Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
clip
feature-extraction
custom_code
Instructions to use chuhac/BiomedCLIP-vit-bert-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chuhac/BiomedCLIP-vit-bert-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="chuhac/BiomedCLIP-vit-bert-hf", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("chuhac/BiomedCLIP-vit-bert-hf", trust_remote_code=True) model = AutoModel.from_pretrained("chuhac/BiomedCLIP-vit-bert-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 349 Bytes
e726b94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"crop_size": 224,
"do_center_crop": true,
"do_normalize": true,
"do_resize": true,
"image_processor_type": "CLIPImageProcessor",
"tokenizer_type": "BertTokenizer",
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"size": 224
}
|