Comments (3)
Hi, try to use this:
import open_clip
model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:wisdomik/QuiltNet-B-16')
tokenizer = open_clip.get_tokenizer('hf-hub:wisdomik/QuiltNet-B-16')
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I wonder 'pytorch_model.bin' and 'open_clip_pytorch_model.bin' which one is the right bin that you trainen yourself?
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Training outputs open_clip_pytorch_model.bin and we convert it to pytorch_model.bin also for compatibility with loading the models directly using HF CLIP modules and not just open_clip. So both are the same and trained as discussed in the paper.
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Related Issues (20)
- Reproducing zero-shot classification results HOT 5
- How to fine-tune QuiltNet-B-32 model HOT 1
- please do not use BiomedCLIP for ARCH dataset HOT 2
- How to access the corresponding texts for QUILT dataset? I got the access to images via ZENODO but could not find corresponding texts. HOT 2
- Visualizations of the results HOT 1
- Access to the data HOT 1
- Clarification on stable_times assignment in data/main.py HOT 1
- Image-to-text generation HOT 1
- Issues with CSV files HOT 2
- Load ViT-B/32 HOT 1
- Missing Images HOT 3
- Keyframe Extraction Code HOT 1
- Superflous images? HOT 1
- Which preprocess should I use for linear probing ? HOT 2
- Training Text HOT 1
- Noisy text
- Text Context Length HOT 2
- How to access Quilt-Instruct?
- Providing dataset HOT 2
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