Instructions to use zer0int/LongCLIP-GmP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zer0int/LongCLIP-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/LongCLIP-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/LongCLIP-GmP-ViT-L-14") model = AutoModelForZeroShotImageClassification.from_pretrained("zer0int/LongCLIP-GmP-ViT-L-14") - Notebooks
- Google Colab
- Kaggle
Is this only the ContrastiveLoss finetuning? Did you use the Coarse-grained alignment loss proposed in LongClip?
#4
by cuifeng - opened
Is this only the ContrastiveLoss finetuning? Did you use the Coarse-grained alignment loss proposed in LongClip?
I have used the LongCLIP-L checkpoint kindly provided by the researchers of the Long-CLIP paper (starting from their model, not from OpenAI's pre-trained CLIP). However, I indeed then just used a "classic" contrastive loss to continue fine-tuning the model. You can find the code I used here: https://github.com/zer0int/Long-CLIP - feel free to modify & submit a pull request, if you'd like!