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| title: Custom Residual CNN Trained on CIFAR Dataset | |
| 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 | |
| # [Main REPO](https://github.com/deepanshudashora/ERAV1/tree/master/session12) | |
| # Problem Statement | |
| 1. Train CNN on cifar dataset with residual blocks | |
| 2. Target accuracy -> 90% on the test set | |
| 3. Use torch_lr_finder for finding LR | |
| 4. User OneCycleLR as Lr scheduler | |
| ## Features | |
| ### GradCAM Image Visualization | |
| Spaces App allows users to visualize GradCAM images generated from the neural network model. GradCAM provides insight into which regions of the input image influenced the model's predictions the most. Users can customize the visualization by specifying: | |
| * The User wants to visualize gradcam output or not | |
| * Opacity levels for better clarity. | |
| ### Misclassified Image Viewer | |
| With Spaces App, users can explore misclassified images by the neural network model. This feature helps identify cases where the model's predictions did not match the actual labels. Users can: | |
| * Choose if you want to check where model failed to predict correct class | |
| * Choose the number of misclassified images to view. | |
| ### Image Upload and Examples | |
| Spaces App allows users to upload their own images for analysis. Additionally, it provides ten example images to help users get started quickly and explore the app's capabilities. | |
| ### Top Classes Display | |
| Users can request the app to show the top predicted classes for an input image. They can specify the number of top classes to be displayed (limited to a maximum of 10), making it easy to focus on the most relevant results. | |
| ## How to Use Spaces App | |
| 1. **Setting GradCAM Preferences** | |
| * Upon launching the app, users will be prompted to choose whether they want to visualize GradCAM images. | |
| * Users can specify the number of GradCAM images to view, the target layer for visualization, and adjust opacity levels for better visualization. | |
| 2. **Misclassified Image Viewer** | |
| * If users are interested in exploring misclassified images, they can select the relevant option and specify the number of images they want to see. | |
| 3. **Uploading Images** | |
| * To analyze custom images, users can upload their own images through the app's image upload functionality. | |
| 4. **Example Images** | |
| * For users who want to quickly explore the app's features, ten example images are provided. | |
| 5. **Top Classes Display** | |
| * Users can choose to see the top predicted classes for an input image. They should specify the number of top classes (up to 10) they wish to view. | |
| # Model Parameters | |
| `````` | |
| ========================================================================================== | |
| Layer (type:depth-idx) Output Shape Param # | |
| ========================================================================================== | |
| CustomResnet [512, 10] -- | |
| ├─Sequential: 1-1 [512, 64, 32, 32] -- | |
| │ └─Conv2d: 2-1 [512, 64, 32, 32] 1,728 | |
| │ └─BatchNorm2d: 2-2 [512, 64, 32, 32] 128 | |
| │ └─ReLU: 2-3 [512, 64, 32, 32] -- | |
| ├─Sequential: 1-2 [512, 128, 16, 16] -- | |
| │ └─Conv2d: 2-4 [512, 128, 32, 32] 73,728 | |
| │ └─MaxPool2d: 2-5 [512, 128, 16, 16] -- | |
| │ └─BatchNorm2d: 2-6 [512, 128, 16, 16] 256 | |
| │ └─ReLU: 2-7 [512, 128, 16, 16] -- | |
| ├─Sequential: 1-3 [512, 128, 16, 16] -- | |
| │ └─Conv2d: 2-8 [512, 128, 16, 16] 147,456 | |
| │ └─BatchNorm2d: 2-9 [512, 128, 16, 16] 256 | |
| │ └─ReLU: 2-10 [512, 128, 16, 16] -- | |
| │ └─Conv2d: 2-11 [512, 128, 16, 16] 147,456 | |
| │ └─BatchNorm2d: 2-12 [512, 128, 16, 16] 256 | |
| │ └─ReLU: 2-13 [512, 128, 16, 16] -- | |
| ├─Sequential: 1-4 [512, 256, 8, 8] -- | |
| │ └─Conv2d: 2-14 [512, 256, 16, 16] 294,912 | |
| │ └─MaxPool2d: 2-15 [512, 256, 8, 8] -- | |
| │ └─BatchNorm2d: 2-16 [512, 256, 8, 8] 512 | |
| │ └─ReLU: 2-17 [512, 256, 8, 8] -- | |
| ├─Sequential: 1-5 [512, 512, 4, 4] -- | |
