--- pretty_name: Objects365 80-Class Image Data task_categories: - object-detection size_categories: - 1M **Important:** This repository/dataset is a derived 80-class organization of Objects365 data. It is not the official Objects365 distribution, and the directory structure and JSONL metadata described below are specific to this dataset organization. For information about the original dataset, see the official Objects365 project and publication [1, 2]. --- # Image Data Organization This document describes the physical organization of the image data used by this dataset. The image storage is intentionally separated from the label metadata. Labels are stored as JSONL files, while the actual image files are organized into split-specific directories and smaller `patch` subdirectories. The `patch` structure described below is a storage convention used by this dataset and should not be interpreted as part of the official Objects365 annotation specification. ## 1. Directory Structure The image dataset is divided into two main splits: - `train/` — training images - `val/` — validation images Each split is further divided into multiple `patch` directories. Each patch contains a subset of the images belonging to that split. ```text images/ ├── train/ │ ├── patch0/ │ │ ├── image_a.jpg │ │ ├── image_b.jpg │ │ └── ... │ ├── patch1/ │ │ ├── image_c.jpg │ │ └── ... │ ├── patch2/ │ │ └── ... │ └── ... └── val/ ├── patch0/ │ ├── image_x.jpg │ └── ... ├── patch1/ │ └── ... ├── patch2/ │ └── ... └── ... ``` The exact number of patch directories may vary. The loader should therefore rely on the path metadata rather than assuming a fixed patch range. ## 2. Why Images Are Split into Patches Instead of placing every image from a split into one very large directory, images are distributed across multiple `patch` directories. This organization provides several practical advantages: - avoids extremely large single directories; - makes file management and transfer easier; - keeps the physical dataset structure manageable at large scale; - allows the loader to locate an image directly from its recorded relative path. The patch directory itself has no semantic meaning. It is only a physical storage partition. ## 3. Physical Image Path Metadata The label metadata contains one JSONL file that maps each image filename to its physical location inside the corresponding image split. For training data, the mapping is stored in: ```text labels/train/images_train.jsonl ``` For validation data: ```text labels/val/images_val.jsonl ``` Each line contains an independent JSON object: ```json {"image_name": "objects365_v2_00953995.jpg", "path": "patch17/objects365_v2_00953995.jpg"} ``` The `path` field is a **relative path**. It is interpreted relative to the root directory of the corresponding image split. For example: ```text image split root: images/train/ relative path: patch17/objects365_v2_00953995.jpg ``` The final physical path is: ```text images/train/patch17/objects365_v2_00953995.jpg ``` Likewise, for validation: ```text image split root: images/val/ relative path: patch3/objects365_v1_00123456.jpg ``` becomes: ```text images/val/patch3/objects365_v1_00123456.jpg ``` Therefore, the JSONL mapping does not store an absolute filesystem path and does not include the `train/` or `val/` prefix. ## 4. Relationship Between Image Metadata and Physical Files The dataset links logical image metadata to physical image files through the filename. The relationship is: ```text images_info.jsonl file_name │ │ exact filename match ▼ images_train.jsonl / images_val.jsonl image_name │ ▼ relative path │ ▼ train/patchXX/... or val/patchXX/... ``` Example: ```json // images_info.jsonl { "id": 900001, "file_name": "objects365_v2_00900001.jpg", "width": 333, "height": 500 } ``` ```json // images_train.jsonl { "image_name": "objects365_v2_00900001.jpg", "path": "patch12/objects365_v2_00900001.jpg" } ``` The loader can then resolve: ```text images/train/patch12/objects365_v2_00900001.jpg ``` This separation keeps logical metadata independent from the physical location of the image on disk. ## 5. Path Resolution in the DataLoader At runtime, the loader performs the following steps: ```text image_id │ ▼ images_info.jsonl │ └── file_name │ ▼ images_train.jsonl / images_val.jsonl │ └── relative path │ ▼ split root + relative path │ ▼ physical image file ``` Conceptually: ```python rel_path = image_path_map[file_name] image_path = os.path.join( images_root_dir, images_split_dir, rel_path, ) ``` For the training split: ```python images_split_dir = "train" ``` For the validation split: ```python images_split_dir = "val" ``` ## 6. Design Principle The dataset deliberately separates three concerns: ```text Logical image metadata └── images_info.jsonl Object annotations └── annotations.jsonl Physical image location └── images_train.jsonl / images_val.jsonl ``` The physical image directory only stores image files: ```text train/patchXX/*.jpg val/patchXX/*.jpg ``` All relationships between image IDs, filenames, labels, and physical paths are maintained through the JSONL metadata. This structure makes the dataset easier to reorganize or relocate because the physical storage layout can change independently from the annotation schema, as long as the image path mapping is updated accordingly. --- # Source Dataset The image data originates from the **Objects365** dataset. Objects365 was introduced by Shao et al. as a large-scale benchmark and pretraining dataset for object detection. The original ICCV 2019 release contains 365 object categories, approximately 638,000 images, and more than 10 million bounding-box annotations [1]. The official Objects365 project maintains information about subsequent dataset releases, downloads, licensing, and dataset statistics [2, 3]. This 80-class dataset is a derived subset/reorganization and should therefore be cited together with the original Objects365 publication. --- # License and Attribution The licensing terms of the original Objects365 dataset must be respected when using or redistributing derived data. According to the official Objects365 download page [3]: - Objects365 annotations and the Objects365 website are licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. - The Objects365 Consortium **does not own the copyright of the underlying images**. - Image usage remains subject to the applicable original image/Flickr terms and copyright restrictions. - Users are responsible for ensuring that their use of the images complies with the relevant copyright and licensing requirements. - The official Objects365 terms state restrictions concerning redistribution of the original images. Therefore, inclusion of metadata or transformed annotations in this dataset does **not** transfer ownership or copyright of the source images. Users intending to redistribute, publish, commercially use, or otherwise repurpose the image data should independently verify that their intended use complies with the original Objects365 terms and the rights associated with the individual images. See the official Objects365 license/download page for the authoritative licensing terms [3]. --- # Citation If this dataset is used in research, please cite the original Objects365 publication: ```bibtex @inproceedings{shao2019objects365, author = {Shuai Shao and Zeming Li and Tianyuan Zhang and Chao Peng and Gang Yu and Xiangyu Zhang and Jing Li and Jian Sun}, title = {Objects365: A Large-Scale, High-Quality Dataset for Object Detection}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year = {2019}, pages = {8430--8439} } ``` If results specifically depend on the 80-class filtering, metadata conversion, JSONL representation, or patch-based image layout provided by this dataset, the derived dataset should also be identified separately in the experimental setup so that the preprocessing pipeline can be reproduced. --- # References [1] S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun, **“Objects365: A Large-Scale, High-Quality Dataset for Object Detection,”** *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)*, 2019, pp. 8430–8439. https://openaccess.thecvf.com/content_ICCV_2019/html/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.html [2] Objects365 Consortium, **Objects365 Dataset — Official Website.** https://www.objects365.org/ [3] Objects365 Consortium, **Objects365 Dataset — Download and License Information.** https://www.objects365.org/download.html