The MS COCO (Microsoft Common Objects in Context) 2017 dataset is a large-scale benchmark for object detection, segmentation, key-point detection, and image captioning. It includes over 328K images with comprehensive annotations that drive advancements in computer vision research.

Dataset

This dataset was created by Tinsae Bahiru

Released under Apache 2.0

Contents

Multitask COCO Dataset – Detection, Keypoints, and Segmentation

This publicly available Multitask COCO dataset has been preprocessed for seamless use in object detection, keypoint detection, and segmentation tasks. It enables multi-label annotations for COCO, ensuring robust performance across various vision applications. Special thanks to yermandy for providing access to multi-label annotations.

Optimized for deep learning models, this dataset is structured for easy integration into training pipelines, supporting diverse applications in computer vision research.

The COCO dataset is a foundational large-scale benchmark for object detection, segmentation, captioning, and keypoint analysis. Created by Microsoft, it features complex everyday scenes with common objects in their natural contexts. With over 330,000 images and 2.5 million labeled instances, it has become the gold standard for training and evaluating computer vision models.

File Information

images/
Contains 2 subdirectories split by usage:
train2017/: Main training set (118K images)
val2017/: Validation set (5K images)
File Naming: 000000000009.jpg (12-digit zero-padded IDs)
Formats: JPEG images with varying resolutions (average 640×480)

annotations/
Contains task-specific JSON files with consistent naming:
captions_*.json: 5 human-generated descriptions per image

Microsoft Common Objects in Context (COCO) Dataset

The Common Objects in Context (COCO) dataset is a widely recognized collection designed to spur object detection, segmentation, and captioning research. Created by Microsoft, COCO provides annotations, including object categories, keypoints, and more. The model it a valuable asset for machine learning practitioners and researchers. Today, many model architectures are benchmarked against COCO, which has enabled a standard system by which architectures can be compared.

While COCO is often touted to comprise over 300k images, it's pivotal to understand that this number includes diverse formats like keypoints, among others. Specifically, the labeled dataset for object detection stands at 123,272 images.

The full object detection labeled dataset is made available here, ensuring researchers have access to the most comprehensive data for their experiments. With that said, COCO has not released their test set annotations, meaning the test data doesn't come with labels. Thus, this data is not included in the dataset.

The Roboflow team has worked extensively with COCO. Here are a few links that may be helpful as you get started working with this dataset:

• An introduction to the COCO dataset
• Weird images in COCO, and what that tells us about the utility and limits of COCO

COCO formatted 2D skeletal keypoints for YouTube and Clinical datasets from: Computer vision to automatically assess infant neuromotor risk (Chambers et al 2020) extracted with ViTPose-H implemented in MMPose. For original dataset and data see: https://figshare.com/s/10034c230ad9b2b2a6a4Includes:json files with bounding boxes and 2D keypoints/confidencesvideo metadata (fps, original dimensions)Data descriptions:Youtube Dataset: 94 infants, 19 excluded19 annotations removed for meeting one or more of the following exclusion criteria:8 Partially overlapping twins4 NICU and/or hospital settings1 In water1 On a rocker2 Face occluded by caregiver and/or toy3 Low contrast/very poor video qualityClinical Dataset: 19 infants (31 videos total)Additional data available in original figshare

Microsoft COCO Pose Detection

## Overview

Microsoft COCO Pose Detection is a dataset for computer vision tasks - it contains Objects annotations for 5,105 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

This Is Keypoint-Only subset from COCO 2017 Dataset. You can access the original COCO Dataset from here

This Dataset contains three folders: annotations, val2017, and train2017. - Contents in annotation folder is two jsons, for val dan train. Each jsons contains various informations, like the image id, bounding box, and keypoints locations. - Contents of val2017 and train2017 is various images that have been filtered. They are the images that have num_keypoints > 0 according to the annotation file.

MS COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.

