Demystifying CLIP Data — mentioned by zerojames

Thu, 26 Oct 2023 14:28:41 GMT

I have been playing with MetaCLIP this afternoon and made https://github.com/autodistill/autodistill-metaclip as a pip installable version. The Facebook repository has some guidance but you have to pull the weights yourself, save them, etc. My inference function (model.predict("image.png")) return an sv.Classifications object that you can load into supervision for processing (i.e. get top k) [1]. The paper [2] notes the following in terms of performance: > In Table 4, we observe that MetaCLIP outperforms OpenAI CLIP on ImageNet and average accuracy across 26 tasks, for 3 model scales. With…

Paper and discussion history. A mention is not an endorsement.

Demystifying CLIP Data — HN submission

Wed, 04 Oct 2023 16:33:55 GMT

Demystifying CLIP Data

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Demystifying CLIP Data · HackerPapers discussions

Demystifying CLIP Data — mentioned by zerojames

Demystifying CLIP Data — HN submission