https://github.com/coderooz/image_classification_transfer_learning
This project involves using transfer learning to classify images into categories such as cats vs. dogs by leveraging a pre-trained model like VGG16 or ResNet. Transfer learning allows you to adapt a pre-trained model to your specific problem, making the training process faster and often more effective.
https://github.com/coderooz/image_classification_transfer_learning
colab image-classification matplot neural-network numpy python tensorflow vgg16-model
Last synced: 10 months ago
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This project involves using transfer learning to classify images into categories such as cats vs. dogs by leveraging a pre-trained model like VGG16 or ResNet. Transfer learning allows you to adapt a pre-trained model to your specific problem, making the training process faster and often more effective.
- Host: GitHub
- URL: https://github.com/coderooz/image_classification_transfer_learning
- Owner: coderooz
- License: mit
- Created: 2024-07-24T15:00:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-24T15:17:11.000Z (over 1 year ago)
- Last Synced: 2025-02-04T14:36:08.797Z (about 1 year ago)
- Topics: colab, image-classification, matplot, neural-network, numpy, python, tensorflow, vgg16-model
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Classification with Transfer Learning
This project utilizes transfer learning to classify images into categories using a pre-trained VGG16 model. Transfer learning allows us to leverage the features learned by the VGG16 model on the ImageNet dataset to improve classification performance on a new dataset.
## Project Structure
- `data/`: Contains scripts for loading and preprocessing image data.
- `model/`: Contains the transfer learning model definition.
- `scripts/`: Contains scripts for training and evaluating the model.
- `requirements.txt`: Lists the required Python packages.
## Getting Started
[**Open Colab file**](https://colab.research.google.com/drive/1MQQoXCuGdUlukvAmHI5p_1ylPL3F4PEb?usp=sharing)
**OR**
1. **Clone the repository:**
```bash
git clone https://github.com/coderooz/image_classification_transfer_learning.git
cd image_classification_transfer_learning
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Prepare your dataset:**
Place your image data in `data/train/` and `data/validation/` directories, with subdirectories for each class.
4. **Train the model:**
```bash
python scripts/train_model.py
```
5. **Evaluate the model:**
```bash
python scripts/evaluate_model.py
```
## Results
The model's accuracy on the validation set will be printed after evaluation.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgements
- The VGG16 model is provided by [TensorFlow](https://www.tensorflow.org/).
## Contact
- Ranit Saha - [Coderooz](https://github.com/coderooz)