This is Part 2/2 of the ActiveHuman dataset! Part 1 can be found here. Dataset Description ActiveHuman was generated using Unity's Perception package. It consists of 175428 RGB images and their semantic segmentation counterparts taken at different environments, lighting conditions, camera distances and angles. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals). The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset. Alongside each image, 2D Bounding Box, 3D Bounding Box and Keypoint ground truth annotations are also generated via the use of Labelers and are stored as a JSON-based dataset. These Labelers are scripts that are responsible for capturing ground truth annotations for each captured image or frame. Keypoint annotations follow the COCO format defined by the COCO keypoint annotation template offered in the perception package.

Folder configuration The dataset consists of 3 folders:

JSON Data: Contains all the generated JSON files. RGB Images: Contains the generated RGB images. Semantic Segmentation Images: Contains the generated semantic segmentation images.

Essential Terminology

Annotation: Recorded data describing a single capture. Capture: One completed rendering process of a Unity sensor which stored the rendered result to data files (e.g. PNG, JPG, etc.). Ego: Object or person on which a collection of sensors is attached to (e.g., if a drone has a camera attached to it, the drone would be the ego and the camera would be the sensor). Ego coordinate system: Coordinates with respect to the ego. Global coordinate system: Coordinates with respect to the global origin in Unity. Sensor: Device that captures the dataset (in this instance the sensor is a camera). Sensor coordinate system: Coordinates with respect to the sensor. Sequence: Time-ordered series of captures. This is very useful for video capture where the time-order relationship of two captures is vital. UIID: Universal Unique Identifier. It is a unique hexadecimal identifier that can represent an individual instance of a capture, ego, sensor, annotation, labeled object or keypoint, or keypoint template.

Dataset Data The dataset includes 4 types of JSON annotation files files:

annotation_definitions.json: Contains annotation definitions for all of the active Labelers of the simulation stored in an array. Each entry consists of a collection of key-value pairs which describe a particular type of annotation and contain information about that specific annotation describing how its data should be mapped back to labels or objects in the scene. Each entry contains the following key-value pairs:

id: Integer identifier of the annotation's definition. name: Annotation name (e.g., keypoints, bounding box, bounding box 3D, semantic segmentation). description: Description of the annotation's specifications. format: Format of the file containing the annotation specifications (e.g., json, PNG). spec: Format-specific specifications for the annotation values generated by each Labeler.

Most Labelers generate different annotation specifications in the spec key-value pair:

BoundingBox2DLabeler/BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. KeypointLabeler:

template_id: Keypoint template UUID. template_name: Name of the keypoint template. key_points: Array containing all the joints defined by the keypoint template. This array includes the key-value pairs:

label: Joint label. index: Joint index. color: RGBA values of the keypoint. color_code: Hex color code of the keypoint skeleton: Array containing all the skeleton connections defined by the keypoint template. Each skeleton connection defines a connection between two different joints. This array includes the key-value pairs:

label1: Label of the first joint. label2: Label of the second joint. joint1: Index of the first joint. joint2: Index of the second joint. color: RGBA values of the connection. color_code: Hex color code of the connection. SemanticSegmentationLabeler:

label_name: String identifier of a label. pixel_value: RGBA values of the label. color_code: Hex color code of the label.

captures_xyz.json: Each of these files contains an array of ground truth annotations generated by each active Labeler for each capture separately, as well as extra metadata that describe the state of each active sensor that is present in the scene. Each array entry in the contains the following key-value pairs:

id: UUID of the capture. sequence_id: UUID of the sequence. step: Index of the capture within a sequence. timestamp: Timestamp (in ms) since the beginning of a sequence. sensor: Properties of the sensor. This entry contains a collection with the following key-value pairs:

sensor_id: Sensor UUID. ego_id: Ego UUID. modality: Modality of the sensor (e.g., camera, radar). translation: 3D vector that describes the sensor's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable that describes the sensor's orientation with respect to the ego coordinate system. camera_intrinsic: matrix containing (if it exists) the camera's intrinsic calibration. projection: Projection type used by the camera (e.g., orthographic, perspective). ego: Attributes of the ego. This entry contains a collection with the following key-value pairs:

ego_id: Ego UUID. translation: 3D vector that describes the ego's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable containing the ego's orientation. velocity: 3D vector containing the ego's velocity (in meters per second). acceleration: 3D vector containing the ego's acceleration (in ). format: Format of the file captured by the sensor (e.g., PNG, JPG). annotations: Key-value pair collections, one for each active Labeler. These key-value pairs are as follows:

id: Annotation UUID . annotation_definition: Integer identifier of the annotation's definition. filename: Name of the file generated by the Labeler. This entry is only present for Labelers that generate an image. values: List of key-value pairs containing annotation data for the current Labeler.

