Instructions to use litert-community/depth-anything-v2-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/depth-anything-v2-small with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- DepthAnythingV2
How to use litert-community/depth-anything-v2-small with DepthAnythingV2:
# Install from https://github.com/DepthAnything/Depth-Anything-V2 # Load the model and infer depth from an image import cv2 import torch from huggingface_hub import hf_hub_download from depth_anything_v2.dpt import DepthAnythingV2 # instantiate the model model = DepthAnythingV2(encoder="<ENCODER>", features=<NUMBER_OF_FEATURES>, out_channels=<OUT_CHANNELS>) # load the weights filepath = hf_hub_download(repo_id="litert-community/depth-anything-v2-small", filename="depth_anything_v2_<ENCODER>.pth", repo_type="model") state_dict = torch.load(filepath, map_location="cpu") model.load_state_dict(state_dict) model.eval() raw_img = cv2.imread("your/image/path") depth = model.infer_image(raw_img) # HxW raw depth map in numpy - Notebooks
- Google Colab
- Kaggle
File size: 776 Bytes
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"_valid_processor_keys": [
"images",
"do_resize",
"size",
"keep_aspect_ratio",
"ensure_multiple_of",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"size_divisor",
"return_tensors",
"data_format",
"input_data_format"
],
"do_normalize": true,
"do_pad": false,
"do_rescale": true,
"do_resize": true,
"ensure_multiple_of": 14,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "DPTImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"keep_aspect_ratio": false,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 518,
"width": 686
},
"size_divisor": null
}
|