| title | colorFrom | colorTo | sdk | app_file | pinned | license |
|---|---|---|---|---|---|---|
Card Image Classifier Comparison |
yellow |
blue |
gradio |
app.py |
false |
mit |
Data: 53 classes 7624 train, 265 test, 265 validation images 224 X 224 X 3. The train, test and validation directories are partitioned into 53 sub directories, one for each of the 53 types of cards. The dataset also includes a csv file which can be used to load the datasets.
- Fine-tune a pretrained image classification model
- Compare a few small efficient ones
- Use old PyTorch utility functions
- If enough time, redo in Lightning and write utility functions for it
- Extract outputs from the feature extraction layers and try completing the task with gradient boosting
- Compare results
The following shows the training and performance benefits of gradient boosting over NN classification layers. Compare these models in their huggingface space (or clone the repo and python app.py).
| Model | Retrained Portion | Epochs | Time | test_loss | test_F1 |
|---|---|---|---|---|---|
| RexNet 1.0 | Full Retrain | val_loss early stop at 5 (equalling 2) | 16:32 | 0.0996 | 1 |
| RexNet 1.5 features -> LightGBM | No retraining; feature extraction -> GB | 100 bagging gbdt iterations (OpenCL, not CUDA) | 3:37 | 0.5433 | |
| RexNet 1.5 | Classification | 10 | 8:00 | 1.5839 | 0.4884 |