Image Feature Extraction
Transformers
Safetensors
clip
zero-shot-image-classification
vision
image-embeddings
pet-recognition
Instructions to use AvitoTech/CLIP-ViT-base-for-animal-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvitoTech/CLIP-ViT-base-for-animal-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="AvitoTech/CLIP-ViT-base-for-animal-identification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("AvitoTech/CLIP-ViT-base-for-animal-identification") model = AutoModelForZeroShotImageClassification.from_pretrained("AvitoTech/CLIP-ViT-base-for-animal-identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from AvitoTech/CLIP-ViT-base-for-animal-identification: direct link, hf CLI and curl.
- Browser
- Download file 316 Bytes
-
https://huggingface.co/AvitoTech/CLIP-ViT-base-for-animal-identification/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://AvitoTech/CLIP-ViT-base-for-animal-identification/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/AvitoTech/CLIP-ViT-base-for-animal-identification/resolve/main/preprocessor_config.json
316 Bytes
| { | |
| "crop_size": 224, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "size": 224 | |
| } | |