| │ └─Conv2d: 2-18 [512, 512, 8, 8] 1,179,648 | |
| │ └─MaxPool2d: 2-19 [512, 512, 4, 4] -- | |
| │ └─BatchNorm2d: 2-20 [512, 512, 4, 4] 1,024 | |
| │ └─ReLU: 2-21 [512, 512, 4, 4] -- | |
| ├─Sequential: 1-6 [512, 512, 4, 4] -- | |
| │ └─Conv2d: 2-22 [512, 512, 4, 4] 2,359,296 | |
| │ └─BatchNorm2d: 2-23 [512, 512, 4, 4] 1,024 | |
| │ └─ReLU: 2-24 [512, 512, 4, 4] -- | |
| │ └─Conv2d: 2-25 [512, 512, 4, 4] 2,359,296 | |
| │ └─BatchNorm2d: 2-26 [512, 512, 4, 4] 1,024 | |
| │ └─ReLU: 2-27 [512, 512, 4, 4] -- | |
| ├─MaxPool2d: 1-7 [512, 512, 1, 1] -- | |
| ├─Linear: 1-8 [512, 10] 5,130 | |
| ========================================================================================== | |
| Total params: 6,573,130 | |
| Trainable params: 6,573,130 | |
| Non-trainable params: 0 | |
| Total mult-adds (G): 194.18 | |
| ========================================================================================== | |
| Input size (MB): 6.29 | |
| Forward/backward pass size (MB): 2382.41 | |
| Params size (MB): 26.29 | |
| Estimated Total Size (MB): 2414.99 | |
| ========================================================================================== | |
| `````` | |
| # Accuracy Report | |
| |Model Experiments|Found Max LR|Min LR|Best Validation accuracy| Best Training Accuray | | |
| |--|--|--|--|--| | |
| |[Exp-1](https://github.com/deepanshudashora/ERAV1/blob/master/session10/experiments/S10_95_90.ipynb)|3.31E-02|0.023|90.91%|95.88%| | |
| |[Exp-2](https://github.com/deepanshudashora/ERAV1/blob/master/session10/experiments/S10_96_91.ipynb)|2.63E-02|0.02|91.32%|96.95%| | |
| |[Exp-3](https://github.com/deepanshudashora/ERAV1/blob/master/session10/experiments/S10_98_91.ipynb)|1.19E-02|0.01|91.72%|98.77%| | |
| |[Exp-4-TorchCode](https://github.com/deepanshudashora/ERAV1/blob/master/session10/S10.ipynb)|1.87E-02|0.01|91.80%|96.93%| | |
| |[Exp-5-Lightning-precision-16](https://github.com/deepanshudashora/ERAV1/blob/master/session12/S12_Training.ipynb)|1.87E-02|0.01|92.20%|98.8%| | | |
| # [Training Logs](https://github.com/deepanshudashora/ERAV1/blob/master/session12/csv_logs_training/lightning_logs/version_0/metrics.csv) | |
| ``` | |
| lr-Adam step train_loss train_acc epoch val_loss val_acc | |
| 88 0.005545 1799 NaN NaN NaN NaN NaN | |
| 89 NaN 1799 0.220482 0.914062 18.0 NaN NaN | |
| 90 0.005043 1849 NaN NaN NaN NaN NaN | |
| 91 NaN 1849 0.235307 0.910156 18.0 NaN NaN | |
| 92 NaN 1861 NaN NaN 18.0 0.406253 0.8724 | |
| 93 0.004541 1899 NaN NaN NaN NaN NaN | |
| 94 NaN 1899 0.197022 0.925781 19.0 NaN NaN | |
| 95 0.004039 1949 NaN NaN NaN NaN NaN | |
| 96 NaN 1949 0.224633 0.933594 19.0 NaN NaN | |
| 97 NaN 1959 NaN NaN 19.0 0.367574 0.8873 | |
| 98 0.003537 1999 NaN NaN NaN NaN NaN | |
| 99 NaN 1999 0.175551 0.921875 20.0 NaN NaN | |
| 100 0.003035 2049 NaN NaN NaN NaN NaN | |
| 101 NaN 2049 0.148070 0.955078 20.0 NaN NaN | |
| 102 NaN 2057 NaN NaN 20.0 0.345555 0.8963 | |
| 103 0.002532 2099 NaN NaN NaN NaN NaN | |
| 104 NaN 2099 0.139945 0.955078 21.0 NaN NaN | |
| 105 0.002030 2149 NaN NaN NaN NaN NaN | |
| 106 NaN 2149 0.112343 0.960938 21.0 NaN NaN | |
| 107 NaN 2155 NaN NaN 21.0 0.311762 0.9046 | |
| 108 0.001528 2199 NaN NaN NaN NaN NaN | |
| 109 NaN 2199 0.079441 0.972656 22.0 NaN NaN | |
| 110 0.001026 2249 NaN NaN NaN NaN NaN | |
| 111 NaN 2249 0.084935 0.962891 22.0 NaN NaN | |
| 112 NaN 2253 NaN NaN 22.0 0.282218 0.9190 | |
| 113 0.000524 2299 NaN NaN NaN NaN NaN | |
| 114 NaN 2299 0.074329 0.968750 23.0 NaN NaN | |
| 115 0.000022 2349 NaN NaN NaN NaN NaN | |
| 116 NaN 2349 0.043582 0.988281 23.0 NaN NaN | |
| 117 NaN 2351 NaN NaN 23.0 0.268215 0.9219 | |
| ``` | |
| # Results | |
| ## Accuracy Plot | |
| Here is the Accuracy and Loss metric plot for the model | |
|  | |
| ## Accuracy Report for Each class | |
| Accuracy of airplane : 82 % | |
| Accuracy of automobile : 100 % | |
| Accuracy of bird : 94 % | |
| Accuracy of cat : 75 % | |
| Accuracy of deer : 81 % | |
| Accuracy of dog : 82 % | |
| Accuracy of frog : 96 % | |
| Accuracy of horse : 100 % | |
| Accuracy of ship : 76 % | |
| Accuracy of truck : 82 % | |
|  | |