Image Classification
docker pull cargoshipsh/image-classification
Automatically classifies an image into one of the 1,000 ImageNet categories. This Vision Transformer (ViT) model by Google was pre-trained on the ImageNet dataset with 1,000 different image classes. On a moderate CPU it takes only a few seconds to classify an image.
Demo
Image
Drag and Drop or click
to upload your own image
Predicted Class
seashore, coast, seacoast, sea-coast
This demo runs on a virtual server with 4 vCPUs and 16 GB Ram (~$20/month)
License
The model is licensed under the Apache 2.0 License and the code for the API wrapper is licensed under MIT License.
System Requirements
Minimum: 2GB RAM, 2 vCPU
Recommended: 4GB RAM, 4 vCPU
API
If you don't want to implement the model all by yourself, no worries. Benefit from our easy to use API and get started right away!
Get StartedUsage
Input [POST]
The input expects and image. This can be a URL or a base64 encoded image.
Option 1: URL
{
"imageUrl": "https://images.unsplash.com/photo-1677496891133-f81cc7a4e56e?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=600&q=80"
}
Option 2: Base64
{
"base64": "/9j/4AAQSkZJRgABAQEASABIAAD/2wBD..."
}
Output
The output is one of the 1,000 ImageNet classes
{
"prediction": "seashore, coast, seacoast, sea-coast"
}
You need to set an API Key via the environment variable API_KEY to run the image and set the X-API-KEY header in your request with the same KEY.
Need a more detailed setup guide?
To get more detailed instructions how to get started please check out our quick start guide in the docs.
Example
Make sure you have Docker installed then run the following command:
docker run -p 80:80 --env API_KEY=CHANGE_ME cargoshipsh/image-classification
In a new terminal window, run the following command to call the API
curl -X POST -H 'Content-type: application/json' -H 'X-API-Key: CHANGE_ME' --data '{"imageUrl": "https://images.unsplash.com/photo-1677496891133-f81cc7a4e56e?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=600&q=80"}' http://localhost:80
You see the output of the model in the terminal.
{"prediction": "seashore, coast, seacoast, sea-coast"}