README.md

metadata

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(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