https://github.com/aysenurcftc/FastAPI-Flower-Classification
A FastAPI app for classifying flower images using a pre-trained Vision Transformer (ViT) model, containerized with Docker for easy deployment.
https://github.com/aysenurcftc/FastAPI-Flower-Classification
classification docker fastapi pytorch
Last synced: 27 days ago
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A FastAPI app for classifying flower images using a pre-trained Vision Transformer (ViT) model, containerized with Docker for easy deployment.
- Host: GitHub
- URL: https://github.com/aysenurcftc/FastAPI-Flower-Classification
- Owner: aysenurcftc
- Created: 2025-01-01T13:52:11.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-11T19:38:24.000Z (9 months ago)
- Last Synced: 2025-07-14T06:33:53.772Z (3 months ago)
- Topics: classification, docker, fastapi, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 1.81 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# FastAPI Flower Classification
A FastAPI-based application that classifies flower images using a pre-trained Vision Transformer (ViT) model. The application provides an API for image classification and is containerized using Docker for easy deployment.
## Installation
```bash
git clone Flower-Classification
cd Flower-Classification
```
```bash
docker build -t flower-classification .
docker run -p 8000:8000 flower-classification
```
Access the application at http://localhost:8000/
## Run Locally
To run the application locally without Docker, you can use uvicorn:
```bash
pip install -r requirements.txt
```
```bash
uvicorn app:app --reload
```
## Acknowledgements
- Vision Transformer (ViT) for the pre-trained model.
- FastAPI: for building fast APIs.
- PyTorch: for providing the deep learning framework.
- Docker: for containerizing the application.