Each Labeler generates different annotation specifications in the values key-value pair:

BoundingBox2DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. x: Position of the 2D bounding box on the X axis. y: Position of the 2D bounding box position on the Y axis. width: Width of the 2D bounding box. height: Height of the 2D bounding box. BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. translation: 3D vector containing the location of the center of the 3D bounding box with respect to the sensor coordinate system (in meters). size: 3D vector containing the size of the 3D bounding box (in meters) rotation: Quaternion variable containing the orientation of the 3D bounding box. velocity: 3D vector containing the velocity of the 3D bounding box (in meters per second). acceleration: 3D vector containing the acceleration of the 3D bounding box acceleration (in ). KeypointLabeler:

label_id: Integer identifier of a label. instance_id: UUID of one instance of a joint. Keypoints with the same joint label that are visible on the same capture have different instance_id values. template_id: UUID of the keypoint template. pose: Pose label for that particular capture. keypoints: Array containing the properties of each keypoint. Each keypoint that exists in the keypoint template file is one element of the array. Each entry's contents have as follows:

index: Index of the keypoint in the keypoint template file. x: Pixel coordinates of the keypoint on the X axis. y: Pixel coordinates of the keypoint on the Y axis. state: State of the keypoint.

The SemanticSegmentationLabeler does not contain a values list.

egos.json: Contains collections of key-value pairs for each ego. These include:

id: UUID of the ego. description: Description of the ego. sensors.json: Contains collections of key-value pairs for all sensors of the simulation. These include:

id: UUID of the sensor. ego_id: UUID of the ego on which the sensor is attached. modality: Modality of the sensor (e.g., camera, radar, sonar). description: Description of the sensor (e.g., camera, radar).

Image names The RGB and semantic segmentation images share the same image naming convention. However, the semantic segmentation images also contain the string Semantic_ at the beginning of their filenames. Each RGB image is named "e_h_l_d_r.jpg", where:

e denotes the id of the environment. h denotes the id of the person. l denotes the id of the lighting condition. d denotes the camera distance at which the image was captured. r denotes the camera angle at which the image was captured.

Coco Kp

## Overview

Coco Kp is a dataset for computer vision tasks - it contains  CuRQ annotations for 319 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

📌 What's Included:

• Training Set: 118K images with annotations for detection, segmentation, and keypoints.
• Validation Set: 5K images with full annotations for validation.
• Testing Set: Images are divided into two splits—dev and challenge—replacing the four splits (dev, standard, reserve, challenge) used in previous years.
• Stuff Annotations: Available for 40K images in the training set and 5K validation images, enabling semantic segmentation research.
• Unlabeled Data: A set of 120K images with no annotations, mirroring the class distribution of the labeled data. This is ideal for exploring semi-supervised learning techniques.

🔍 Key Changes in COCO 2017:

• The train/val split was updated based on community feedback, now featuring 118K/5K images instead of the previous 83K/41K split.
• While the annotations for detection and keypoints are consistent with previous years, additional stuff annotations were introduced in 2017.
• Unlabeled data is now available for semi-supervised learning tasks, opening new avenues for experimentation.

📂 Dataset Structure:

• train2017: Images and annotations
• val2017: Images and annotations
• test2017: Images (no annotations provided)
• unlabeled2017: Unlabeled images

This dataset can be used for a variety of computer vision tasks, including object detection, instance segmentation, keypoint detection, semantic segmentation, and image captioning. Whether you're working on supervised or semi-supervised learning, this resource is designed to meet your needs.

