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")# Load model directly from transformers import AutoModelForDepthEstimation model = AutoModelForDepthEstimation.from_pretrained("WEO-SAS/chm-meta-v2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 916 Bytes
617c323 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | from abc import ABC, abstractmethod
from typing import List, Optional
import numpy as np
class BaseModel(ABC):
@abstractmethod
def predict(self, image: np.ndarray) -> np.ndarray:
"""
Run CHM inference on a single image.
Args:
image: (3, H, W) float32 numpy array, values in [0, 1]
Returns:
(H, W) float32 numpy array — canopy height in metres
"""
pass
@abstractmethod
def predict_tif(
self,
input_path: str,
output_path: str,
bands: Optional[List[int]] = None,
) -> None:
"""
Full GeoTIFF CHM pipeline.
Args:
input_path : path to input RGB or multi-band GeoTIFF
output_path : output path for CHM GeoTIFF (1 band, metres)
bands : 0-based band indices to use as RGB (default: [0,1,2])
"""
pass
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