Instructions to use facebook/ijepa_vith14_1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/ijepa_vith14_1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="facebook/ijepa_vith14_1k")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/ijepa_vith14_1k") model = AutoModel.from_pretrained("facebook/ijepa_vith14_1k") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "IJepaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1280, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratio": 4, | |
| "model_type": "ijepa", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 32, | |
| "patch_size": 14, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0.dev0" | |
| } | |