Depth Estimation
Transformers
Safetensors
English
chmv2
dinov3
canopy-height
chm
Eval Results (legacy)
Instructions to use WEO-SAS/chm-meta-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WEO-SAS/chm-meta-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="WEO-SAS/chm-meta-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForDepthEstimation model = AutoModelForDepthEstimation.from_pretrained("WEO-SAS/chm-meta-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from WEO-SAS/chm-meta-v2: direct link, hf CLI and curl.
- Browser
- Download file 475 Bytes
-
https://huggingface.co/WEO-SAS/chm-meta-v2/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://WEO-SAS/chm-meta-v2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/WEO-SAS/chm-meta-v2/resolve/main/preprocessor_config.json
475 Bytes
| { | |
| "data_format": "channels_first", | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": false, | |
| "ensure_multiple_of": 16, | |
| "image_mean": [ | |
| 0.42, | |
| 0.411, | |
| 0.296 | |
| ], | |
| "image_processor_type": "CHMv2ImageProcessorFast", | |
| "image_std": [ | |
| 0.213, | |
| 0.156, | |
| 0.143 | |
| ], | |
| "keep_aspect_ratio": true, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| }, | |
| "size_divisor": 16 | |
| } | |