From_coco

## Overview

From_coco is a dataset for computer vision tasks - it contains Armor WTdO annotations for 9,293 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This is Part 1/2 of the ActiveHuman dataset! Part 2 can be found here. Dataset Description ActiveHuman was generated using Unity's Perception package. It consists of 175428 RGB images and their semantic segmentation counterparts taken at different environments, lighting conditions, camera distances and angles. In total, the dataset contains images for 8 environments, 33 humans, 4 lighting conditions, 7 camera distances (1m-4m) and 36 camera angles (0-360 at 10-degree intervals). The dataset does not include images at every single combination of available camera distances and angles, since for some values the camera would collide with another object or go outside the confines of an environment. As a result, some combinations of camera distances and angles do not exist in the dataset. Alongside each image, 2D Bounding Box, 3D Bounding Box and Keypoint ground truth annotations are also generated via the use of Labelers and are stored as a JSON-based dataset. These Labelers are scripts that are responsible for capturing ground truth annotations for each captured image or frame. Keypoint annotations follow the COCO format defined by the COCO keypoint annotation template offered in the perception package.

Folder configuration The dataset consists of 3 folders:

JSON Data: Contains all the generated JSON files. RGB Images: Contains the generated RGB images. Semantic Segmentation Images: Contains the generated semantic segmentation images.

Essential Terminology

Annotation: Recorded data describing a single capture. Capture: One completed rendering process of a Unity sensor which stored the rendered result to data files (e.g. PNG, JPG, etc.). Ego: Object or person on which a collection of sensors is attached to (e.g., if a drone has a camera attached to it, the drone would be the ego and the camera would be the sensor). Ego coordinate system: Coordinates with respect to the ego. Global coordinate system: Coordinates with respect to the global origin in Unity. Sensor: Device that captures the dataset (in this instance the sensor is a camera). Sensor coordinate system: Coordinates with respect to the sensor. Sequence: Time-ordered series of captures. This is very useful for video capture where the time-order relationship of two captures is vital. UIID: Universal Unique Identifier. It is a unique hexadecimal identifier that can represent an individual instance of a capture, ego, sensor, annotation, labeled object or keypoint, or keypoint template.

Dataset Data The dataset includes 4 types of JSON annotation files files:

annotation_definitions.json: Contains annotation definitions for all of the active Labelers of the simulation stored in an array. Each entry consists of a collection of key-value pairs which describe a particular type of annotation and contain information about that specific annotation describing how its data should be mapped back to labels or objects in the scene. Each entry contains the following key-value pairs:

id: Integer identifier of the annotation's definition. name: Annotation name (e.g., keypoints, bounding box, bounding box 3D, semantic segmentation). description: Description of the annotation's specifications. format: Format of the file containing the annotation specifications (e.g., json, PNG). spec: Format-specific specifications for the annotation values generated by each Labeler.

Most Labelers generate different annotation specifications in the spec key-value pair:

BoundingBox2DLabeler/BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. KeypointLabeler:

template_id: Keypoint template UUID. template_name: Name of the keypoint template. key_points: Array containing all the joints defined by the keypoint template. This array includes the key-value pairs:

label: Joint label. index: Joint index. color: RGBA values of the keypoint. color_code: Hex color code of the keypoint skeleton: Array containing all the skeleton connections defined by the keypoint template. Each skeleton connection defines a connection between two different joints. This array includes the key-value pairs:

label1: Label of the first joint. label2: Label of the second joint. joint1: Index of the first joint. joint2: Index of the second joint. color: RGBA values of the connection. color_code: Hex color code of the connection. SemanticSegmentationLabeler:

label_name: String identifier of a label. pixel_value: RGBA values of the label. color_code: Hex color code of the label.

