Instructions to use facebook/dino-vitb8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/dino-vitb8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="facebook/dino-vitb8")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/dino-vitb8") model = AutoModel.from_pretrained("facebook/dino-vitb8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from facebook/dino-vitb8: direct link, hf CLI and curl.
- Browser
- Download file 453 Bytes
-
https://huggingface.co/facebook/dino-vitb8/resolve/refs%2Fpr%2F3/config.json
- Command line
-
hf download hf://facebook/dino-vitb8@refs/pr/3/config.json
-
curl -L -o config.json https://huggingface.co/facebook/dino-vitb8/resolve/refs%2Fpr%2F3/config.json
453 Bytes
| { | |
| "architectures": [ | |
| "ViTModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "vit", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 8, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.10.0.dev0" | |
| } | |