Zero-Shot Image Classification
Transformers
OpenCLIP
Safetensors
English
vision
image-text-to-text
medical
dermatology
multimodal
clip
zero-shot-classification
image-classification
Instructions to use redlessone/DermLIP_ViT-B-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redlessone/DermLIP_ViT-B-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="redlessone/DermLIP_ViT-B-16") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("redlessone/DermLIP_ViT-B-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Access DermLIP weights
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DermLIP is released under CC BY-NC-ND 4.0 for non-commercial academic research. Please tell us briefly who you are and how you plan to use the model; this helps us track usage and report impact to our funders. Access is granted automatically.
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