--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification - visual emb-gam --- # Model description This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset. This forms the GAM of an [Emb-GAM](https://arxiv.org/abs/2209.11799) extended to images. Patch embeddings are meant to be extracted with the [`facebook/dino-vitb16` DINO checkpoint](https://huggingface.co/facebook/dino-vitb16). ## Intended uses & limitations This model is not intended to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.

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| Hyperparameter | Value | |-------------------|-----------------------------------------------------------| | Cs | 10 | | class_weight | | | cv | StratifiedKFold(n_splits=5, random_state=1, shuffle=True) | | dual | False | | fit_intercept | True | | intercept_scaling | 1.0 | | l1_ratios | | | max_iter | 100 | | multi_class | auto | | n_jobs | | | penalty | l2 | | random_state | 1 | | refit | False | | scoring | | | solver | lbfgs | | tol | 0.0001 | | verbose | 0 |

### Model Plot The model plot is below.

LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)

Please rerun this cell to show the HTML repr or trust the notebook.

## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| | accuracy | 0.97707 | | f1 score | 0.97707 | # How to Get Started with the Model Use the code below to get started with the model.

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```python from PIL import Image from skops import hub_utils import torch from transformers import AutoFeatureExtractor, AutoModel import pickle import os # load embedding model device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16') model = AutoModel.from_pretrained('facebook/dino-vitb16').eval().to(device) # load logistic regression os.mkdir('emb-gam-dino') hub_utils.download(repo_id='Ramos-Ramos/emb-gam-dino', dst='emb-gam-dino') with open('emb-gam-dino/model.pkl', 'rb') as file: logistic_regression = pickle.load(file) # load image img = Image.open('examples/english_springer.png') # preprocess image inputs = {k: v.to(device) for k, v in feature_extractor(img, return_tensors='pt').items()} # extract patch embeddings with torch.no_grad(): patch_embeddings = model(**inputs).last_hidden_state[0, 1:].cpu() # classify pred = logistic_regression.predict(patch_embeddings.sum(dim=0, keepdim=True)) # get patch contributions patch_contributions = logistic_regression.coef_ @ patch_embeddings.T.numpy() ```

# Model Card Authors This model card is written by following authors: Patrick Ramos # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation **BibTeX:** ``` @article{singh2022emb, title={Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models}, author={Singh, Chandan and Gao, Jianfeng}, journal={arXiv preprint arXiv:2209.11799}, year={2022} } ``` # Additional Content ## confusion_matrix ![confusion_matrix](confusion_matrix.png) # Demo Check out our HuggingFace Space [here](https://huggingface.co/spaces/Ramos-Ramos/emb-gam-dino)! It does Imagenette classification and visualizes patch contributions per label.