metadata

title: FastAPI Image Classification with VGG16
emoji: 🔍
colorFrom: indigo
colorTo: purple
sdk: docker
python_version: '3.9'
sdk_version: '1.0'
suggested_hardware: cpu-upgrade
suggested_storage: medium
app_file: your_script_name.py
app_port: 8000
base_path: /
fullWidth: true
header: default
short_description: Classify images using VGG16 in FastAPI.
tags:
  - image-classification
  - VGG16
  - FastAPI
  - Docker
  - deep-learning
thumbnail: http://example.com/path_to_custom_thumbnail.jpg
pinned: false
hf_oauth: false
disable_embedding: false
startup_duration_timeout: 30m
custom_headers:
  cross-origin-embedder-policy: require-corp
  cross-origin-opener-policy: same-origin
  cross-origin-resource-policy: cross-origin
license: mit

FastAPI Image Classification with VGG16

Description

This Space uses a FastAPI application to classify images via a pre-trained VGG16 model. The application is containerized using Docker for easy deployment and scalability. Users can upload an image, and the model will classify it into one of two categories.

How to Use

Start the Docker Container:

Build the Docker image and run it using:

docker build -t image-classification .
docker run -p 8000:8000 image-classification

Accessing the API:
- Open your browser and go to http://localhost:8000/docs.
- Use the /predict/ endpoint to submit an image file and receive the classification result.

Requirements

Docker
Python 3.9

Installation

Clone the repository:

git clone <repository-url>
cd <repository-directory>