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README.md
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---
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license: apache-2.0
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dataset_info:
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data_files:
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- split: train
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path: data/train-*
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: image
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dtype: image
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- name: label
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dtype:
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class_label:
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names:
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'0': croissant
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'1': kitchen-counter
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'2': serving-cup
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'3': whiteboard
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splits:
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- name: train
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num_bytes: 3760395
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num_examples: 23
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download_size: 3762376
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dataset_size: 3760395
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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tags:
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- robotics
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- computer vision
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pretty_name: Reachy Doing Things Image Dataset
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size_categories:
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- n<1K
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---
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# Reachy Doing Things Dataset
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![demo](https://github.com/pollen-robotics/pollen-vision/assets/pollen_vision_intro.gif)
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## Overview
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The Reachy Doing Things Images Dataset consists of images captured from the perspective of the Reachy humanoid robot. These images were taken during teleoperation sessions, providing a unique view of the environment as perceived by the robot during manipulation tasks. The images were captured with a RGBD camera mounted on the shoulder of the robot.
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## Purpose
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This dataset is primarily aimed at testing and validating the performance of vision tools integrated into the [pollen-vision library](https://github.com/pollen-robotics/pollen-vision). Currently, it serves as a validation suite for object detection, object segmentation, and image tagging algorithms.
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While it is not intended for training models at this stage, it provides valuable real-world data to refine and improve these vision tools. The images in the dataset are not annotated.
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## Content
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- Non annotated ego-centric images captured during teleoperation sessions with Reachy
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## Future Updates
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While the dataset is currently limited in size, ongoing efforts are underway to expand its content. Future updates will include additional images and annotations, making it a more comprehensive resource for vision research and development.
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## Contact
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For any inquiries or feedback regarding the dataset, please contact contact@pollen-robotics.com.
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---
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license: apache-2.0
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dataset_info:
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data_files:
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- split: train
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path: data/train-*
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---
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