Model description
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Intended uses & limitations
[More Information Needed]
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
<details> <summary> Click to expand </summary>
| 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 |
</details>
Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-3244a8f9-b723-4dac-a610-2fbd0449e147 div.sk-text-repr-fallback {display: none;}</style><div id="sk-3244a8f9-b723-4dac-a610-2fbd0449e147" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3fe57819-ef08-46de-a235-f0f501b88302" type="checkbox" checked><label for="3fe57819-ef08-46de-a235-f0f501b88302" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegressionCV</label><div class="sk-toggleable__content"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre></div></div></div></div></div>
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
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Model Card Authors
This model card is written by following authors:
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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 extended to images. Patch embeddings are meant to be extracted with the Ramos-Ramos/vicreg-resnet-50 ResNet checkpoint.
eval_method
The model is evaluated using test split, on accuracy and F1 score with macro average.
confusion_matrix
