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{2022},\ncopyright = {arXiv.org perpetual, non-exclusive license}\n}", + "description": "ASQA is the first long-form question answering dataset that focuses on ambiguous\nfactoid questions. Different from previous long-form answers datasets, each\nquestion is annotated with both long-form answers and extractive question-answer\npairs, which should be answerable by the generated passage. A generated\nlong-form answer will be evaluated using both ROUGE and QA accuracy. We showed\nthat these evaluation metrics correlated with human judgment well. In this\nrepostory we release the ASQA dataset, together with the evaluation code:\n`https://github.com/google-research/language/tree/master/language/asqa`", + "downloadSize": "18732616", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://github.com/google-research/language/tree/master/language/asqa" + ] + }, + "moduleName": "tensorflow_datasets.datasets.asqa.asqa_dataset_builder", + "name": "asqa", + "releaseNotes": { + "1.0.0": "Initial release.", + "2.0.0": "Sample ID goes from int32 (overflowing) to int64." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "11803538", + "shardLengths": [ + "4353" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "dev", + "numBytes": "3416540", + "shardLengths": [ + "948" + ] + } + ], + "version": "2.0.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/asqa/2.0.0/features.json 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b/tensorflow_datasets/testing/metadata/asu_table_top_converted_externally_to_rlds/0.1.0/dataset_info.json new file mode 100644 index 00000000000..e14dbad54ab --- /dev/null +++ b/tensorflow_datasets/testing/metadata/asu_table_top_converted_externally_to_rlds/0.1.0/dataset_info.json @@ -0,0 +1,33 @@ +{ + "citation": "@inproceedings{zhou2023modularity,\n title={Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation},\n author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Stepputtis, Simon and Amor, Heni},\n booktitle={Conference on Robot Learning},\n pages={1684--1695},\n year={2023},\n organization={PMLR}\n}\n@article{zhou2023learning,\n title={Learning modular language-conditioned robot policies through attention},\n author={Zhou, Yifan and Sonawani, Shubham and Phielipp, Mariano and Ben Amor, Heni and Stepputtis, Simon},\n journal={Autonomous Robots},\n pages={1--21},\n year={2023},\n 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Each conversation is rated with five safety top-level categories and one overall comprehension question of the conversation. Raters were recruited were balanced by gender (man or woman), race/ethnicity (White, Black, Latine, Asian, Multiracial) and each rater rated all items. Each rater rated all conversations. Each conversation has 123 unique ratings. Total number of rows in this dataset is 43050.", + "configName": "350", + "description": "# The Diversity in Conversational AI Evaluation for Safety (**DICES**) dataset\n\nMachine learning approaches are often trained and evaluated with datasets that\nrequire a clear separation between positive and negative examples. This approach\noverly simplifies the natural subjectivity present in many tasks and content\nitems. It also obscures the inherent diversity in human perceptions and\nopinions. Often tasks that attempt to preserve the variance in content and\ndiversity in humans are quite expensive and laborious. To fill in this gap and\nfacilitate more in-depth model performance analyses we propose the DICES dataset\n- a unique dataset with diverse perspectives on safety of AI generated\nconversations. We focus on the task of safety evaluation of conversational AI\nsystems. The DICES dataset contains detailed demographics information about each\nrater, extremely high replication of unique ratings per conversation to ensure\nstatistical significance of further analyses and encodes rater votes as\ndistributions across different demographics to allow for in-depth explorations\nof different rating aggregation strategies.\n\nThis dataset is well suited to observe and measure variance, ambiguity and\ndiversity in the context of safety of conversational AI. The dataset is\naccompanied by a paper describing a set of metrics that show how rater diversity\ninfluences the safety perception of raters from different geographic regions,\nethnicity groups, age groups and genders. The goal of the DICES dataset is to be\nused as a shared benchmark for safety evaluation of conversational AI systems.\n\n**CONTENT WARNING**: This dataset contains adversarial examples of conversations\nthat may be offensive.", + "downloadSize": "31143485", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://github.com/google-research-datasets/dices-dataset" + ] + }, + "moduleName": "tensorflow_datasets.datasets.dices.dices_dataset_builder", + "name": "dices", + "releaseNotes": { + "1.0.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "78045224", + "shardLengths": [ + "43050" + ] + } + ], + "version": "1.0.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/dices/350/1.0.0/features.json b/tensorflow_datasets/testing/metadata/dices/350/1.0.0/features.json new file mode 100644 index 00000000000..d7973fc8089 --- /dev/null +++ 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Parrish, Alicia and Serapio-Garc{\\'\\i}a, Gregory and Prabhakaran, Vinodkumar and Wang, Ding},\n journal={Advances in Neural Information Processing Systems},\n volume={36},\n year={2024}\n}", + "configDescription": "Dataset 990 contains 990 conversations rated by a diverse rater pool of 173 unique raters. Each conversation is rated with three safety top-level categories and one overall conversation comprehension question. Raters were recruited so that the number of raters for each conversation was balanced by gender (Man, Woman) and locale (US, India). Each rater rated only a sample of the conversation. Each conversation has 60-70 unique ratings. Total number of rows in this dataset is 72103.", + "configName": "990", + "description": "# The Diversity in Conversational AI Evaluation for Safety (**DICES**) dataset\n\nMachine learning approaches are often trained and evaluated with datasets that\nrequire a clear separation between positive and negative examples. This approach\noverly simplifies the natural subjectivity present in many tasks and content\nitems. It also obscures the inherent diversity in human perceptions and\nopinions. Often tasks that attempt to preserve the variance in content and\ndiversity in humans are quite expensive and laborious. To fill in this gap and\nfacilitate more in-depth model performance analyses we propose the DICES dataset\n- a unique dataset with diverse perspectives on safety of AI generated\nconversations. We focus on the task of safety evaluation of conversational AI\nsystems. The DICES dataset contains detailed demographics information about each\nrater, extremely high replication of unique ratings per conversation to ensure\nstatistical significance of further analyses and encodes rater votes as\ndistributions across different demographics to allow for in-depth explorations\nof different rating aggregation strategies.\n\nThis dataset is well suited to observe and measure variance, ambiguity and\ndiversity in the context of safety of conversational AI. The dataset is\naccompanied by a paper describing a set of metrics that show how rater diversity\ninfluences the safety perception of raters from different geographic regions,\nethnicity groups, age groups and genders. 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a/tensorflow_datasets/testing/metadata/dlr_edan_shared_control_converted_externally_to_rlds/0.