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 config.json from AvitoTech/CLIP-ViT-base-for-animal-identification: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/AvitoTech/CLIP-ViT-base-for-animal-identification/resolve/refs%2Fpr%2F2/config.json
- Command line
-
hf download hf://AvitoTech/CLIP-ViT-base-for-animal-identification@refs/pr/2/config.json
-
curl -L -o config.json https://huggingface.co/AvitoTech/CLIP-ViT-base-for-animal-identification/resolve/refs%2Fpr%2F2/config.json
1.09 kB
| { | |
| "architectures": [ | |
| "CLIPModel" | |
| ], | |
| "model_type": "clip", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "architectures": [ | |
| "CLIPTextModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 512, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 77, | |
| "model_type": "clip_text_model", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "vocab_size": 49408 | |
| }, | |
| "vision_config": { | |
| "architectures": [ | |
| "CLIPVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 32 | |
| } | |
| } |