File size: 9,949 Bytes
ffe3a9a 617c3e2 ffe3a9a 85a2c5e ffe3a9a 85a2c5e ffe3a9a 617c3e2 82be63b 617c3e2 ffe3a9a 85a2c5e ffe3a9a 85a2c5e ffe3a9a 7052adb ffe3a9a 85a2c5e ffe3a9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
---
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 [`microsoft/resnet-50` DINO checkpoint](https://huggingface.co/microsoft/resnet-50).
## 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.
<style>#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 {color: black;background-color: white;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 pre{padding: 0;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-toggleable {background-color: white;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-estimator:hover {background-color: #d4ebff;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-item {z-index: 1;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-parallel-item:only-child::after {width: 0;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-cdb8d112-cb44-4c1d-aaa0-00223c118110 div.sk-text-repr-fallback {display: none;}</style><div id="sk-cdb8d112-cb44-4c1d-aaa0-00223c118110" 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="8e021ebe-2e55-4711-8708-111032540cfe" type="checkbox" checked><label for="8e021ebe-2e55-4711-8708-111032540cfe" 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 | 0.996688 |
| f1 score | 0.996688 |
# 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('microsoft/resnet-50')
model = AutoModel.from_pretrained('microsoft/resnet-50').eval().to(device)
# load logistic regression
os.mkdir('emb-gam-resnet')
hub_utils.download(repo_id='Ramos-Ramos/emb-gam-resnet', dst='emb-gam-resnet')
with open('emb-gam-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
 |