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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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--- |
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# DPT (large-sized model) |
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl-org/DPT). |
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Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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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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## Intended uses & limitations |
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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 |
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fine-tuned 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 classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
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```python |
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from transformers import DPTFeatureExtractor, 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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feature_extractor = DPTFeatureExtractor.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 = feature_extractor(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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### 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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``` |
