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
βββ 0001.png
βββ 0002.png
βββ 0003.png
βββ .
βββ .
βββ .
βββ 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.
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