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Browse files- data-00000-of-00001.arrow +3 -0
- dataset_info.json +64 -0
- state.json +16 -0
data-00000-of-00001.arrow
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version https://git-lfs.github.com/spec/v1
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oid sha256:f093db726e1e40322ef296ee8d8ec5cede8555762f90529d01e61e0ccfd4743b
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size 285153288
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dataset_info.json
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{
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"builder_name": "scene_parse_150",
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"citation": "@inproceedings{zhou2017scene,\n title={Scene Parsing through ADE20K Dataset},\n author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},\n booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n year={2017}\n}\n\n@article{zhou2016semantic,\n title={Semantic understanding of scenes through the ade20k dataset},\n author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},\n journal={arXiv preprint arXiv:1608.05442},\n year={2016}\n}\n",
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"config_name": "instance_segmentation",
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"dataset_name": "scene_parse_150",
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"dataset_size": 1162541464,
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"description": "Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed.\nMIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing.\nThe data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts.\nSpecifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing.\nThere are totally 150 semantic categories included for evaluation, which include stuffs like sky, road, grass, and discrete objects like person, car, bed.\nNote that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene.\n",
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"download_checksums": {
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"http://sceneparsing.csail.mit.edu/data/ChallengeData2017/images.tar": {
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"num_bytes": 892088320,
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"checksum": null
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},
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"http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar": {
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"num_bytes": 90398720,
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"checksum": null
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},
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"http://sceneparsing.csail.mit.edu/data/ChallengeData2017/release_test.tar": {
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"num_bytes": 214906880,
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"checksum": null
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}
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},
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"download_size": 1197393920,
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"features": {
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"image": {
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"_type": "Image"
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},
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"annotation": {
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"_type": "Image"
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}
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},
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"homepage": "http://sceneparsing.csail.mit.edu/",
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"license": "BSD 3-Clause License",
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"size_in_bytes": 2359935384,
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"splits": {
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"train": {
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"name": "train",
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"num_bytes": 862611496,
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"num_examples": 20210,
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"shard_lengths": [
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12000,
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8210
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],
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"dataset_name": "scene_parse_150"
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},
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"test": {
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"name": "test",
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"num_bytes": 212427690,
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"num_examples": 3352,
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"dataset_name": "scene_parse_150"
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},
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"validation": {
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"name": "validation",
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"num_bytes": 87502278,
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"num_examples": 2000,
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"dataset_name": "scene_parse_150"
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}
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},
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"version": {
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"version_str": "1.0.0",
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"major": 1,
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"minor": 0,
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"patch": 0
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}
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}
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state.json
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{
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"_data_files": [
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{
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"filename": "data-00000-of-00001.arrow"
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}
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],
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"_fingerprint": "54f7aa9eda565258",
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"_format_columns": [
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"image",
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"annotation"
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],
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"_format_kwargs": {},
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"_format_type": null,
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"_output_all_columns": false,
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"_split": null
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}
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