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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- depth-estimation |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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model-index: |
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- name: dpt-large |
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results: |
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- task: |
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type: monocular-depth-estimation |
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name: Monocular Depth Estimation |
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dataset: |
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type: MIX-6 |
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name: MIX-6 |
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metrics: |
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- type: Zero-shot transfer |
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value: 10.82 |
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name: Zero-shot transfer |
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config: Zero-shot transfer |
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verified: false |
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--- |
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## Model Details: DPT-Large (also known as MiDaS 3.0) |
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. |
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It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/DPT). |
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DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation. |
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The model card has been written in combination by the Hugging Face team and Intel. |
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| Model Detail | Description | |
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| ----------- | ----------- | |
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| Model Authors - Company | Intel | |
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| Date | March 22, 2022 | |
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| Version | 1 | |
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| Type | Computer Vision - Monocular Depth Estimation | |
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| Paper or Other Resources | [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) and [GitHub Repo](https://github.com/isl-org/DPT) | |
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| License | Apache 2.0 | |
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| Questions or Comments | [Community Tab](https://huggingface.co/Intel/dpt-large/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)| |
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| Intended Use | Description | |
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| ----------- | ----------- | |
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| Primary intended uses | You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for fine-tuned versions on a task that interests you. | |
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| Primary intended users | Anyone doing monocular depth estimation | |
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| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.| |
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### How to use |
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The easiest is leveraging the pipeline API: |
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``` |
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from transformers import pipeline |
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pipe = pipeline(task="depth-estimation", model="Intel/dpt-large") |
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result = pipe(image) |
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result["depth"] |
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``` |
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In case you want to implement the entire logic yourself, here's how to do that for zero-shot depth estimation on an image: |
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```python |
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from transformers import DPTImageProcessor, DPTForDepthEstimation |
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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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processor = DPTImageProcessor.from_pretrained("Intel/dpt-large") |
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") |
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# prepare image for the model |
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inputs = 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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# visualize the prediction |
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output = prediction.squeeze().cpu().numpy() |
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formatted = (output * 255 / np.max(output)).astype("uint8") |
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depth = Image.fromarray(formatted) |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt). |
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| Factors | Description | |
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| ----------- | ----------- | |
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| Groups | Multiple datasets compiled together | |
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| Instrumentation | - | |
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| Environment | Inference completed on Intel Xeon Platinum 8280 CPU @ 2.70GHz with 8 physical cores and an NVIDIA RTX 2080 GPU. | |
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| Card Prompts | Model deployment on alternate hardware and software will change model performance | |
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| Metrics | Description | |
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| ----------- | ----------- | |
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| Model performance measures | Zero-shot Transfer | |
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| Decision thresholds | - | |
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| Approaches to uncertainty and variability | - | |
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| Training and Evaluation Data | Description | |
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| ----------- | ----------- | |
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| Datasets | The dataset is called MIX 6, and contains around 1.4M images. The model was initialized with ImageNet-pretrained weights.| |
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| Motivation | To build a robust monocular depth prediction network | |
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| Preprocessing | "We resize the image such that the longer side is 384 pixels and train on random square crops of size 384. ... We perform random horizontal flips for data augmentation." See [Ranftl et al. (2021)](https://arxiv.org/abs/2103.13413) for more details. | |
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## Quantitative Analyses |
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| Model | Training set | DIW WHDR | ETH3D AbsRel | Sintel AbsRel | KITTI δ>1.25 | NYU δ>1.25 | TUM δ>1.25 | |
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| --- | --- | --- | --- | --- | --- | --- | --- | |
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| DPT - Large | MIX 6 | 10.82 (-13.2%) | 0.089 (-31.2%) | 0.270 (-17.5%) | 8.46 (-64.6%) | 8.32 (-12.9%) | 9.97 (-30.3%) | |
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| DPT - Hybrid | MIX 6 | 11.06 (-11.2%) | 0.093 (-27.6%) | 0.274 (-16.2%) | 11.56 (-51.6%) | 8.69 (-9.0%) | 10.89 (-23.2%) | |
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| MiDaS | MIX 6 | 12.95 (+3.9%) | 0.116 (-10.5%) | 0.329 (+0.5%) | 16.08 (-32.7%) | 8.71 (-8.8%) | 12.51 (-12.5%) |
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| MiDaS [30] | MIX 5 | 12.46 | 0.129 | 0.327 | 23.90 | 9.55 | 14.29 | |
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| Li [22] | MD [22] | 23.15 | 0.181 | 0.385 | 36.29 | 27.52 | 29.54 | |
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| Li [21] | MC [21] | 26.52 | 0.183 | 0.405 | 47.94 | 18.57 | 17.71 | |
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| Wang [40] | WS [40] | 19.09 | 0.205 | 0.390 | 31.92 | 29.57 | 20.18 | |
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| Xian [45] | RW [45] | 14.59 | 0.186 | 0.422 | 34.08 | 27.00 | 25.02 | |
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| Casser [5] | CS [8] | 32.80 | 0.235 | 0.422 | 21.15 | 39.58 | 37.18 | |
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Table 1. Comparison to the state of the art on monocular depth estimation. We evaluate zero-shot cross-dataset transfer according to the |
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protocol defined in [30]. Relative performance is computed with respect to the original MiDaS model [30]. Lower is better for all metrics. ([Ranftl et al., 2021](https://arxiv.org/abs/2103.13413)) |
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| Ethical Considerations | Description | |
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| ----------- | ----------- | |
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| Data | The training data come from multiple image datasets compiled together. | |
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| Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of monocular depth image datasets. | |
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| Mitigations | No additional risk mitigation strategies were considered during model development. | |
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| Risks and harms | The extent of the risks involved by using the model remain unknown. | |
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| Use cases | - | |
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| Caveats and Recommendations | |
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| ----------- | |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. | |
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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2103-13413, |
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author = {Ren{\'{e}} Ranftl and |
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Alexey Bochkovskiy and |
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Vladlen Koltun}, |
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title = {Vision Transformers for Dense Prediction}, |
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journal = {CoRR}, |
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volume = {abs/2103.13413}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2103.13413}, |
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eprinttype = {arXiv}, |
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eprint = {2103.13413}, |
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timestamp = {Wed, 07 Apr 2021 15:31:46 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2103-13413.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
