| --- |
| pretty_name: Objects365 80-Class Object Detection Subset |
| task_categories: |
| - object-detection |
| size_categories: |
| - 1M<n<10M |
| source_datasets: |
| - Objects365 |
| annotations_creators: |
| - found |
| license: cc-by-4.0 |
| tags: |
| - image |
| - computer-vision |
| - object-detection |
| - bounding-boxes |
| - objects365 |
| - coco-format |
| - jsonl |
| - long-tail |
| - pretraining |
| - 80-classes |
| --- |
| |
| # Objects365 80-Class Object Detection Subset |
|
|
| ## Dataset Description |
|
|
| This dataset is a filtered **80-class subset of Objects365** prepared for large-scale object-detection pretraining and training. |
|
|
| The original Objects365 dataset contains **365 object categories**, more than **600,000 training images**, and over **10 million manually annotated bounding boxes**. This derived version retains 80 target classes and reorganizes the corresponding metadata and annotations into JSON Lines (`.jsonl`) files for large-scale sequential and random-access processing. |
|
|
| The dataset card focuses on the **data itself**: provenance, statistics, directory organization, schemas, class definitions, annotation representation, and licensing. |
|
|
| The original Objects365 dataset should be cited whenever this derived subset is used in research. |
|
|
| --- |
|
|
| ## Source Dataset |
|
|
| This dataset is derived from: |
|
|
| **Objects365: A Large-Scale, High-Quality Dataset for Object Detection** |
|
|
| - Authors: Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, Jian Sun |
| - Venue: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 |
| - Pages: 8430–8439 |
| - Official project: https://www.objects365.org/ |
| - Paper: https://openaccess.thecvf.com/content_ICCV_2019/html/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.html |
| |
| ### Citation |
| |
| ```bibtex |
| @inproceedings{Shao_2019_ICCV, |
| author = {Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian}, |
| title = {Objects365: A Large-Scale, High-Quality Dataset for Object Detection}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| month = {October}, |
| year = {2019}, |
| pages = {8430--8439} |
| } |
| ``` |
| |
| Please cite the original Objects365 paper rather than treating this 80-class reorganization as an independently collected image dataset. |
| |
| --- |
| |
| ## Dataset Derivation |
| |
| The dataset is an **extended/filtered derivative** of Objects365. |
| |
| The transformation consists primarily of: |
| |
| 1. selecting 80 target object categories from the original Objects365 label space; |
| 2. retaining image metadata associated with the selected categories; |
| 3. retaining and reorganizing corresponding bounding-box annotations; |
| 4. converting large metadata structures into JSONL files; |
| 5. creating explicit image-name-to-path mappings for locally stored image patches; |
| 6. optionally storing class-frequency / sampling metadata separately from the original annotations. |
| |
| No claim is made that the underlying images were created or owned by the maintainers of this derived dataset. |
| |
| --- |
| |
| ## Directory Structure |
| |
| ```text |
| labels/ |
| ├── README.md |
| ├── train/ |
| │ ├── annotations.jsonl |
| │ ├── categories.jsonl |
| │ ├── class_sampling.jsonl |
| │ ├── images_info.jsonl |
| │ └── images_train.jsonl |
| └── val/ |
| ├── annotations.jsonl |
| ├── categories.jsonl |
| ├── images_info.jsonl |
| └── images_val.jsonl |
| ``` |
| |
| ### Dataset Statistics |
|
|
| | Split | File | Records | Approx. size | Purpose | |
| | :--- | :--- | ---: | ---: | :--- | |
| | **train** | `annotations.jsonl` | 15,538,897 | 2.64 GB | Bounding boxes and annotation attributes | |
| | | `categories.jsonl` | 80 | 2.4 KB | Definition of the retained object classes | |
| | | `class_sampling.jsonl` | 80 | 2.5 KB | Per-class sampling metadata | |
| | | `images_info.jsonl` | 1,652,206 | 177.93 MB | Image IDs, dimensions, names, licenses, and URLs | |
| | | `images_train.jsonl` | 1,742,289 | 150.87 MB | Image-name to physical-path mappings | |
| | **val** | `annotations.jsonl` | 442,988 | 75.53 MB | Validation bounding boxes and labels | |
