Instructions to use zer0int/CLIP-GmP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zer0int/CLIP-GmP-ViT-L-14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="zer0int/CLIP-GmP-ViT-L-14") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("zer0int/CLIP-GmP-ViT-L-14") model = AutoModelForZeroShotImageClassification.from_pretrained("zer0int/CLIP-GmP-ViT-L-14") - Notebooks
- Google Colab
- Kaggle
Add new #1 top-performing smooth-GmP model
Browse filesFine-tuned with GmP on COCO-SPRIGHT-40k as before, BUT with new a custom loss function and label smoothing: This CLIP has >91% accuracy on ImageNet/ObjectNet!
ViT-L-14-BEST-smooth-GmP-ft-pickle-OpenAI.pt
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oid sha256:b2135fa3c6d8f9a64e3114b673e12ca38d50c77da0204ffeb34a9aeb29a7791e
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size 932307905
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ViT-L-14-BEST-smooth-GmP-ft-state_dict.pt
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oid sha256:61661af02a59b5d5a175a94cba447ec5cc2318532ef202343a3be063b20981a9
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size 932240142
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ViT-L-14-BEST-smooth-GmP-ft.safetensors
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oid sha256:9fb57a8045c00fd501d9195515e94c0ae339d0fb9f46dc76cb20e685886494d9
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size 932103308
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