Instructions to use Andron00e/CLIPForImageClassification-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/CLIPForImageClassification-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/CLIPForImageClassification-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("Andron00e/CLIPForImageClassification-v1") model = AutoModelForImageClassification.from_pretrained("Andron00e/CLIPForImageClassification-v1") - Notebooks
- Google Colab
- Kaggle
File size: 488 Bytes
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"_name_or_path": "openai/clip-vit-base-patch32",
"architectures": [
"CLIPForImageClassification"
],
"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": "4.35.2",
"vision_config": {
"dropout": 0.0,
"model_type": "clip"
}
}
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