captures_xyz.json: Each of these files contains an array of ground truth annotations generated by each active Labeler for each capture separately, as well as extra metadata that describe the state of each active sensor that is present in the scene. Each array entry in the contains the following key-value pairs:

id: UUID of the capture. sequence_id: UUID of the sequence. step: Index of the capture within a sequence. timestamp: Timestamp (in ms) since the beginning of a sequence. sensor: Properties of the sensor. This entry contains a collection with the following key-value pairs:

sensor_id: Sensor UUID. ego_id: Ego UUID. modality: Modality of the sensor (e.g., camera, radar). translation: 3D vector that describes the sensor's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable that describes the sensor's orientation with respect to the ego coordinate system. camera_intrinsic: matrix containing (if it exists) the camera's intrinsic calibration. projection: Projection type used by the camera (e.g., orthographic, perspective). ego: Attributes of the ego. This entry contains a collection with the following key-value pairs:

ego_id: Ego UUID. translation: 3D vector that describes the ego's position (in meters) with respect to the global coordinate system. rotation: Quaternion variable containing the ego's orientation. velocity: 3D vector containing the ego's velocity (in meters per second). acceleration: 3D vector containing the ego's acceleration (in ). format: Format of the file captured by the sensor (e.g., PNG, JPG). annotations: Key-value pair collections, one for each active Labeler. These key-value pairs are as follows:

id: Annotation UUID . annotation_definition: Integer identifier of the annotation's definition. filename: Name of the file generated by the Labeler. This entry is only present for Labelers that generate an image. values: List of key-value pairs containing annotation data for the current Labeler.

Each Labeler generates different annotation specifications in the values key-value pair:

BoundingBox2DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. x: Position of the 2D bounding box on the X axis. y: Position of the 2D bounding box position on the Y axis. width: Width of the 2D bounding box. height: Height of the 2D bounding box. BoundingBox3DLabeler:

label_id: Integer identifier of a label. label_name: String identifier of a label. instance_id: UUID of one instance of an object. Each object with the same label that is visible on the same capture has different instance_id values. translation: 3D vector containing the location of the center of the 3D bounding box with respect to the sensor coordinate system (in meters). size: 3D vector containing the size of the 3D bounding box (in meters) rotation: Quaternion variable containing the orientation of the 3D bounding box. velocity: 3D vector containing the velocity of the 3D bounding box (in meters per second). acceleration: 3D vector containing the acceleration of the 3D bounding box acceleration (in ). KeypointLabeler:

label_id: Integer identifier of a label. instance_id: UUID of one instance of a joint. Keypoints with the same joint label that are visible on the same capture have different instance_id values. template_id: UUID of the keypoint template. pose: Pose label for that particular capture. keypoints: Array containing the properties of each keypoint. Each keypoint that exists in the keypoint template file is one element of the array. Each entry's contents have as follows:

index: Index of the keypoint in the keypoint template file. x: Pixel coordinates of the keypoint on the X axis. y: Pixel coordinates of the keypoint on the Y axis. state: State of the keypoint.

The SemanticSegmentationLabeler does not contain a values list.

egos.json: Contains collections of key-value pairs for each ego. These include:

id: UUID of the ego. description: Description of the ego. sensors.json: Contains collections of key-value pairs for all sensors of the simulation. These include:

id: UUID of the sensor. ego_id: UUID of the ego on which the sensor is attached. modality: Modality of the sensor (e.g., camera, radar, sonar). description: Description of the sensor (e.g., camera, radar).

Image names The RGB and semantic segmentation images share the same image naming convention. However, the semantic segmentation images also contain the string Semantic_ at the beginning of their filenames. Each RGB image is named "e_h_l_d_r.jpg", where:

e denotes the id of the environment. h denotes the id of the person. l denotes the id of the lighting condition. d denotes the camera distance at which the image was captured. r denotes the camera angle at which the image was captured.

COCO is a large-scale object detection, segmentation, and captioning dataset. http://cocodataset.org COCO has several features: Object segmentation Recognition in context Superpixel stuff segmentation 330K images (>200K labeled) 1.5 million object instances 80 object categories 91 stuff categories 5 captions per image * 250,000 people with keypoints

This dataset is extracted from 2017 COCO keypoints dataset. I filtered out images with only one person in the frame for my specific project. Because it is derived from COCO keypoint dataset so it's annotation format is similar to COCO. Feel free to use it 😁.

