Updated README.md
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by hvt4 - opened
README.md
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VGG11 model pre-trained on ImageNet-1k. Originally introduced by Karen Simonyan and Andrew Zisserman in the influential paper, [**Very Deep Convolutional Networks for Large-Scale Image Recognition**](https://arxiv.org/abs/1409.1556) this configuration serves as the base 8-layer convolutional architecture (plus 3 fully connected layers) that proved the effectiveness of using small $3 \times 3$ filters to build deep networks while maintaining a manageable number of parameters.
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## Intended uses & limitations
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The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
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## Model description
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acc@5 (on ImageNet-1K): 88.628%
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num_params: 132863336
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## Use
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```python
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#!/usr/bin/env python3
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import argparse, json
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if __name__ == "__main__":
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main()
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```
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### BibTeX entry and citation info
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```bibtex
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VGG11 model pre-trained on ImageNet-1k. Originally introduced by Karen Simonyan and Andrew Zisserman in the influential paper, [**Very Deep Convolutional Networks for Large-Scale Image Recognition**](https://arxiv.org/abs/1409.1556) this configuration serves as the base 8-layer convolutional architecture (plus 3 fully connected layers) that proved the effectiveness of using small $3 \times 3$ filters to build deep networks while maintaining a manageable number of parameters.
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## Model description
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acc@5 (on ImageNet-1K): 88.628%
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num_params: 132863336
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## Intended uses & limitations
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The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
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## How to Use
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**1. Install Dependencies** Ensure your Python environment is set up with the required libraries. Run the following command in your terminal:
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```bash
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pip install numpy Pillow huggingface_hub ai-edge-litert
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```
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**2. Prepare Your Image** The script expects an image file to analyze. Make sure you have an image (e.g., cat.jpg or car.png) saved in the same working directory as your script.
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**3. Save the Script** Create a new file named `classify.py`, paste the script below into it, and save the file:
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```python
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#!/usr/bin/env python3
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import argparse, json
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if __name__ == "__main__":
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main()
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```
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**4. Execute the Python Script** Run the below command:
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```bash
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python classify.py --image cat.jpg
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```
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### BibTeX entry and citation info
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```bibtex
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