Tran1312/object365-labels_80Cls

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.

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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

@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.

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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

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:

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:

{"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:

{"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:

{"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.

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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:

{"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:

{"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:

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.

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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.

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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.

@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/

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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.