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/dlr_edan_shared_control_converted_externally_to_rlds/0.1.0/dataset_info.json new file mode 100644 index 00000000000..6694d56a913 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/dlr_edan_shared_control_converted_externally_to_rlds/0.1.0/dataset_info.json @@ -0,0 +1,55 @@ +{ + "citation": "@inproceedings{vogel_edan_2020,\n\ttitle = {EDAN - an EMG-Controlled Daily Assistant to Help People with Physical Disabilities},\n\tlanguage = {en},\n\tbooktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},\n\tauthor = {Vogel, J\u00f6rn and Hagengruber, Annette and Iskandar, Maged and Quere, Gabriel and Leipscher, Ulrike and Bustamante, Samuel and Dietrich, Alexander and Hoeppner, Hannes and Leidner, Daniel and Albu-Sch\u00e4ffer, Alin},\n\tyear = {2020}\n}\n@inproceedings{quere_shared_2020,\n\taddress = {Paris, France},\n\ttitle = {Shared {Control} {Templates} for {Assistive} {Robotics}},\n\tlanguage = {en},\n\tbooktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\n\tauthor = {Quere, Gabriel and Hagengruber, Annette and Iskandar, Maged and Bustamante, Samuel and Leidner, Daniel and Stulp, Freek and Vogel, Joern},\n\tyear = {2020},\n\tpages = {7},\n}", + "description": "wheelchair with arm performing shelf pick tasks", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://ieeexplore.ieee.org/document/9341156" + ] + }, + "moduleName": "tensorflow_datasets.robotics.rtx.rtx", + "name": "dlr_edan_shared_control_converted_externally_to_rlds", + "releaseNotes": { + "0.1.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "3316378421", + "shardLengths": [ + "2", + "3", + "3", + 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You can use `tf.transpose` within `ds.map` to convert the images to a human-friendlier format.", + "downloadSize": "561753746", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://www.nist.gov/itl/products-and-services/emnist-dataset" + ] + }, + "moduleName": "tensorflow_datasets.image_classification.mnist", + "name": "emnist", + "releaseNotes": { + "3.0.0": "New split API (https://tensorflow.org/datasets/splits)", + "3.1.0": "Updated broken download URL" + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "313798805", + "shardLengths": [ + "174483", + "174483", + "174483", + "174483" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + "numBytes": "52323272", + "shardLengths": [ + "116323" + ] + } + ], + "supervisedKeys": { + "tuple": { + "items": [ + { + "featureKey": "image" + }, + { + "featureKey": "label" + } + ] + } + }, + "version": "3.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/byclass/3.1.0/features.json b/tensorflow_datasets/testing/metadata/emnist/byclass/3.1.0/features.json new file mode 100644 index 00000000000..583af74ab21 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/byclass/3.1.0/features.json @@ -0,0 +1,26 @@ +{ + "featuresDict": { + "features": { + "image": { + "image": { + "dtype": "uint8", + "shape": { + "dimensions": [ + "28", + "28", + "1" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "label": { + "classLabel": { + "numClasses": "62" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/bymerge/3.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/emnist/bymerge/3.1.0/dataset_info.json new file mode 100644 index 00000000000..8bbe4a2e5ad --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/bymerge/3.1.0/dataset_info.json @@ -0,0 +1,53 @@ +{ + "citation": "@article{cohen_afshar_tapson_schaik_2017,\n title={EMNIST: Extending MNIST to handwritten letters},\n DOI={10.1109/ijcnn.2017.7966217},\n journal={2017 International Joint Conference on Neural Networks (IJCNN)},\n author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},\n year={2017}\n}", + "configDescription": "EMNIST ByMerge", + "configName": "bymerge", + "description": "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.\n\nNote: Like the original EMNIST data, images provided here are inverted horizontally and rotated 90 anti-clockwise. You can use `tf.transpose` within `ds.map` to convert the images to a human-friendlier format.", + "downloadSize": "561753746", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://www.nist.gov/itl/products-and-services/emnist-dataset" + ] + }, + "moduleName": "tensorflow_datasets.image_classification.mnist", + "name": "emnist", + "releaseNotes": { + "3.0.0": "New split API (https://tensorflow.org/datasets/splits)", + "3.1.0": "Updated broken download URL" + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "313820868", + "shardLengths": [ + "174483", + "174483", + "174483", + "174483" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + "numBytes": "52301209", + "shardLengths": [ + "116323" + ] + } + ], + "supervisedKeys": { + "tuple": { + "items": [ + { + "featureKey": "image" + }, + { + "featureKey": "label" + } + ] + } + }, + "version": "3.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/bymerge/3.1.0/features.json b/tensorflow_datasets/testing/metadata/emnist/bymerge/3.1.0/features.json new file mode 100644 index 00000000000..ca2e4c37f2d --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/bymerge/3.1.0/features.json @@ -0,0 +1,26 @@ +{ + "featuresDict": { + "features": { + "image": { + "image": { + "dtype": "uint8", + "shape": { + "dimensions": [ + "28", + "28", + "1" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "label": { + "classLabel": { + "numClasses": "47" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/digits/3.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/emnist/digits/3.1.0/dataset_info.json new file mode 100644 index 00000000000..a75c138a3dd --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/digits/3.1.0/dataset_info.json @@ -0,0 +1,50 @@ +{ + "citation": "@article{cohen_afshar_tapson_schaik_2017,\n title={EMNIST: Extending MNIST to handwritten letters},\n DOI={10.1109/ijcnn.2017.7966217},\n journal={2017 International Joint Conference on Neural Networks (IJCNN)},\n author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},\n year={2017}\n}", + "configDescription": "EMNIST Digits", + "configName": "digits", + "description": "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.\n\nNote: Like the original EMNIST data, images provided here are inverted horizontally and rotated 90 anti-clockwise. You can use `tf.transpose` within `ds.map` to convert the images to a human-friendlier format.", + "downloadSize": "561753746", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://www.nist.gov/itl/products-and-services/emnist-dataset" + ] + }, + "moduleName": "tensorflow_datasets.image_classification.mnist", + "name": "emnist", + "releaseNotes": { + "3.0.0": "New split API (https://tensorflow.org/datasets/splits)", + "3.1.0": "Updated broken download URL" + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "108148019", + "shardLengths": [ + "240000" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + "numBytes": "18021081", + "shardLengths": [ + "40000" + ] + } + ], + "supervisedKeys": { + "tuple": { + "items": [ + { + "featureKey": "image" + }, + { + "featureKey": "label" + } + ] + } + }, + "version": "3.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/digits/3.1.0/features.json b/tensorflow_datasets/testing/metadata/emnist/digits/3.1.0/features.json new file mode 100644 index 00000000000..fd8d938f217 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/digits/3.1.0/features.json @@ -0,0 +1,26 @@ +{ + "featuresDict": { + "features": { + "image": { + "image": { + "dtype": "uint8", + "shape": { + "dimensions": [ + "28", + "28", + "1" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "label": { + "classLabel": { + "numClasses": "10" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/letters/3.