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---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
- visual emb-gam
---
# 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 [`google/vit-base-patch16-224` ViT checkpoint](https://huggingface.co/google/vit-base-patch16-224).
## 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>
| 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-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-text-repr-fallback {display: none;}</style><div id="sk-57980b4f-6828-4a54-ae50-b50e1f9f097e" 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="51ec5e46-9aaa-4487-adda-6718142c9f85" type="checkbox" checked><label for="51ec5e46-9aaa-4487-adda-6718142c9f85" 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 | 0.99465 |
| f1 score | 0.99465 |
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
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("google/vit-base-patch16-224")
model = AutoModel.from_pretrained("google/vit-base-patch16-224").eval().to(device)
# load logistic regression
os.mkdir("emb-gam-vit")
hub_utils.download(repo_id="Ramos-Ramos/emb-gam-vit", dst="emb-gam-vit")
with open("emb-gam-vit/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, 1:].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
 |