Depth Anything V2 Small (Transformers version)
Browse files- README.md +108 -0
- config.json +53 -0
- model.safetensors +3 -0
- preprocessor_config.json +44 -0
README.md
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--
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license: apache-2.0
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tags:
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- depth
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- relative depth
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pipeline_tag: depth-estimation
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library: transformers
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widget:
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- inference: false
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---
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# Depth Anything V2 Small – Transformers Version
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Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features:
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- more fine-grained details than Depth Anything V1
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- more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard)
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- more efficient (10x faster) and more lightweight than SD-based models
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- impressive fine-tuned performance with our pre-trained models
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This model checkpoint is compatible with the transformers library.
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Depth Anything V2 was introduced in [the paper of the same name](https://arxiv.org/abs/2406.09414) by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang et al., and was first released in [this repository](https://github.com/LiheYoung/Depth-Anything).
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[Online demo](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2).
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## Model description
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Depth Anything V2 leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone.
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The model is trained on ~600K synthetic labeled images and ~62 million real unlabeled images, obtaining state-of-the-art results for both relative and absolute depth estimation.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg"
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alt="drawing" width="600"/>
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<small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small>
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## Intended uses & limitations
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You can use the raw model for tasks like zero-shot depth estimation. See the [model hub](https://huggingface.co/models?search=depth-anything) to look for
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other versions on a task that interests you.
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### How to use
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Here is how to use this model to perform zero-shot depth estimation:
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```python
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from transformers import pipeline
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from PIL import Image
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import requests
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# load pipe
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pipe = pipeline(task="depth-estimation", model="pcuenq/Depth-Anything-V2-Small-hf")
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# load image
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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# inference
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depth = pipe(image)["depth"]
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```
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Alternatively, you can use the model and processor classes:
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```python
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from transformers import AutoImageProcessor, AutoModelForDepthEstimation
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import torch
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import numpy as np
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from PIL import Image
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import requests
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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image_processor = AutoImageProcessor.from_pretrained("pcuenq/Depth-Anything-V2-Small-hf")
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model = AutoModelForDepthEstimation.from_pretrained("pcuenq/Depth-Anything-V2-Small-hf")
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# prepare image for the model
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inputs = image_processor(images=image, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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predicted_depth = outputs.predicted_depth
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# interpolate to original size
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prediction = torch.nn.functional.interpolate(
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predicted_depth.unsqueeze(1),
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size=image.size[::-1],
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mode="bicubic",
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align_corners=False,
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)
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```
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For more code examples, please refer to the [documentation](https://huggingface.co/transformers/main/model_doc/depth_anything.html#).
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### Citation
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```bibtex
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@misc{yang2024depth,
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title={Depth Anything V2},
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author={Lihe Yang and Bingyi Kang and Zilong Huang and Zhen Zhao and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao},
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year={2024},
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eprint={2406.09414},
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archivePrefix={arXiv},
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primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'}
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}
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```
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config.json
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{
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"_commit_hash": null,
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"architectures": [
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"DepthAnythingForDepthEstimation"
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],
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"backbone": null,
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"backbone_config": {
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"architectures": [
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"Dinov2Model"
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],
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"hidden_size": 384,
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"image_size": 518,
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"model_type": "dinov2",
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"num_attention_heads": 6,
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"out_features": [
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"stage3",
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"stage6",
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"stage9",
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"stage12"
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],
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"out_indices": [
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],
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"patch_size": 14,
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"reshape_hidden_states": false,
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"torch_dtype": "float32"
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},
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"fusion_hidden_size": 64,
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"head_hidden_size": 32,
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"head_in_index": -1,
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"initializer_range": 0.02,
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"model_type": "depth_anything",
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"neck_hidden_sizes": [
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48,
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"patch_size": 14,
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"reassemble_factors": [
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4,
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],
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"reassemble_hidden_size": 384,
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"torch_dtype": "float32",
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"transformers_version": null,
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"use_pretrained_backbone": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3152477ce0d8d6978d76b995120de97cb5b928701fd0f817769f59e249a16b70
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size 99173660
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preprocessor_config.json
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{
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"_valid_processor_keys": [
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"images",
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"do_resize",
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"size",
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"keep_aspect_ratio",
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"ensure_multiple_of",
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"resample",
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"do_rescale",
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"rescale_factor",
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"do_normalize",
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"image_mean",
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"image_std",
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"do_pad",
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"size_divisor",
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"return_tensors",
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"data_format",
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"input_data_format"
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],
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"do_normalize": true,
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"do_pad": false,
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"do_rescale": true,
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"do_resize": true,
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"ensure_multiple_of": 14,
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"image_mean": [
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0.485,
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],
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"image_processor_type": "DPTImageProcessor",
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"image_std": [
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0.229,
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],
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"keep_aspect_ratio": true,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 518,
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"width": 518
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},
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"size_divisor": null
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}
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