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/emnist/letters/3.1.0/dataset_info.json new file mode 100644 index 00000000000..6a71ddbe210 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/letters/3.1.0/dataset_info.json @@ -0,0 +1,50 @@ +{ + "citation": "@article{cohen_afshar_tapson_schaik_2017,\n title={EMNIST: Extending MNIST to handwritten letters},\n DOI={10.1109/ijcnn.2017.7966217},\n journal={2017 International Joint Conference on Neural Networks (IJCNN)},\n author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},\n year={2017}\n}", + "configDescription": "EMNIST Letters", + "configName": "letters", + "description": "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.\n\nNote: Like the original EMNIST data, images provided here are inverted horizontally and rotated 90 anti-clockwise. You can use `tf.transpose` within `ds.map` to convert the images to a human-friendlier format.", + "downloadSize": "561753746", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://www.nist.gov/itl/products-and-services/emnist-dataset" + ] + }, + "moduleName": "tensorflow_datasets.image_classification.mnist", + "name": "emnist", + "releaseNotes": { + "3.0.0": "New split API (https://tensorflow.org/datasets/splits)", + "3.1.0": "Updated broken download URL" + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "39670898", + "shardLengths": [ + "88800" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + "numBytes": "6608282", + "shardLengths": [ + "14800" + ] + } + ], + "supervisedKeys": { + "tuple": { + "items": [ + { + "featureKey": "image" + }, + { + "featureKey": "label" + } + ] + } + }, + "version": "3.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/letters/3.1.0/features.json b/tensorflow_datasets/testing/metadata/emnist/letters/3.1.0/features.json new file mode 100644 index 00000000000..6f8e7d41e05 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/letters/3.1.0/features.json @@ -0,0 +1,26 @@ +{ + "featuresDict": { + "features": { + "image": { + "image": { + "dtype": "uint8", + "shape": { + "dimensions": [ + "28", + "28", + "1" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "label": { + "classLabel": { + "numClasses": "37" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/mnist/3.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/emnist/mnist/3.1.0/dataset_info.json new file mode 100644 index 00000000000..08de61f796d --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/mnist/3.1.0/dataset_info.json @@ -0,0 +1,50 @@ +{ + "citation": "@article{cohen_afshar_tapson_schaik_2017,\n title={EMNIST: Extending MNIST to handwritten letters},\n DOI={10.1109/ijcnn.2017.7966217},\n journal={2017 International Joint Conference on Neural Networks (IJCNN)},\n author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},\n year={2017}\n}", + "configDescription": "EMNIST MNIST", + "configName": "mnist", + "description": "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.\n\nNote: Like the original EMNIST data, images provided here are inverted horizontally and rotated 90 anti-clockwise. You can use `tf.transpose` within `ds.map` to convert the images to a human-friendlier format.", + "downloadSize": "561753746", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://www.nist.gov/itl/products-and-services/emnist-dataset" + ] + }, + "moduleName": "tensorflow_datasets.image_classification.mnist", + "name": "emnist", + "releaseNotes": { + "3.0.0": "New split API (https://tensorflow.org/datasets/splits)", + "3.1.0": "Updated broken download URL" + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "27038750", + "shardLengths": [ + "60000" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + "numBytes": "4515501", + "shardLengths": [ + "10000" + ] + } + ], + "supervisedKeys": { + "tuple": { + "items": [ + { + "featureKey": "image" + }, + { + "featureKey": "label" + } + ] + } + }, + "version": "3.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/emnist/mnist/3.1.0/features.json b/tensorflow_datasets/testing/metadata/emnist/mnist/3.1.0/features.json new file mode 100644 index 00000000000..fd8d938f217 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/emnist/mnist/3.1.0/features.json @@ -0,0 +1,26 @@ +{ + "featuresDict": { + "features": { + "image": { + "image": { + "dtype": "uint8", + "shape": { + "dimensions": [ + "28", + "28", + "1" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "label": { + "classLabel": { + "numClasses": "10" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/eth_agent_affordances/0.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/eth_agent_affordances/0.1.0/dataset_info.json new file mode 100644 index 00000000000..69be3b9217a --- /dev/null +++ b/tensorflow_datasets/testing/metadata/eth_agent_affordances/0.1.0/dataset_info.json @@ -0,0 +1,79 @@ +{ + "citation": "@inproceedings{schiavi2023learning,\n title={Learning agent-aware affordances for closed-loop interaction with articulated objects},\n author={Schiavi, Giulio and Wulkop, Paula and Rizzi, Giuseppe and Ott, Lionel and Siegwart, Roland and Chung, Jen Jen},\n booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},\n pages={5916--5922},\n year={2023},\n organization={IEEE}\n}", + "description": "Franka opening ovens -- point cloud + proprio only", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://ieeexplore.ieee.org/iel7/10160211/10160212/10160747.pdf" + ] + }, + "moduleName": "tensorflow_datasets.robotics.rtx.rtx", + "name": "eth_agent_affordances", + "releaseNotes": { + "0.1.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "18546670748", + "shardLengths": [ + "1", + "3", + "1", + "3", + "1", + "6", + "2", + "3", + "3", + "2", + "4", + "2", + "3", + "3", + "1", + "2", + "2", + "5", + "3", + "3", + "2", + "1", + "2", + "2", + "1", + "3", + "1", + "2", + "3", + "3", + "1", + "2", + "2", + "2", + "2", + "3", + "2", + "2", + "1", + "3", + "1", + "1", + "1", + "1", + "1", + "2", + "1", + "2", + "1", + "4", + "3", + "3", + "2", + "2" + ] + } + ], + "version": "0.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/eth_agent_affordances/0.1.0/features.json b/tensorflow_datasets/testing/metadata/eth_agent_affordances/0.1.0/features.json new file mode 100644 index 00000000000..be5fa385f85 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/eth_agent_affordances/0.1.0/features.json @@ -0,0 +1,168 @@ +{ + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict", + "featuresDict": { + "features": { + "episode_metadata": { + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict", + "featuresDict": { + "features": { + "file_path": { + "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text", + "text": {}, + "description": "Path to the original data file." + }, + "input_point_cloud": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": { + "dimensions": [ + "10000", + "3" + ] + }, + "dtype": "float16", + "encoding": "none" + }, + "description": "Point cloud (geometry only) of the object at the beginning of the episode (world frame) as a numpy array (10000,3)." + } + } + } + }, + "steps": { + "pythonClassName": "tensorflow_datasets.core.features.dataset_feature.Dataset", + "sequence": { + "feature": { + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict", + "featuresDict": { + "features": { + "is_last": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": {}, + "dtype": "bool", + "encoding": "none" + } + }, + "observation": { + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict", + "featuresDict": { + "features": { + "input_point_cloud": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": { + "dimensions": [ + "10000", + "3" + ] + }, + "dtype": "float16", + "encoding": "none" + }, + "description": "Point cloud (geometry only) of the object at the beginning of the episode (world frame) as a numpy array (10000,3)." + }, + "image": { + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image", + "image": { + "shape": { + "dimensions": [ + "64", + "64", + "3" + ] + }, + "dtype": "uint8", + "encodingFormat": "png" + }, + "description": "Main camera RGB observation. Not available for this dataset, will be set to np.zeros." + }, + "state": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": { + "dimensions": [ + "8" + ] + }, + "dtype": "float32", + "encoding": "none" + }, + "description": "State, consists of [end-effector pose (x,y,z,yaw,pitch,roll) in world frame, 1x gripper open/close, 1x door opening angle]." + } + } + } + }, + "is_terminal": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": {}, + "dtype": "bool", + "encoding": "none" + } + }, + "discount": { + "pythonClassName": "tensorflow_datasets.core.features.scalar.Scalar", + "tensor": { + "shape": {}, + "dtype": "float32", + "encoding": "none" + }, + "description": "Discount if provided, default to 1." + }, + "is_first": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": {}, + "dtype": "bool", + "encoding": "none" + } + }, + "action": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": { + "dimensions": [ + "6" + ] + }, + "dtype": "float32", + "encoding": "none" + }, + "description": "Robot action, consists of [end-effector velocity (v_x,v_y,v_z,omega_x,omega_y,omega_z) in world frame" + }, + "reward": { + "pythonClassName": "tensorflow_datasets.core.features.scalar.Scalar", + "tensor": { + "shape": {}, + "dtype": "float32", + "encoding": "none" + }, + "description": "Reward if provided, 1 on final step for demos." + }, + "language_instruction": { + "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text", + "text": {}, + "description": "Language Instruction." + }, + "language_embedding": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "shape": { + "dimensions": [ + "512" + ] + }, + "dtype": "float32", + "encoding": "none" + }, + "description": "Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5" + } + } + } + }, + "length": "-1" + } + } + } + } +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/fmb/0.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/fmb/0.1.0/dataset_info.json new file mode 100644 index 00000000000..9086ec5961c --- /dev/null +++ b/tensorflow_datasets/testing/metadata/fmb/0.1.0/dataset_info.json @@ -0,0 +1,877 @@ +{ + "citation": "https://doi.org/10.48550/arXiv.2401.08553", + "description": "Our dataset consists of objects in diverse appearance and geometry. It requires multi-stage and multi-modal fine motor skills to successfully assemble the pegs onto a unfixed board in a randomized scene. We collected a total of 22,550 trajectories across two different tasks on a Franka Panda arm. We record the trajectories from 2 global views and 2 wrist views. 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} + } + } +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/nyu_rot_dataset_converted_externally_to_rlds/0.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/nyu_rot_dataset_converted_externally_to_rlds/0.1.0/dataset_info.json new file mode 100644 index 00000000000..a8832908f96 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/nyu_rot_dataset_converted_externally_to_rlds/0.1.0/dataset_info.json @@ -0,0 +1,26 @@ +{ + "citation": "@inproceedings{haldar2023watch,\n title={Watch and match: Supercharging imitation with regularized optimal transport},\n author={Haldar, Siddhant and Mathur, Vaibhav and Yarats, Denis and Pinto, Lerrel},\n booktitle={Conference on Robot Learning},\n pages={32--43},\n year={2023},\n organization={PMLR}\n}", + "description": "xArm short-horizon table-top tasks", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://rot-robot.github.io/" + ] + }, + "moduleName": "tensorflow_datasets.robotics.rtx.rtx", + "name": "nyu_rot_dataset_converted_externally_to_rlds", + "releaseNotes": { + "0.1.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "5593987", + "shardLengths": [ + "14" + ] + } + ], + "version": "0.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/nyu_rot_dataset_converted_externally_to_rlds/0.1.0/features.json b/tensorflow_datasets/testing/metadata/nyu_rot_dataset_converted_externally_to_rlds/0.1.0/features.json new file mode 100644 index 00000000000..f1d511844f7 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/nyu_rot_dataset_converted_externally_to_rlds/0.1.0/features.json @@ -0,0 +1,140 @@ +{ + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict", + "featuresDict": { + "features": { + "episode_metadata": { + "pythonClassName": 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See https://tfhub.dev/google/universal-sentence-encoder-large/5" + } + } + } + }, + "length": "-1" + } + } + } + } +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/oxford_iiit_pet/4.0.0/dataset_info.json b/tensorflow_datasets/testing/metadata/oxford_iiit_pet/4.0.0/dataset_info.json new file mode 100644 index 00000000000..5cb71d4000d --- /dev/null +++ b/tensorflow_datasets/testing/metadata/oxford_iiit_pet/4.0.0/dataset_info.json @@ -0,0 +1,53 @@ +{ + "citation": "@InProceedings{parkhi12a,\n author = \"Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.\",\n title = \"Cats and Dogs\",\n booktitle = \"IEEE Conference on Computer Vision and Pattern Recognition\",\n year = \"2012\",\n}", + "description": "The Oxford-IIIT pet dataset is a 37 category pet image dataset with roughly 200\nimages for each class. The images have large variations in scale, pose and\nlighting. All images have an associated ground truth annotation of breed and\nspecies. Additionally, head bounding boxes are provided for the training split,\nallowing using this dataset for simple object detection tasks. In the test\nsplit, the bounding boxes are empty.", + "downloadSize": "811092049", + "fileFormat": "array_record", + "location": { + "urls": [ + "http://www.robots.ox.ac.uk/~vgg/data/pets/" + ] + }, + "moduleName": "tensorflow_datasets.datasets.oxford_iiit_pet.oxford_iiit_pet_dataset_builder", + "name": "oxford_iiit_pet", + "releaseNotes": { + "4.0.0": "Add head bounding boxes. Fix corrupt images. Update dataset URL." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "387408912", + "shardLengths": [ + "920", + "920", + "920", + "920" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + "numBytes": "423853746", + "shardLengths": [ + "917", + "917", + "918", + "917" + ] + } + ], + "supervisedKeys": { + "tuple": { + "items": [ + { + "featureKey": "image" + }, + { + "featureKey": "label" + } + ] + } + }, + "version": "4.0.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/oxford_iiit_pet/4.0.0/features.json b/tensorflow_datasets/testing/metadata/oxford_iiit_pet/4.0.0/features.json new file mode 100644 index 00000000000..983ffe8cf40 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/oxford_iiit_pet/4.0.0/features.json @@ -0,0 +1,62 @@ +{ + "featuresDict": { + "features": { + "file_name": { + "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text", + "text": {} + }, + "head_bbox": { + "boundingBox": { + "dtype": "float32", + "shape": { + "dimensions": [ + "4" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.bounding_boxes.BBoxFeature" + }, + "image": { + "image": { + "dtype": "uint8", + "encodingFormat": "jpeg", + "shape": { + "dimensions": [ + "-1", + "-1", + "3" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "label": { + "classLabel": { + "numClasses": "37" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + }, + "segmentation_mask": { + "image": { + "dtype": "uint8", + "shape": { + "dimensions": [ + "-1", + "-1", + "1" + ] + }, + "useColormap": true + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "species": { + "classLabel": { + "numClasses": "2" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/plant_leaves/0.1.1/dataset_info.json b/tensorflow_datasets/testing/metadata/plant_leaves/0.1.1/dataset_info.json new file mode 100644 index 00000000000..c0b84e13c29 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/plant_leaves/0.1.1/dataset_info.json @@ -0,0 +1,103 @@ +{ + "citation": "@misc{,\n author={Siddharth Singh Chouhan, Ajay Kaul, Uday Pratap Singh, Sanjeev\nJain},\n title={A Database of Leaf Images: Practice towards Plant Conservation with\nPlant Pathology},\n howpublished={Mendeley Data},\n year={2019}\n}", + "description": "This dataset consists of 4502 images of healthy and unhealthy plant leaves\ndivided into 22 categories by species and state of health. The images are in\nhigh resolution JPG format.