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/dino-resnet-50 DINO checkpoint.

Intended uses & limitations

This model is not intended to be used in production.

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

<details> <summary> Click to expand </summary>

</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-28d2ddd8-c7c0-4979-9746-85c6791d4321 div.sk-text-repr-fallback {display: none;}</style><div id="sk-28d2ddd8-c7c0-4979-9746-85c6791d4321" class="sk-top-container"><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="784a44e8-df9f-4d85-9361-94e73c8cc8fc" type="checkbox" checked><label for="784a44e8-df9f-4d85-9361-94e73c8cc8fc" 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.

How to Get Started with the Model

Use the code below to get started with the model.

<details> <summary> Click to expand </summary>

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/dino-resnet-50")
model = AutoModel.from_pretrained("Ramos-Ramos/dino-resnet-50").eval().to(device)

# load logistic regression
os.mkdir("emb-gam-dino-resnet")
hub_utils.download(repo_id="Ramos-Ramos/emb-gam-dino-resnet", dst="emb-gam-dino-resnet")

with open("emb-gam-dino-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()

</details>

Model Card Authors

This model card is written by following authors:

Patrick Ramos and Ryan Ramos

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:

@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