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
File size: 431 Bytes
28d6428 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"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"
}
|