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
| { | |
| "model_cfg": { | |
| "embed_dim": 512, | |
| "vision_cfg": { | |
| "image_size": 224, | |
| "layers": 12, | |
| "width": 768, | |
| "patch_size": 16 | |
| }, | |
| "text_cfg": { | |
| "context_length": 77, | |
| "vocab_size": 49408, | |
| "width": 512, | |
| "heads": 8, | |
| "layers": 12 | |
| } | |
| }, | |
| "preprocess_cfg": { | |
| "mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "interpolation": "bicubic", | |
| "resize_mode": "shortest" | |
| } | |
| } |