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Segmented ImageNet-1K Subset
A subset of ImageNet-1K that has instance segmentation annotations (classes, boxes, and masks), originally intended for use by the ViT Prisma Library.
The annotations were autogenerated by Grounded Segment Anything.
The total size of the dataset is 12,000 images: 10,000 from ImageNet-1K train and 1,000 each from test and val.
Organization
Images are organized in the same structure as ImageNet-1K:
images/
train_images/
val_images/
test_images/
The train and val ImageNet classes can be identified from the filenames. See imagenet-1k classes.
Annotations are stored in a similar manner:
annotations/
train_annotations/
masks/
boxes/
val_annotations/
masks/
boxes/
test_annotations/
masks/
boxes/
masks contains .npy files (shape NUM_BOXES, H, W), and boxes contains .json files organized as follows:
{
"classes": ["aquarium", "goldfish", "goldfish"],
"boxes": [[3, 2, 1160, 564], [1, 131, 447, 547], [3, 2, 1159, 564]],
"scores": [0.48, 0.4, 0.27]
}
The filenames match the corresponding images.
Citation
Please consider citing this dataset if used in your research:
@misc{segmented_imagenet1k_subset_2024,
author = {ViT-Prisma Contributors},
title = {Segmented ImageNet-1k Subset},
url = {https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset},
version = {1.0.0},
date = {2024-04-02},
}
Grounded Segment Anything and Imagenet can be cited as follows:
@software{grounded_segment_anything,
author = {Grounded-SAM Contributors},
title = {Grounded-Segment-Anything},
url = {https://github.com/IDEA-Research/Grounded-Segment-Anything},
version = {1.2.0},
date = {2023-04-06},
license = {Apache-2.0},
message = {If you use this software, please cite it as below.}
}
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}