Hand Keypoint Dataset

Overview

This dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models for hand detection and keypoint estimation. The annotations were generated using the MediaPipe library, ensuring high accuracy and consistency. The dataset is compatible with both COCO and YOLOv8 formats.

Dataset Structure

The dataset is organized as follows:

hand_keypoint_dataset/ │ ├── images/ │ ├── train/ │ ├── val/ │ ├── coco_annotation/ │ ├── train/ │ │ ├── _annotations.coco.json │ ├── val/ │ │ ├── _annotations.coco.json │
├── labels/ │ ├── train/ │ ├── val/ │ └── README.md

images: Contains all the images divided into training and validation. annotations: Contains the annotations for the images in COCO. labels: Contains the annotations for the images in YOLO formats.

Keypoints

The dataset includes keypoints for hand detection. The keypoints are annotated as follows:

1. Wrist
2. Thumb (4 points)
3. Index finger (4 points)
4. Middle finger (4 points)
5. Ring finger (4 points)
6. Little finger (4 points)

Each hand has a total of 21 keypoints.

Usage

COCO Format

To use the dataset with COCO-compatible models, you can directly load the JSON files using COCO APIs available in various deep learning frameworks.

YOLOv8 Format

For YOLOv8, ensure you have the required environment set up. You can use the provided text files to train YOLOv8 models by specifying the dataset path in your configuration file.

Credits

We would like to thank the following sources for providing the images used in this dataset:

https://sites.google.com/view/11khands https://www.kaggle.com/datasets/ritikagiridhar/2000-hand-gestures https://www.kaggle.com/datasets/imsparsh/gesture-recognition

The images were collected and used under the respective licenses provided by each platform.

For any questions or issues, please contact its.riondsilva@gmail.com

Thank you for using the Hand Keypoint Dataset!

Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.

Relevant computer vision tasks:

• bounding box detection
• classification
• instance segmentation
• keypoint estimation
• 3D bounding box estimation
• 3D voxel reconstruction
• 3D reconstruction

The dataset is for academic research use only, since it uses resources with restrictive licenses.
For a detailed description of how the resources are used, we refer to our paper and project page.

Licenses of the resources in detail:

• Google Scanned Objects: CC BY 4.0 (for details on which files are used, see the respective meta folder)
• Cardboard Dataset: CC BY 4.0
• Shipping Label Dataset: CC BY-NC 4.0
• Other Labels: See file misc/source_urls.json
• LDR Dataset: License for Non-Commercial Use
• Large Logo Dataset (LLD): Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.

You can use our textureless models (i.e. the obj files) of damaged parcels under CC BY 4.0 (note that this does not apply to the textures).

If you use this resource for scientific research, please consider citing

@inproceedings{naumannParcel3DShapeReconstruction2023,
  author  = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai},
  title   = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month   = {June},
  year   = {2023},
  pages   = {4402-4412}
}

Garlic Keypoint Detection dataset

This dataset contains 1000 images of a single garlic clove in a pressumably industrial setting. The annotations are coco-formatted and are composed of a bounding box and 2 keypoints: head and tail. The dataset was taken from https://universe.roboflow.com/gesture-recognition-dsn2n/garlic_keypoint/dataset/1. Refer to the original repo for licensing questions. The annotations json files were slightly modified (formatting, image base directory,..)… See the full description on the dataset page: https://huggingface.co/datasets/tlpss/roboflow-garlic.

Ear acupoint key point detection data set, MS COCO format, divided into training set and test set, and written a sample config configuration file for openMMLab mmPose and mmDet Markers: Zhang Zihao, Tian Wenbo

耳朵穴位关键点检测数据集，MS COCO格式，划分好了训练集和测试集，并写好了样例config配置文件 链接: https://pan.baidu.com/s/1swTLpArj7XEDXW4d0lo7Mg 提取码: 741p 标注人：张子豪、田文博

I share this dataset for the openMMLab 2rd AI Camp.