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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("Andron00e/CLIPForImageClassification-v1") model = AutoModelForImageClassification.from_pretrained("Andron00e/CLIPForImageClassification-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Andron00e/CLIPForImageClassification-v1: direct link, hf CLI and curl.
- Browser
- Download file 488 Bytes
-
https://huggingface.co/Andron00e/CLIPForImageClassification-v1/resolve/eec6bf2d0e60e39be759ec4b45c53bd960f41486/config.json
- Command line
-
hf download hf://Andron00e/CLIPForImageClassification-v1@eec6bf2d0e60e39be759ec4b45c53bd960f41486/config.json
-
curl -L -o config.json https://huggingface.co/Andron00e/CLIPForImageClassification-v1/resolve/eec6bf2d0e60e39be759ec4b45c53bd960f41486/config.json
488 Bytes
| { | |
| "_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" | |
| } | |
| } | |