Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
contrastive-learning
zero-shot
custom_code
Instructions to use nebulette/tipsv2-b14-vision-module with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nebulette/tipsv2-b14-vision-module with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="nebulette/tipsv2-b14-vision-module", 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("nebulette/tipsv2-b14-vision-module", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "TIPSv2ImageModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_tips.TIPSv2ImageConfig", | |
| "AutoModel": "modeling_tips.TIPSv2ImageModel" | |
| }, | |
| "dtype": "float32", | |
| "ffn_layer": "mlp", | |
| "hidden_size": 768, | |
| "image_size": 448, | |
| "init_values": 1.0, | |
| "model_type": "tipsv2", | |
| "model_variant": "vit_base", | |
| "num_register_tokens": 1, | |
| "patch_size": 14, | |
| "transformers_version": "4.57.3" | |
| } | |