Zero-Shot Image Classification
Transformers
Safetensors
gil_clip
feature-extraction
clip
fashion-clip
vision
multimodal
fashion
custom_code
Instructions to use gilgmesh/gil-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gilgmesh/gil-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gilgmesh/gil-clip", trust_remote_code=True) 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("gilgmesh/gil-clip", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "added_tokens_decoder": { | |
| "49406": { | |
| "content": "<|startoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "49407": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "bos_token": "<|startoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "do_lower_case": true, | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "extra_special_tokens": {}, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|endoftext|>", | |
| "processor_class": "CLIPProcessor", | |
| "tokenizer_class": "CLIPTokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |