metadata

title: Card Image Classifier Comparison
colorFrom: yellow
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit

Card Image Classifier Comparison

Kaggle page

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.

Plan:

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

Comparison of best models of each type

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