Zero-Shot Image Classification
Transformers
Safetensors
English
clip
vision-language
compositional-reasoning
contrastive-learning
text-encoder
sugarcrepe
whatsup
crepe
valse
Instructions to use Mayfull/READ-CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mayfull/READ-CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Mayfull/READ-CLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Mayfull/READ-CLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("Mayfull/READ-CLIP") - Notebooks
- Google Colab
- Kaggle
File size: 484 Bytes
353cfa2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"_name_or_path": "openai/clip-vit-base-patch32",
"architectures": [
"CLIPModel"
],
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "clip",
"projection_dim": 512,
"text_config": {
"bos_token_id": 0,
"dropout": 0.0,
"eos_token_id": 2,
"model_type": "clip_text_model"
},
"torch_dtype": "float32",
"transformers_version": "4.48.3",
"vision_config": {
"dropout": 0.0,
"model_type": "clip_vision_model"
}
}
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