--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_file: model.pkl --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | 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 | 0.982166 | | f1 score | 0.982166 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [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} } # get_started_code 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('Ramos-Ramos/vicreg-resnet-50') model = AutoModel.from_pretrained('Ramos-Ramos/vicreg-resnet-50').eval().to(device) # load logistic regression os.mkdir('emb-gam-vicreg-resnet') hub_utils.download(repo_id='Ramos-Ramos/emb-gam-vicreg-resnet', dst='emb-gam-vicreg-resnet') with open('emb-gam-vicreg-resnet/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].permute(1, 2, 0).view(7*7, 2048).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 Patrick Ramos and Ryan Ramos # limitations This model is not intended to be used in production. # 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 [`Ramos-Ramos/vicreg-resnet-50` ResNet checkpoint](https://huggingface.co/Ramos-Ramos/vicreg-resnet-50). # eval_method The model is evaluated using test split, on accuracy and F1 score with macro average. # confusion_matrix ![confusion_matrix](confusion_matrix.png)