90 lines
3.1 KiB
Markdown
90 lines
3.1 KiB
Markdown
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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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```
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