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"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/tipsv2-b14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 347 Bytes
75750a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"image_processor": {
"do_convert_rgb": true,
"do_normalize": false,
"do_rescale": true,
"do_resize": true,
"image_processor_type": "Tipsv2ImageProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 448,
"width": 448
}
},
"processor_class": "Tipsv2Processor"
}
|