Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
feature-extraction
vision
image-text
contrastive-learning
zero-shot
custom_code
Instructions to use google/tipsv2-b14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-b14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-b14", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/tipsv2-b14", trust_remote_code=True) model = AutoModel.from_pretrained("google/tipsv2-b14", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 528 Bytes
105fb64 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"model_type": "tipsv2",
"architectures": [
"TIPSv2Model"
],
"auto_map": {
"AutoConfig": "configuration_tips.TIPSv2Config",
"AutoModel": "modeling_tips.TIPSv2Model"
},
"patch_size": 14,
"img_size": 448,
"init_values": 1.0,
"num_register_tokens": 1,
"vocab_size": 32000,
"max_len": 64,
"vision_fn": "vit_base",
"embed_dim": 768,
"text_hidden_size": 768,
"text_mlp_dim": 3072,
"text_num_heads": 12,
"text_num_layers": 12,
"ffn_layer": "mlp",
"temperature": 0.005065968260169029
} |