_transform_ndarray vs _transform_blob

[mrjackbo]

Hey, thanks for the great project!

I noticed that you always use _transform_ndarray when encoding an image, while _transform_blob seems to be more in line with the original code (e.g. here and here).

Unfortunately they give quite different results as explained in the warning here: _transform_blob applies Resize to a PIL Image, hence uses anti-aliasing, while _transform_ndarray applies Resize to an ndarray, and does not use anti-aliasing. If you plot the results, they look quite different. In terms of CLIP embeddings, in my example images I get cosine similarities of around 0.94 (ViT-H14::laion2b-s32b-b79k), which is less than I would have expected.

Am I doing something wrong? Are the models you provide with clip-as-a-service trained with a different preprocessing function than the ones I located in the original repos? Are the text embeddings now slightly misaligned to the image embeddings?

[numb3r3]

@mrjackbo Thank you for pointing that out. Previously, we conducted a simple experiment to show that the _transform_ndarray wouldn't harm the downstream task (including retrieval, and zero-shot classification tasks). Thus, we made the conclusion that the embeddings from the same transform operation would be acceptable.

However, based on your question:

Are the text embeddings now slightly misaligned to the image embeddings?

I think you are right, we did not consider this use case. We should use the _transform_blob that potentially improve the text-image retrieval quality.

[saahil]

TODO:

• [ ] Change the pre-processing implementation in CLIP Executor to address the above bug