| | | `categories.jsonl` | 80 | 2.4 KB | Validation class definitions | |
| | | `images_info.jsonl` | 67,749 | 7.30 MB | Validation image metadata | |
| | | `images_val.jsonl` | 80,000 | 6.93 MB | Validation image-path mappings | |
|
|
| `images_train.jsonl` may contain more physical image records than `images_info.jsonl` because not every downloaded image contains one of the retained 80 categories after filtering. |
|
|
| --- |
|
|
| ## Data Organization |
|
|
| Metadata is stored primarily in **JSON Lines (`.jsonl`)** format. |
|
|
| Each non-empty line contains one independent JSON object. |
|
|
| This representation is useful for very large annotation collections because individual records can be scanned, filtered, sharded, or indexed without deserializing one monolithic JSON object. |
|
|
| The principal relations are: |
|
|
| ```text |
| categories.jsonl |
| │ |
| └── id |
| │ |
| ▼ |
| annotations.jsonl |
| │ |
| ├── category_id |
| └── image_id |
| │ |
| ▼ |
| images_info.jsonl |
| │ |
| └── file_name |
| │ |
| ▼ |
| images_train.jsonl / images_val.jsonl |
| ``` |
|
|
| --- |
|
|
| ## Data Schemas |
|
|
| ### `categories.jsonl` |
|
|
| Defines the retained **80-class object vocabulary**. |
|
|
| Fields: |
|
|
| - `id` (`int`): raw category identifier. |
| - `name` (`str`): category name. |
|
|
| Example: |
|
|
| ```json |
| {"name": "Person", "id": 0} |
| {"name": "Chair", "id": 1} |
| {"name": "Sneakers", "id": 2} |
| ``` |
|
|
| --- |
|
|
| ### `images_info.jsonl` |
| |
| Stores metadata for labeled images. |
| |
| Fields: |
| |
| - `id` (`int`): unique image identifier. |
| - `file_name` (`str`): original image filename. |
| - `width` (`int`): original image width. |
| - `height` (`int`): original image height. |
| - `license` (`int`): license identifier inherited from the source metadata. |
| - `url` (`str`): source URL when available. |
|
|
| Example: |
|
|
| ```json |
| {"height": 512, "id": 420917, "license": 5, "width": 769, "file_name": "objects365_v1_00420917.jpg", "url": ""} |
| ``` |
|
|
| The `license` field is source metadata and should not be interpreted, by itself, as granting new rights over the underlying image. |
|
|
| --- |
|
|
| ### `images_train.jsonl` and `images_val.jsonl` |
|
|
| These files map image filenames to their relative physical storage paths. |
|
|
| Fields: |
|
|
| - `image_name` (`str`): image filename. |
| - `path` (`str`): relative path to the image file. |
|
|
| Example: |
|
|
| ```json |
| {"image_name": "objects365_v2_00953995.jpg", "path": "patch17/objects365_v2_00953995.jpg"} |
| ``` |
|
|
| These path files are storage metadata for this dataset organization and are not original Objects365 annotations. |
|
|
| --- |
|
|
| ### `annotations.jsonl` |
|
|
| Stores object-detection annotations. |
|
|
| Fields: |
|
|
| - `id` (`int`): annotation identifier. |
| - `image_id` (`int`): associated image identifier. |
| - `category_id` (`int`): associated object category. |
| - `bbox` (`list[float]`): COCO-style bounding box `[x_min, y_min, width, height]`. |
| - `area` (`float`): bounding-box area. |
| - `iscrowd` (`int`): crowd-region flag. |
| - `isfake` (`int`): synthetic/drawn-object flag. |
| - `isreflected` (`int`): reflection-related metadata. |
| - `flag_reflected` (`int`): auxiliary reflection-related metadata. |
|
|
| Example: |
|
|
| ```json |
| {"id": 26899493, "iscrowd": 0, "isfake": 0, "area": 3764.58, "isreflected": 0, "bbox": [20.3, 260.25, 82.69, 45.52], "image_id": 0, "category_id": 0, "flag_reflected": 0} |
| ``` |
|
|
| --- |
|
|
| ### `class_sampling.jsonl` |
| |
| Contains auxiliary per-class sampling metadata for the training split. |
| |
| Fields: |
| |
| - `id` (`int`): category identifier. |
| - `probability` (`int` or `float`): stored sampling percentage / repeat metadata. |
| |
| Example: |
| |
| ```json |
| {"id": 0, "probability": 100} |
| {"id": 73, "probability": 239} |
| {"id": 78, "probability": 202} |
| ``` |
| |
| This file is not part of the original Objects365 annotation format; it is derived metadata associated with this 80-class subset. |
