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Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN accuracy.

Home Page: https://encord.com

License: Apache License 2.0

Python 1.18% Shell 0.01% Jupyter Notebook 98.82%
evaluation-framework evaluation-metrics model-evaluation-metrics text-to-image-evaluation embedding-evaluation embeddings-extraction knn-search linear-probing zero-shot-classification zero-shot-retrieval

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text-to-image-eval's Issues

Trouble adding `Qdrant/google-landmark-geo` Hugging Face geospatial dataset.

When adding the JSON definition of the Qdrant/google-landmark-geo Hugging Face dataset, got datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset.

JSON definition:

{
  "dataset_type": "HFDataset",
  "title": "geo-landmarks",
  "title_in_source": "Qdrant/google-landmark-geo"
}

Would worth investigating if the dataset is missing some customisation parameters in its definition or is ill-formed in Hugging Face.
It only has 5 downloads in the last month, so it could be broken or just very difficult to get ready.

Add support for multi-label classification datasets like `alkzar90/NIH-Chest-X-ray-dataset` from Hugging Face

When adding the JSON definition of the alkzar90/NIH-Chest-X-ray-dataset Hugging Face medical dataset, found that it's a multi-label dataset instead of a multiclass.

JSON definition:

{
  "dataset_type": "HFDataset",
  "title": "NIH-Chest-X-ray",
  "title_in_source": "alkzar90/NIH-Chest-X-ray-dataset",
  "name": "image-classification",
  "target_feature": "labels",
  "trust_remote_code": true
}

Would be great to consider adding support for multi-label datasets, where this dataset may serve as the starting test dataset.

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