Depth Estimation
Transformers
Safetensors
prompt_depth_anything
prompt-depth-anything
metric-depth
custom_code
Instructions to use bigshanedogg/Prompt-Depth-Anything-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Prompt-Depth-Anything-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True) model = AutoModelForDepthEstimation.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE from bigshanedogg/Prompt-Depth-Anything-Small: direct link, hf CLI and curl.
- Browser
- Download file 2.36 kB
-
https://huggingface.co/bigshanedogg/Prompt-Depth-Anything-Small/resolve/main/NOTICE
- Command line
-
hf download hf://bigshanedogg/Prompt-Depth-Anything-Small/NOTICE
-
curl -L -o NOTICE https://huggingface.co/bigshanedogg/Prompt-Depth-Anything-Small/resolve/main/NOTICE
2.36 kB
| Prompt-Depth-Anything-Small -- unofficial transformers-format package (trust_remote_code) | |
| Packaged by bigshanedogg, 2026. NOT an official release of the original authors. | |
| Components | |
| ---------- | |
| 1. Prompt Depth Anything weights (model.ckpt, prompt-depth-anything-vits) | |
| Source: https://huggingface.co/depth-anything/prompt-depth-anything-vits | |
| Copyright: Haotong Lin, Sida Peng, Jingxiao Chen, Songyou Peng, Jiaming Sun, Minghuan Liu, Hujun Bao, Jiashi Feng, Xiaowei Zhou, Bingyi Kang | |
| License: Apache-2.0 -- LICENSE | |
| 2. Prompt Depth Anything code (promptda/promptda.py, model/, utils/logger.py; vendored into promptda_model.py; load_image / load_depth of utils/io_wrapper.py re-typed in image_processing_prompt_depth_anything.py) | |
| Source: https://github.com/DepthAnything/PromptDA @ 9161547ca7151e14fcc8f490ad3cc02fa9437483 | |
| Copyright: the Prompt Depth Anything authors | |
| License: Apache-2.0 -- LICENSE | |
| 3. DINOv2 code (the repo's torchhub copy of facebookresearch/dinov2; vendored into promptda_dinov2.py) | |
| Source: https://github.com/DepthAnything/PromptDA/tree/main/torchhub/facebookresearch_dinov2_main @ 9161547ca7151e14fcc8f490ad3cc02fa9437483 | |
| Copyright: Copyright (c) Meta Platforms, Inc. and affiliates. | |
| License: Apache-2.0 -- LICENSE.dinov2 | |
| Changes made in this package | |
| ---------------------------- | |
| - promptda_dinov2.py / promptda_model.py: the official files concatenated into flat files; package-internal imports replaced by relative imports; NestedTensorBlock spelled out where vision_transformer.py imported it as Block. No computation changed. | |
| - configuration_ / modeling_ / image_processing_ / processing_prompt_depth_anything.py: new transformers wrappers; the model builds DINOv2 with the torchhub hub's arguments instead of torch.hub.load and runs PromptDA.forward; the image processor re-types io_wrapper.load_image / load_depth (that module imports open3d / matplotlib), checked bit-exact. | |
| - model.safetensors: the Lightning model.ckpt state dict without its 'pipeline.' prefix (as PromptDA.load_checkpoint), converted by save_pretrained, every tensor checked equal; the training state (epoch, loops) dropped. | |
| License files | |
| ------------- | |
| - LICENSE: Apache License 2.0, copy from DepthAnything/PromptDA (code and weights) | |
| - LICENSE.dinov2: Apache License 2.0, copy from the repo's torchhub/facebookresearch_dinov2_main | |