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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
 
 
 
 
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
 
 
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
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- [More Information Needed]
 
 
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- ### Downstream Use [optional]
 
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
 
 
 
 
 
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- ### Out-of-Scope Use
 
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
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- [More Information Needed]
 
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- ## Bias, Risks, and Limitations
 
 
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 
 
 
 
 
 
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ---
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  library_name: transformers
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+ library: transformers
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+ license: cc-by-nc-4.0
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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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+ widget:
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+ - inference: false
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  ---
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+ # Depth Anything V2 Base – 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="depth-anything/Depth-Anything-V2-Base-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("depth-anything/Depth-Anything-V2-Base-hf")
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+ model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Base-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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+ ```