https://github.com/vishnun0027/crop_disease_classification
Image classification is the process of assigning a label or class to an image based on its visual content. Fine-tuning a Vision Transformer (ViT) on the Crop_Disease dataset involves adapting a pre-trained ViT model specifically for the task of identifying crop diseases in images. This process includes training the model on the Crop_Disease dataset
https://github.com/vishnun0027/crop_disease_classification
computer-vision deep-learning image-classification pillow python pytorch streamlit vision-transformer
Last synced: 4 months ago
JSON representation
Image classification is the process of assigning a label or class to an image based on its visual content. Fine-tuning a Vision Transformer (ViT) on the Crop_Disease dataset involves adapting a pre-trained ViT model specifically for the task of identifying crop diseases in images. This process includes training the model on the Crop_Disease dataset
- Host: GitHub
- URL: https://github.com/vishnun0027/crop_disease_classification
- Owner: vishnun0027
- Created: 2024-06-25T06:06:49.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-06-25T06:43:26.000Z (about 2 years ago)
- Last Synced: 2025-06-04T21:45:14.721Z (about 1 year ago)
- Topics: computer-vision, deep-learning, image-classification, pillow, python, pytorch, streamlit, vision-transformer
- Language: Jupyter Notebook
- Homepage:
- Size: 403 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Crop Disease Identification
### Image classification
Image classification assigns a label or class to an image. Fine-tune ViT on the [Crop_Disease](https://huggingface.co/datasets/vishnun0027/Crop_Disease) dataset to classify a Crop Disease in an image
## Screenshots
Prediction
## Setup
1. Clone this repository:
```sh
git clone https://github.com/vishnun0027/crop_disease_classification.git
```
2. Install the required dependencies:
```sh
pip install -r requirements.txt
```
3. Run the Streamlit app:
```sh
streamlit run main.py
```
### License
This project is licensed under the MIT License. See the LICENSE file for details.