| |
| --- |
| |
| ## 11. List of 80 Object Classes |
| |
| | ID | Class Name | ID | Class Name | ID | Class Name | ID | Class Name | |
| | : |
| |
| --- |
| |
| ## Provenance |
| |
| The data lineage is: |
| |
| ```text |
| Objects365 |
| │ |
| ├── original images |
| ├── image metadata |
| ├── 365-category label space |
| └── bounding-box annotations |
| │ |
| ▼ |
| 80-class selection |
| │ |
| ▼ |
| metadata / annotation filtering |
| │ |
| ▼ |
| JSONL reorganization |
| │ |
| ▼ |
| Objects365 80-Class Object Detection Subset |
| ``` |
| |
| The derived dataset changes the organization and retained label space but does not alter the provenance of the original images. |
| |
| --- |
| |
| ## License and Copyright |
| |
| ### Objects365 annotations and website |
| |
| The official Objects365 project states that its **annotations and website are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0)**. |
| |
| Accordingly, annotation-derived metadata in this repository should retain attribution to Objects365. |
| |
| CC BY 4.0: |
| |
| https://creativecommons.org/licenses/by/4.0/ |
| |
| ### Underlying images |
| |
| The **CC BY 4.0 license does not automatically apply to the underlying images**. |
| |
| The Objects365 Consortium explicitly states that it does **not own the copyright to the images**. Image use remains subject to the terms and copyright conditions of the original image sources and the Objects365 dataset conditions. |
| |
| The official Objects365 download page further states that users must accept responsibility for their use of copyrighted images and places restrictions on redistribution of those images. |
| |
| Therefore: |
| |
| - `license: cc-by-4.0` in this dataset card should be interpreted as applying to the Objects365 annotation-derived content and associated metadata where applicable; |
| - it must **not** be interpreted as relicensing third-party images under CC BY 4.0; |
| - redistribution of the underlying image files should be evaluated separately against the Objects365 terms and the rights of the original image owners. |
| |
| Official Objects365 license / download page: |
| |
| https://www.objects365.org/download.html |
| |
| ### Derived metadata |
| |
| Files generated specifically for this reorganization, such as image-path mappings or class-selection metadata, may be distributed separately by the maintainers, but they do not change the legal status of the underlying Objects365 images or annotations. |
| |
| --- |
| |
| ## Attribution |
| |
| When using this dataset, please acknowledge that it is derived from Objects365 and cite the original paper: |
| |
| > Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian 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. |
| |
| ```bibtex |
| @inproceedings{Shao_2019_ICCV, |
| author = {Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian}, |
| title = {Objects365: A Large-Scale, High-Quality Dataset for Object Detection}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| month = {October}, |
| year = {2019}, |
| pages = {8430--8439} |
| } |
| ``` |
| |
| --- |
| |
| ## References |
| |
| 1. **Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., Sun, J.** |
| *Objects365: A Large-Scale, High-Quality Dataset for Object Detection.* ICCV 2019. |
| 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 Official Project** |
| https://www.objects365.org/ |
|
|
| 3. **Objects365 Download and License Terms** |
| https://www.objects365.org/download.html |
|
|
| 4. **Creative Commons Attribution 4.0 International** |
| https://creativecommons.org/licenses/by/4.0/ |
|
|
| --- |
|
|
| ## Notes |
|
|
| - This is a **filtered 80-class derivative**, not the complete 365-class Objects365 dataset. |
| - The original Objects365 paper and project remain the authoritative sources for the parent dataset. |
| - Annotation provenance should be preserved when redistributing derived label files. |
| - Image copyright is distinct from annotation licensing. |
| - The presence of an image in Objects365 does not imply that the image itself is licensed under CC BY 4.0. |