\n\nThere are no files with label prefix 0000, therefore label encoding is shifted\nby one (e.g. file with label prefix 0001 gets encoded label 0).\n\nNote: Each image is a separate download. Some might rarely fail, therefore make\nsure to restart if that happens. An exception will be raised in case one of the\ndownloads repeatedly fails.\n\nDataset URL: https://data.mendeley.com/datasets/hb74ynkjcn/1 License:\nhttp://creativecommons.org/licenses/by/4.0", + "downloadSize": "7044557764", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://data.mendeley.com/datasets/hb74ynkjcn/1" + ] + }, + "moduleName": "tensorflow_datasets.datasets.plant_leaves.plant_leaves_dataset_builder", + "name": "plant_leaves", + "releaseNotes": { + "0.1.0": "Initial release.", + "0.1.1": "Fix checksum error." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "7312339772", + "shardLengths": [ + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + "71", + "70", + "70", + 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"pythonClassName": "tensorflow_datasets.core.features.text_feature.Text", + "text": {} + }, + "label": { + "classLabel": { + "numClasses": "22" + }, + "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/plex_robosuite/0.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/plex_robosuite/0.1.0/dataset_info.json new file mode 100644 index 00000000000..76d7325ee8a --- /dev/null +++ b/tensorflow_datasets/testing/metadata/plex_robosuite/0.1.0/dataset_info.json @@ -0,0 +1,50 @@ +{ + "citation": "https://doi.org/10.48550/arXiv.2303.08789", + "description": "A dataset of high-qualty demonstration trajectories for Robosuite's Door, Stack, PickPlaceMilk, PickPlaceBread, PickPlaceCereal, and NutAssemblyRound tasks, 75 demonstrations per each.", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://microsoft.github.io/PLEX/" + ] + }, + "moduleName": "tensorflow_datasets.robotics.rtx.rtx", + "name": "plex_robosuite", + "releaseNotes": { + "0.1.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "val", + "numBytes": "144943593", + "shardLengths": [ + "29", + "19" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "1214267796", + "shardLengths": [ + "32", + "22", + "35", + "28", + "27", + "18", + "17", + "23", + "17", + "26", + "23", + "25", + "22", + "38", + "24", + "25" + ] + } + ], + "version": "0.1.0" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/plex_robosuite/0.1.0/features.json b/tensorflow_datasets/testing/metadata/plex_robosuite/0.1.0/features.json new file mode 100644 index 00000000000..7363660c102 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/plex_robosuite/0.1.0/features.json @@ -0,0 +1,154 @@ +{ + "featuresDict": { + "features": { + "episode_metadata": { + "featuresDict": { + "features": { + "episode_id": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "string", + "encoding": "none", + "shape": {} + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "steps": { + "pythonClassName": "tensorflow_datasets.core.features.dataset_feature.Dataset", + "sequence": { + "feature": { + "featuresDict": { + "features": { + "action": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "float64", + "encoding": "none", + "shape": { + "dimensions": [ + "7" + ] + } + } + }, + "discount": { + "pythonClassName": "tensorflow_datasets.core.features.scalar.Scalar", + "tensor": { + "dtype": "float32", + "encoding": "none", + "shape": {} + } + }, + "is_first": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "bool", + "encoding": "none", + "shape": {} + } + }, + "is_last": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "bool", + "encoding": "none", + "shape": {} + } + }, + "is_terminal": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "bool", + "encoding": "none", + "shape": {} + } + }, + "language_embedding": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "float32", + "encoding": "none", + "shape": { + "dimensions": [ + "512" + ] + } + } + }, + "language_instruction": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "string", + "encoding": "none", + "shape": {} + } + }, + "observation": { + "featuresDict": { + "features": { + "image": { + "image": { + "dtype": "uint8", + "encodingFormat": "jpeg", + "shape": { + "dimensions": [ + "128", + "128", + "3" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "state": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "float64", + "encoding": "none", + "shape": { + "dimensions": [ + "32" + ] + } + } + }, + "wrist_image": { + "image": { + "dtype": "uint8", + "encodingFormat": "jpeg", + "shape": { + "dimensions": [ + "128", + "128", + "3" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "reward": { + "pythonClassName": "tensorflow_datasets.core.features.scalar.Scalar", + "tensor": { + "dtype": "float32", + "encoding": "none", + "shape": {} + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "length": "-1" + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/pneumonia_mnist/1.0.0/dataset_info.json b/tensorflow_datasets/testing/metadata/pneumonia_mnist/1.0.0/dataset_info.json new file mode 100644 index 00000000000..a0c532a87c0 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/pneumonia_mnist/1.0.0/dataset_info.json @@ -0,0 +1,54 @@ +{ + "citation": "@article{yang2023medmnist,\n title={Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification},\n author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},\n journal={Scientific Data},\n volume={10},\n number={1},\n pages={41},\n year={2023},\n publisher={Nature Publishing Group UK London}\n}", + "description": "# MedMNIST Pneumonia Dataset\n\nThe PneumoniaMNIST is based on a prior dataset of 5,856 pediatric chest X-Ray\nimages. The task is binary-class classification of pneumonia against normal. The\nsource training set is split with a ratio of 9:1 into training and validation\nset, and use its source validation set as the test set. The source images are\ngray-scale, and their sizes are (384\u20132,916) \u00d7 (127\u20132,713). 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photographs of natural scenes},\n author={Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara},\n booktitle={Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)},\n pages={787--798},\n year={2014}\n}\n@inproceedings{yu2016modeling,\n title={Modeling context in referring expressions},\n author={Yu, Licheng and Poirson, Patrick and Yang, Shan and Berg, Alexander C and Berg, Tamara L},\n booktitle={European Conference on Computer Vision},\n pages={69--85},\n year={2016},\n organization={Springer}\n}\n@inproceedings{mao2016generation,\n title={Generation and Comprehension of Unambiguous Object Descriptions},\n author={Mao, Junhua and Huang, Jonathan and Toshev, Alexander and Camburu, Oana and Yuille, Alan and Murphy, Kevin},\n booktitle={CVPR},\n year={2016}\n}\n@inproceedings{nagaraja2016modeling,\n title={Modeling context between objects for referring expression understanding},\n author={Nagaraja, Varun K and Morariu, Vlad I and Davis, Larry S},\n booktitle={European Conference on Computer Vision},\n pages={792--807},\n year={2016},\n organization={Springer}\n}", + "configName": "refcoco_google", + "description": "A collection of 3 referring expression datasets based off images in the COCO\ndataset. A referring expression is a piece of text that describes a unique\nobject in an image. These datasets are collected by asking human raters to\ndisambiguate objects delineated by bounding boxes in the COCO dataset.