| 823 0.000161 20449 NaN NaN NaN | |
| 824 NaN 20449 4.361921 39.0 NaN | |
| 825 0.000161 20499 NaN NaN NaN | |
| 826 NaN 20499 4.318252 39.0 NaN | |
| 827 0.000161 20549 NaN NaN NaN | |
| 828 NaN 20549 4.013813 39.0 NaN | |
| 829 0.000161 20599 NaN NaN NaN | |
| 830 NaN 20599 4.476331 39.0 NaN | |
| 831 0.000161 20649 NaN NaN NaN | |
| 832 NaN 20649 4.192605 39.0 NaN | |
| 833 0.000161 20699 NaN NaN NaN | |
| 834 NaN 20699 4.065756 39.0 NaN | |
| 835 NaN 20719 NaN 39.0 4.348697 | |
| ``` | |