Image Scoring/Regression

Image scoring is a form of supervised learning where a model is trained to predict a score or value for an image. AutoTrain simplifies the process, enabling you to train a state-of-the-art image scoring model by simply uploading labeled example images.

Preparing your data

To ensure your image scoring model trains effectively, follow these guidelines for preparing your data:

Organizing Images

Prepare a zip file containing your images and metadata.jsonl.

Archive.zip
├── 0001.png
├── 0002.png
├── 0003.png
├── .
├── .
├── .
└── metadata.jsonl

Example for metadata.jsonl:

{"file_name": "0001.png", "target": 0.5}
{"file_name": "0002.png", "target": 0.7}
{"file_name": "0003.png", "target": 0.3}

Please note that metadata.jsonl should contain the file_name and the target value for each image.

Image Requirements

• Format: Ensure all images are in JPEG, JPG, or PNG format.

• Quantity: Include at least 5 images to provide the model with sufficient examples for learning.

• Exclusivity: The zip file should exclusively contain images and metadata.jsonl. No additional files or nested folders should be included.

Some points to keep in mind:

• The images must be jpeg, jpg or png.
• There should be at least 5 images per class.
• There must not be any other files in the zip file.
• There must not be any other folders inside the zip folder.

When train.zip is decompressed, it creates no folders: only images and metadata.jsonl.