\n\nRefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are\nstrictly appearance based descriptions, which they enforced by preventing raters\nfrom using location based descriptions (e.g., \"person to the right\" is not a\nvalid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more\nrich description of objects compared to RefCoco due to differences in the\nannotation process. In particular, RefCoco was collected in an interactive\ngame-based setting, while RefCocoG was collected in a non-interactive setting.\nOn average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.\n\nEach dataset has different split allocations that are typically all reported in\npapers. 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"tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "length": "-1" + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/ref_coco/refcoco_unc/1.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/ref_coco/refcoco_unc/1.1.0/dataset_info.json new file mode 100644 index 00000000000..e2ed82c8b15 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/ref_coco/refcoco_unc/1.1.0/dataset_info.json @@ -0,0 +1,84 @@ +{ + "citation": "@inproceedings{kazemzadeh2014referitgame,\n title={Referitgame: Referring to objects in photographs of natural scenes},\n author={Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara},\n booktitle={Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)},\n pages={787--798},\n year={2014}\n}\n@inproceedings{yu2016modeling,\n title={Modeling context in referring expressions},\n author={Yu, Licheng and Poirson, Patrick and Yang, Shan and Berg, Alexander C and Berg, Tamara L},\n booktitle={European Conference on Computer Vision},\n pages={69--85},\n year={2016},\n organization={Springer}\n}\n@inproceedings{mao2016generation,\n title={Generation and Comprehension of Unambiguous Object Descriptions},\n author={Mao, Junhua and Huang, Jonathan and Toshev, Alexander and Camburu, Oana and Yuille, Alan and Murphy, Kevin},\n booktitle={CVPR},\n year={2016}\n}\n@inproceedings{nagaraja2016modeling,\n title={Modeling context between objects for referring expression understanding},\n author={Nagaraja, Varun K and Morariu, Vlad I and Davis, Larry S},\n booktitle={European Conference on Computer Vision},\n pages={792--807},\n year={2016},\n organization={Springer}\n}", + "configName": "refcoco_unc", + "description": "A collection of 3 referring expression datasets based off images in the COCO\ndataset. A referring expression is a piece of text that describes a unique\nobject in an image. These datasets are collected by asking human raters to\ndisambiguate objects delineated by bounding boxes in the COCO dataset.\n\nRefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are\nstrictly appearance based descriptions, which they enforced by preventing raters\nfrom using location based descriptions (e.g., \"person to the right\" is not a\nvalid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more\nrich description of objects compared to RefCoco due to differences in the\nannotation process. In particular, RefCoco was collected in an interactive\ngame-based setting, while RefCocoG was collected in a non-interactive setting.\nOn average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.\n\nEach dataset has different split allocations that are typically all reported in\npapers. The \"testA\" and \"testB\" sets in RefCoco and RefCoco+ contain only people\nand only non-people respectively. Images are partitioned into the various\nsplits. In the \"google\" split, objects, not images, are partitioned between the\ntrain and non-train splits. This means that the same image can appear in both\nthe train and validation split, but the objects being referred to in the image\nwill be different between the two sets. In contrast, the \"unc\" and \"umd\" splits\npartition images between the train, validation, and test split. In RefCocoG, the\n\"google\" split does not have a canonical test set, and the validation set is\ntypically reported in papers as \"val*\".\n\nStats for each dataset and split (\"refs\" is the number of referring expressions,\nand \"images\" is the number of images):\n\ndataset | partition | split | refs | images\n-------- | --------- | ----- | ----- | ------\nrefcoco | google | train | 40000 | 19213\nrefcoco | google | val | 5000 | 4559\nrefcoco | google | test | 5000 | 4527\nrefcoco | unc | train | 42404 | 16994\nrefcoco | unc | val | 3811 | 1500\nrefcoco | unc | testA | 1975 | 750\nrefcoco | unc | testB | 1810 | 750\nrefcoco+ | unc | train | 42278 | 16992\nrefcoco+ | unc | val | 3805 | 1500\nrefcoco+ | unc | testA | 1975 | 750\nrefcoco+ | unc | testB | 1798 | 750\nrefcocog | google | train | 44822 | 24698\nrefcocog | google | val | 5000 | 4650\nrefcocog | umd | train | 42226 | 21899\nrefcocog | umd | val | 2573 | 1300\nrefcocog | umd | test | 5023 | 2600", + "fileFormat": 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"tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "length": "-1" + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/ref_coco/refcocog_google/1.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/ref_coco/refcocog_google/1.1.0/dataset_info.json new file mode 100644 index 00000000000..f093f6b16aa --- /dev/null +++ b/tensorflow_datasets/testing/metadata/ref_coco/refcocog_google/1.1.0/dataset_info.json @@ -0,0 +1,74 @@ +{ + "citation": "@inproceedings{kazemzadeh2014referitgame,\n title={Referitgame: Referring to objects in photographs of natural scenes},\n author={Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara},\n booktitle={Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)},\n pages={787--798},\n year={2014}\n}\n@inproceedings{yu2016modeling,\n title={Modeling context in referring expressions},\n author={Yu, Licheng and Poirson, Patrick and Yang, Shan and Berg, Alexander C and Berg, Tamara L},\n booktitle={European Conference on Computer Vision},\n pages={69--85},\n year={2016},\n organization={Springer}\n}\n@inproceedings{mao2016generation,\n title={Generation and Comprehension of Unambiguous Object Descriptions},\n author={Mao, Junhua and Huang, Jonathan and Toshev, Alexander and Camburu, Oana and Yuille, Alan and Murphy, Kevin},\n booktitle={CVPR},\n year={2016}\n}\n@inproceedings{nagaraja2016modeling,\n title={Modeling context between objects for referring expression understanding},\n author={Nagaraja, Varun K and Morariu, Vlad I and Davis, Larry S},\n booktitle={European Conference on Computer Vision},\n pages={792--807},\n year={2016},\n organization={Springer}\n}", + "configName": "refcocog_google", + "description": "A collection of 3 referring expression datasets based off images in the COCO\ndataset. A referring expression is a piece of text that describes a unique\nobject in an image. These datasets are collected by asking human raters to\ndisambiguate objects delineated by bounding boxes in the COCO dataset.\n\nRefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are\nstrictly appearance based descriptions, which they enforced by preventing raters\nfrom using location based descriptions (e.g., \"person to the right\" is not a\nvalid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more\nrich description of objects compared to RefCoco due to differences in the\nannotation process. In particular, RefCoco was collected in an interactive\ngame-based setting, while RefCocoG was collected in a non-interactive setting.\nOn average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.