DeepFashion2
2D Box
Classification
2D Polygon
2D Keypoints
Fashion
|...
许可协议: Unknown

Overview

DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers. It totally has 801K clothing clothing items, where each item in an image is labeled with scale, occlusion, zoom-in, viewpoint, category, style, bounding box, dense landmarks and per-pixel mask.There are also 873K Commercial-Consumer clothes pairs.The dataset is split into a training set (391K images), a validation set (34k images), and a test set (67k images).

Data Format

Training images: train/image Training annotations: train/annos

Validation images: validation/image Validation annotations: validation/annos

Test images: test/image

Each image in seperate image set has a unique six-digit number such as 000001.jpg. A corresponding annotation file in json format is provided in annotation set such as 000001.json. We provide code to generate coco-type annotations from our dataset in deepfashion2_to_coco.py. Please note that during evaluation, image_id is the digit number of the image name. (For example, the image_id of image 000001.jpg is 1). Json files in json_for_validation and json_for_test are generated based on the above rule using deepfashion2_to_coco.py. In this way, you can generate groundtruth json files for evaluation for clothes detection task and clothes segmentation task, which are not listed in DeepFashion2 Challenge.

In validation set, we provide image-level information in keypoints_val_information.json, retrieval_val_consumer_information.json and retrieval_val_shop_information.json. ( In validation set, the first 10844 images are from consumers and the last 20681 images are from shops.) For clothes detection task and clothes segmentation task, which are not listed in DeepFashion2 Challenge, keypoints_val_information.json can also be used.

We provide keypoints_val_vis.json, keypoints_val_vis_and_occ.json, val_query.json and val_gallery.json for evaluation of validation set. You can get validation score locally using Evaluation Code and above json files. You can also submit your results to evaluation server in our DeepFashion2 Challenge.

In test set, we provide image-level information in keypoints_test_information.json, retrieval_test_consumer_information.json and retrieval_test_shop_information.json.( In test set, the first 20681 images are from consumers and the last 41948 images are from shops.) You need submit your results to evaluation server in our DeepFashion2 Challenge.

Citation

If you use the DeepFashion2 dataset in your work, please cite it as:

@article{DeepFashion2,
  author = {Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and
Ping Luo},
  title={A Versatile Benchmark for Detection, Pose Estimation, Segmentation and
Re-Identification of Clothing Images},
  journal={CVPR},
  year={2019}
}
数据概要
数据格式
Image,
数据量
448.234K
文件大小
11.6GB
发布方
The Chinese University of Hong Kong
The Chinese University of Hong Kong is a comprehensive research university offering a variety of bachelor, master and doctoral programs.
数据集反馈
| 290 | 数据量 448.234K | 大小 11.6GB
DeepFashion2
2D Box Classification 2D Polygon 2D Keypoints
Fashion
许可协议: Unknown

Overview

DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers. It totally has 801K clothing clothing items, where each item in an image is labeled with scale, occlusion, zoom-in, viewpoint, category, style, bounding box, dense landmarks and per-pixel mask.There are also 873K Commercial-Consumer clothes pairs.The dataset is split into a training set (391K images), a validation set (34k images), and a test set (67k images).

Data Format

Training images: train/image Training annotations: train/annos

Validation images: validation/image Validation annotations: validation/annos

Test images: test/image

Each image in seperate image set has a unique six-digit number such as 000001.jpg. A corresponding annotation file in json format is provided in annotation set such as 000001.json. We provide code to generate coco-type annotations from our dataset in deepfashion2_to_coco.py. Please note that during evaluation, image_id is the digit number of the image name. (For example, the image_id of image 000001.jpg is 1). Json files in json_for_validation and json_for_test are generated based on the above rule using deepfashion2_to_coco.py. In this way, you can generate groundtruth json files for evaluation for clothes detection task and clothes segmentation task, which are not listed in DeepFashion2 Challenge.

In validation set, we provide image-level information in keypoints_val_information.json, retrieval_val_consumer_information.json and retrieval_val_shop_information.json. ( In validation set, the first 10844 images are from consumers and the last 20681 images are from shops.) For clothes detection task and clothes segmentation task, which are not listed in DeepFashion2 Challenge, keypoints_val_information.json can also be used.

We provide keypoints_val_vis.json, keypoints_val_vis_and_occ.json, val_query.json and val_gallery.json for evaluation of validation set. You can get validation score locally using Evaluation Code and above json files. You can also submit your results to evaluation server in our DeepFashion2 Challenge.

In test set, we provide image-level information in keypoints_test_information.json, retrieval_test_consumer_information.json and retrieval_test_shop_information.json.( In test set, the first 20681 images are from consumers and the last 41948 images are from shops.) You need submit your results to evaluation server in our DeepFashion2 Challenge.

Citation

If you use the DeepFashion2 dataset in your work, please cite it as:

@article{DeepFashion2,
  author = {Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and
Ping Luo},
  title={A Versatile Benchmark for Detection, Pose Estimation, Segmentation and
Re-Identification of Clothing Images},
  journal={CVPR},
  year={2019}
}
数据集反馈
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