Instructions to use DarthReca/depth-any-canopy-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarthReca/depth-any-canopy-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="DarthReca/depth-any-canopy-small")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("DarthReca/depth-any-canopy-small") model = AutoModelForDepthEstimation.from_pretrained("DarthReca/depth-any-canopy-small") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_commit_hash": "5426e4f0f36572d16453bbda7a8389317b1bef99", | |
| "_name_or_path": "depth-anything/Depth-Anything-V2-Small-hf", | |
| "architectures": [ | |
| "DepthAnythingForDepthEstimation" | |
| ], | |
| "backbone": null, | |
| "backbone_config": { | |
| "architectures": [ | |
| "Dinov2Model" | |
| ], | |
| "hidden_size": 384, | |
| "image_size": 518, | |
| "model_type": "dinov2", | |
| "num_attention_heads": 6, | |
| "out_features": [ | |
| "stage3", | |
| "stage6", | |
| "stage9", | |
| "stage12" | |
| ], | |
| "out_indices": [ | |
| 3, | |
| 6, | |
| 9, | |
| 12 | |
| ], | |
| "patch_size": 14, | |
| "reshape_hidden_states": false, | |
| "torch_dtype": "float32" | |
| }, | |
| "backbone_kwargs": null, | |
| "fusion_hidden_size": 64, | |
| "head_hidden_size": 32, | |
| "head_in_index": -1, | |
| "initializer_range": 0.02, | |
| "model_type": "depth_anything", | |
| "neck_hidden_sizes": [ | |
| 48, | |
| 96, | |
| 192, | |
| 384 | |
| ], | |
| "patch_size": 14, | |
| "reassemble_factors": [ | |
| 4, | |
| 2, | |
| 1, | |
| 0.5 | |
| ], | |
| "reassemble_hidden_size": 384, | |
| "torch_dtype": "float32", | |
| "transformers_version": null, | |
| "use_pretrained_backbone": false, | |
| "use_timm_backbone": false | |
| } | |