\n\nEach dataset has different split allocations that are typically all reported in\npapers. The \"testA\" and \"testB\" sets in RefCoco and RefCoco+ contain only people\nand only non-people respectively. Images are partitioned into the various\nsplits. In the \"google\" split, objects, not images, are partitioned between the\ntrain and non-train splits. This means that the same image can appear in both\nthe train and validation split, but the objects being referred to in the image\nwill be different between the two sets. In contrast, the \"unc\" and \"umd\" splits\npartition images between the train, validation, and test split. In RefCocoG, the\n\"google\" split does not have a canonical test set, and the validation set is\ntypically reported in papers as \"val*\".\n\nStats for each dataset and split (\"refs\" is the number of referring expressions,\nand \"images\" is the number of images):\n\ndataset | partition | split | refs | images\n-------- | --------- | ----- | ----- | ------\nrefcoco | google | train | 40000 | 19213\nrefcoco | google | val | 5000 | 4559\nrefcoco | google | test | 5000 | 4527\nrefcoco | unc | train | 42404 | 16994\nrefcoco | unc | val | 3811 | 1500\nrefcoco | unc | testA | 1975 | 750\nrefcoco | unc | testB | 1810 | 750\nrefcoco+ | unc | train | 42278 | 16992\nrefcoco+ | unc | val | 3805 | 1500\nrefcoco+ | unc | testA | 1975 | 750\nrefcoco+ | unc | testB | 1798 | 750\nrefcocog | google | train | 44822 | 24698\nrefcocog | google | val | 5000 | 4650\nrefcocog | umd | train | 42226 | 21899\nrefcocog | umd | val | 2573 | 1300\nrefcocog | umd | test | 5023 | 2600", + "fileFormat": 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a/tensorflow_datasets/testing/metadata/ref_coco/refcocog_umd/1.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/ref_coco/refcocog_umd/1.1.0/dataset_info.json new file mode 100644 index 00000000000..6da6d1988e2 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/ref_coco/refcocog_umd/1.1.0/dataset_info.json @@ -0,0 +1,79 @@ +{ + "citation": "@inproceedings{kazemzadeh2014referitgame,\n title={Referitgame: Referring to objects in photographs of natural scenes},\n author={Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara},\n booktitle={Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)},\n pages={787--798},\n year={2014}\n}\n@inproceedings{yu2016modeling,\n title={Modeling context in referring expressions},\n author={Yu, Licheng and Poirson, Patrick and Yang, Shan and Berg, Alexander C and Berg, Tamara L},\n booktitle={European Conference on Computer Vision},\n pages={69--85},\n year={2016},\n organization={Springer}\n}\n@inproceedings{mao2016generation,\n title={Generation and Comprehension of Unambiguous Object Descriptions},\n author={Mao, Junhua and Huang, Jonathan and Toshev, Alexander and Camburu, Oana and Yuille, Alan and Murphy, Kevin},\n booktitle={CVPR},\n year={2016}\n}\n@inproceedings{nagaraja2016modeling,\n title={Modeling context between objects for referring expression understanding},\n author={Nagaraja, Varun K and Morariu, Vlad I and Davis, Larry S},\n booktitle={European Conference on Computer Vision},\n pages={792--807},\n year={2016},\n organization={Springer}\n}", + "configName": "refcocog_umd", + "description": "A collection of 3 referring expression datasets based off images in the COCO\ndataset. A referring expression is a piece of text that describes a unique\nobject in an image. These datasets are collected by asking human raters to\ndisambiguate objects delineated by bounding boxes in the COCO dataset.\n\nRefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are\nstrictly appearance based descriptions, which they enforced by preventing raters\nfrom using location based descriptions (e.g., \"person to the right\" is not a\nvalid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more\nrich description of objects compared to RefCoco due to differences in the\nannotation process. In particular, RefCoco was collected in an interactive\ngame-based setting, while RefCocoG was collected in a non-interactive setting.\nOn average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.\n\nEach dataset has different split allocations that are typically all reported in\npapers. The \"testA\" and \"testB\" sets in RefCoco and RefCoco+ contain only people\nand only non-people respectively. Images are partitioned into the various\nsplits. In the \"google\" split, objects, not images, are partitioned between the\ntrain and non-train splits. This means that the same image can appear in both\nthe train and validation split, but the objects being referred to in the image\nwill be different between the two sets. In contrast, the \"unc\" and \"umd\" splits\npartition images between the train, validation, and test split. In RefCocoG, the\n\"google\" split does not have a canonical test set, and the validation set is\ntypically reported in papers as \"val*\".\n\nStats for each dataset and split (\"refs\" is the number of referring expressions,\nand \"images\" is the number of images):\n\ndataset | partition | split | refs | images\n-------- | --------- | ----- | ----- | ------\nrefcoco | google | train | 40000 | 19213\nrefcoco | google | val | 5000 | 4559\nrefcoco | google | test | 5000 | 4527\nrefcoco | unc | train | 42404 | 16994\nrefcoco | unc | val | 3811 | 1500\nrefcoco | unc | testA | 1975 | 750\nrefcoco | unc | testB | 1810 | 750\nrefcoco+ | unc | train | 42278 | 16992\nrefcoco+ | unc | val | 3805 | 1500\nrefcoco+ | unc | testA | 1975 | 750\nrefcoco+ | unc | testB | 1798 | 750\nrefcocog | google | train | 44822 | 24698\nrefcocog | google | val | 5000 | 4650\nrefcocog | umd | train | 42226 | 21899\nrefcocog | umd | val | 2573 | 1300\nrefcocog | umd | test | 5023 | 2600", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://github.com/lichengunc/refer" + ] + }, + "moduleName": "tensorflow_datasets.datasets.ref_coco.ref_coco_dataset_builder", + "name": "ref_coco", + "releaseNotes": { + "1.0.0": "Initial release.", + "1.1.0": "Added masks." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "train", + "numBytes": "3725488889", + "shardLengths": [ + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684", + "684", + "685", + "684" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "validation", + "numBytes": "219653079", + "shardLengths": [ + "650", + "650" + ] + }, + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "test", + 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"tensorflow_datasets.core.features.bounding_boxes.BBoxFeature" + }, + "id": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + }, + "label": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "length": "-1" + } + }, + "image": { + "image": { + "dtype": "uint8", + "encodingFormat": "jpeg", + "shape": { + "dimensions": [ + "-1", + "-1", + "3" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "image/id": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + }, + "objects": { + "pythonClassName": "tensorflow_datasets.core.features.sequence_feature.Sequence", + "sequence": { + "feature": { + "featuresDict": { + "features": { + "area": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + }, + "bbox": { + "boundingBox": { + "dtype": "float32", + "shape": { + "dimensions": [ + "4" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.bounding_boxes.BBoxFeature" + }, + "gt_box_index": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + }, + "id": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + }, + "label": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + }, + "mask": { + "image": { + "dtype": "uint8", + "encodingFormat": "png", + "shape": { + "dimensions": [ + "-1", + "-1", + "3" + ] + } + }, + "pythonClassName": "tensorflow_datasets.core.features.image_feature.Image" + }, + "refexp": { + "pythonClassName": "tensorflow_datasets.core.features.sequence_feature.Sequence", + "sequence": { + "feature": { + "featuresDict": { + "features": { + "raw": { + "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text", + "text": {} + }, + "refexp_id": { + "pythonClassName": "tensorflow_datasets.core.features.tensor_feature.Tensor", + "tensor": { + "dtype": "int64", + "encoding": "none", + "shape": {} + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "length": "-1" + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" + }, + "length": "-1" + } + } + } + }, + "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict" +} \ No newline at end of file diff --git a/tensorflow_datasets/testing/metadata/ref_coco/refcocoplus_unc/1.