Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-l14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-l14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-l14", 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 AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/tipsv2-l14", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-l14", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 292 Bytes
c060861 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"backend": "tokenizers",
"bos_token": null,
"do_lower_case": true,
"eos_token": null,
"model_max_length": 64,
"pad_token": "<pad>",
"processor_class": "Tipsv2Processor",
"token_type_ids_pattern": "all_zeros",
"tokenizer_class": "Tipsv2Tokenizer",
"unk_token": "<unk>"
}
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