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Model description

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Intended uses & limitations

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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 0.982166
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

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Citation

Below you can find information related to citation.

BibTeX:

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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(77, 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

confusion_matrix

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