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
| """TIPSv2 model configuration.""" | |
| from transformers import PretrainedConfig | |
| class TIPSv2ImageConfig(PretrainedConfig): | |
| """Configuration for TIPSv2 vision-language model.""" | |
| model_type = "tipsv2" | |
| def __init__( | |
| self, | |
| model_variant="base", | |
| hidden_size=768, | |
| patch_size=14, | |
| image_size=448, | |
| ffn_layer="mlp", | |
| init_values=1.0, | |
| num_register_tokens=1, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.model_variant = model_variant | |
| self.hidden_size = hidden_size | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.ffn_layer = ffn_layer | |
| self.init_values = init_values | |
| self.num_register_tokens = num_register_tokens | |