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--- |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- AiresPucrs/CelebA-Smiles |
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metrics: |
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- accuracy |
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tags: |
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- image-classification |
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--- |
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# LeNNon-Smile-Detector |
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## Model Overview |
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The LeNNon-Smile-Detector is a convolutional neural network trained to detect the presence of a human smile in a facial image. |
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### Details |
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- **Size:** 2,566,506 parameters |
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- **Model type:** Convolutional neural network |
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- **Optimizer**: `torch.optim.Adam` with a learning rate of 0.001 |
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- **Number of Epochs:** 20 |
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- **Batch Size:** 256 |
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- **Hardware:** Tesla V4 |
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- **Emissions:** Not measured |
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- **Total Energy Consumption:** Not measured |
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### How to Use |
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To run inference on this model, you can use the following code snippet: |
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```python |
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import torch |
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from PIL import Image |
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from lennon import LeNNon |
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from torchvision import transforms |
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from huggingface_hub import hf_hub_download |
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# Download the pytorch model |
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hf_hub_download(repo_id="AiresPucrs/LeNNon-Smile-Detector", |
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filename="LeNNon-Smile-Detector.pt", |
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local_dir="./", |
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repo_type="model" |
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) |
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# Download the source implementation of the model's architecture |
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hf_hub_download(repo_id="AiresPucrs/LeNNon-Smile-Detector", |
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filename="lennon.py", |
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local_dir="./", |
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repo_type="model" |
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) |
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# Check if GPU is available |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load the model an pass it to the proper device |
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model = torch.load('./LeNNon-Smile-Detector.pt') |
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model = model.to(device) |
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model.eval() |
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# This `transform` object will transform our test images into proper tensors |
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transform = transforms.Compose([ |
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transforms.Resize((100, 100)), # Resize the image to 100x100 |
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transforms.ToTensor(), |
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]) |
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image_path = "your_image_path_here" |
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# Open and preprocess he image |
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image = Image.open(image_path) |
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tensor = transform(image) |
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tensor = tensor.to(device) |
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# forward pass trough the model |
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with torch.no_grad(): |
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outputs = model(tensor) |
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# Get the class prediction |
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_, predicted = torch.max(outputs.data, 1) |
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print("Smiling" if predicted.item() > 0 else "Not Smiling") |
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``` |
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## Intended Use |
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This model was created for research purposes only. Specifically, it was designed to explore discriminatory biases present in a subset of the CelebA dataset (the [CelebA-Smiles](https://huggingface.co/datasets/AiresPucrs/CelebA-Smiles)). We do not recommend any application of this model outside this scope. |
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## Performance Metrics |
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The model achieved an accuracy of 97% in a test set split containing 5000 images from the [CelebA-Smiles](https://huggingface.co/datasets/AiresPucrs/CelebA-Smiles). |
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## Training Data |
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The model was trained on a subset of the [CelebA dataset](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset), i.e., CelebA-Smiles. |
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CelebA-Smiles contains 50K images. 25K of smiling faces, and 25K of non smiling faces. |
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Images on the CelebA-Smiles were resized to 100 x 100 pixels before training the model. For more details on the features contained in the CelebA-Smiles dataset, check its [dataset card](https://huggingface.co/datasets/AiresPucrs/CelebA-Smiles). |
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## Limitations |
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We performed a simple fairness analysis of our model regarding the sensitive attribute **age**. |
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According to our analysis, the model has no discriminatory bias regarding this feature. Results can be found [here](https://github.com/Nkluge-correa/teeny-tiny_castle/blob/master/ML%20Fairness/fair_metrics_celeba.ipynb). |
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Other possible biases were not investigated, but further investigations are likely given that other sensitive attributes present in the training dataset are available. |
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In conclusion, we do not recommend using this model in real-world applications. It was solely developed for academic and educational purposes. |
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## Cite as |
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```latex |
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@misc{teenytinycastle, |
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doi = {10.5281/zenodo.7112065}, |
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url = {https://github.com/Nkluge-correa/teeny-tiny_castle}, |
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author = {Nicholas Kluge Corr{\^e}a}, |
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title = {Teeny-Tiny Castle}, |
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year = {2024}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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} |
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``` |
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## License |
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This model is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details. |
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