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/ref_coco/refcocoplus_unc/1.1.0/dataset_info.json new file mode 100644 index 00000000000..69612db24f3 --- /dev/null +++ b/tensorflow_datasets/testing/metadata/ref_coco/refcocoplus_unc/1.1.0/dataset_info.json @@ -0,0 +1,84 @@ +{ + "citation": "@inproceedings{kazemzadeh2014referitgame,\n title={Referitgame: Referring to objects in photographs of natural scenes},\n author={Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara},\n booktitle={Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)},\n pages={787--798},\n year={2014}\n}\n@inproceedings{yu2016modeling,\n title={Modeling context in referring expressions},\n author={Yu, Licheng and Poirson, Patrick and Yang, Shan and Berg, Alexander C and Berg, Tamara L},\n booktitle={European Conference on Computer Vision},\n pages={69--85},\n year={2016},\n organization={Springer}\n}\n@inproceedings{mao2016generation,\n title={Generation and Comprehension of Unambiguous Object Descriptions},\n author={Mao, Junhua and Huang, Jonathan and Toshev, Alexander and Camburu, Oana and Yuille, Alan and Murphy, Kevin},\n booktitle={CVPR},\n year={2016}\n}\n@inproceedings{nagaraja2016modeling,\n title={Modeling context between objects for referring expression understanding},\n author={Nagaraja, Varun K and Morariu, Vlad I and Davis, Larry S},\n booktitle={European Conference on Computer Vision},\n pages={792--807},\n year={2016},\n organization={Springer}\n}", + "configName": "refcocoplus_unc", + "description": "A collection of 3 referring expression datasets based off images in the COCO\ndataset. A referring expression is a piece of text that describes a unique\nobject in an image. These datasets are collected by asking human raters to\ndisambiguate objects delineated by bounding boxes in the COCO dataset.\n\nRefCoco and RefCoco+ are from Kazemzadeh et al. 2014. RefCoco+ expressions are\nstrictly appearance based descriptions, which they enforced by preventing raters\nfrom using location based descriptions (e.g., \"person to the right\" is not a\nvalid description for RefCoco+). RefCocoG is from Mao et al. 2016, and has more\nrich description of objects compared to RefCoco due to differences in the\nannotation process. In particular, RefCoco was collected in an interactive\ngame-based setting, while RefCocoG was collected in a non-interactive setting.\nOn average, RefCocoG has 8.4 words per expression while RefCoco has 3.5 words.\n\nEach dataset has different split allocations that are typically all reported in\npapers. The \"testA\" and \"testB\" sets in RefCoco and RefCoco+ contain only people\nand only non-people respectively. Images are partitioned into the various\nsplits. In the \"google\" split, objects, not images, are partitioned between the\ntrain and non-train splits. This means that the same image can appear in both\nthe train and validation split, but the objects being referred to in the image\nwill be different between the two sets. In contrast, the \"unc\" and \"umd\" splits\npartition images between the train, validation, and test split. 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Berg and Wan-Yen Lo and Piotr Doll\u00e1r and Ross Girshick},\n year={2023},\n eprint={2304.02643},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}", + "description": "# SA-1B Download\n\nSegment Anything 1 Billion (SA-1B) is a dataset designed for training\ngeneral-purpose object segmentation models from open world images. The dataset\nwas introduced in the paper\n[\"Segment Anything\"](https://arxiv.org/abs/2304.02643).\n\nThe SA-1B dataset consists of 11M diverse, high-resolution, licensed, and\nprivacy-protecting images and 1.1B mask annotations. Masks are given in the COCO\nrun-length encoding (RLE) format, and do not have classes.\n\nThe license is custom. 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of\nReinforcement Learning Agents to Optimize Energy and\nEmission in Office Buildings\" by Judah Goldfeder and\nJohn Sipple, containing 6 years of detailed telemetric\nreadouts from 3 commercial office buildings.", + "disableShuffling": true, + "downloadSize": "11796016478", + "fileFormat": "array_record", + "location": { + "urls": [ + "https://github.com/google/sbsim" + ] + }, + "moduleName": "tensorflow_datasets.datasets.smart_buildings.smart_buildings_dataset_builder", + "name": "smart_buildings", + "releaseNotes": { + "1.0.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}", + "name": "sb1_19", + "numBytes": "18025984597", + "shardLengths": [ + "411", + "411", + "410", + "411", + "411", + "410", + "411", + "410", + "411", + "411", + "410", + "411", + "411", + "410", + "411", + "410", + "411", + "411", + "410", + "411", + "411", + "410", + "411", + "410", + "411", + "411", + "410", + "411", + "411", + "410", + "411", + 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oxford_flowers102/2.1.1 -oxford_iiit_pet/3.2.0 +oxford_iiit_pet/4.0.0 para_crawl/enbg/1.2.0 para_crawl/encs/1.2.0 para_crawl/enda/1.2.0 @@ -1322,9 +1360,11 @@ pg19/0.1.1 piqa/1.0.0 places365_small/2.1.0 placesfull/1.0.0 -plant_leaves/0.1.0 +plant_leaves/0.1.1 plant_village/1.0.2 plantae_k/0.1.0 +plex_robosuite/0.1.0 +pneumonia_mnist/1.0.0 protein_net/casp10/1.0.0 protein_net/casp11/1.0.0 protein_net/casp12/1.0.0 @@ -1353,6 +1393,9 @@ qa4mre/2013.main.EN/0.1.0 qa4mre/2013.main.ES/0.1.0 qa4mre/2013.main.RO/0.1.0 qasc/0.1.0 +qm9/cormorant/1.0.0 +qm9/dimenet/1.0.0 +qm9/original/1.0.0 quac/1.0.0 quality/raw/1.0.0 quality/stripped/1.0.0 @@ -1365,11 +1408,11 @@ reddit/1.0.0 reddit_tifu/long/1.1.2 reddit_tifu/long_split/1.1.2 reddit_tifu/short/1.1.2 -ref_coco/refcoco_google/1.0.0 -ref_coco/refcoco_unc/1.0.0 -ref_coco/refcocog_google/1.0.0 -ref_coco/refcocog_umd/1.0.0 -ref_coco/refcocoplus_unc/1.0.0 +ref_coco/refcoco_google/1.1.0 +ref_coco/refcoco_unc/1.1.0 +ref_coco/refcocog_google/1.1.0 +ref_coco/refcocog_umd/1.1.0 +ref_coco/refcocoplus_unc/1.1.0 resisc45/3.0.0 rlu_atari/Alien_run_1/1.3.0 rlu_atari/Alien_run_2/1.3.0 @@ -2138,6 +2181,7 @@ rlu_rwrl/walker_walk_combined_challenge_none_1_percent/1.0.1 rlu_rwrl/walker_walk_combined_challenge_none_20_percent/1.0.1 rlu_rwrl/walker_walk_combined_challenge_none_40_percent/1.0.1 rlu_rwrl/walker_walk_combined_challenge_none_5_percent/1.0.1 +robo_set/0.1.0 robomimic_mg/can_mg_image/1.0.0 robomimic_mg/can_mg_low_dim/1.0.0 robomimic_mg/lift_mg_image/1.0.0 @@ -2167,6 +2211,7 @@ robonet/robonet_sample_64/4.0.1 robosuite_panda_pick_place_can/human_dc29b40a/1.0.0 robosuite_panda_pick_place_can/human_images_dc29b40a/1.0.0 robosuite_panda_pick_place_can/synthetic_stochastic_sac_afe13968/1.0.0 +roboturk/0.1.0 rock_paper_scissors/3.0.0 rock_you/1.0.0 s3o4d/1.0.0 @@ -2204,6 +2249,7 @@ scrolls/qasper/1.0.0 scrolls/qmsum/1.0.0 scrolls/quality/1.0.0 scrolls/summ_screen_fd/1.0.0 +segment_anything/1.0.0 sentiment140/1.0.0 shapes3d/2.0.0 sift1m/1.0.0 @@ -2213,16 +2259,22 @@ siscore/rotation/1.0.0 siscore/size/1.0.0 smallnorb/2.0.0 smallnorb/2.1.0 +smart_buildings/sb1/1.0.0 smartwatch_gestures/1.0.0 snli/1.1.0 speech_commands/0.0.3 +spoc_robot/0.1.0 spoken_digit/1.0.9 squad/v1.1/3.0.0 squad/v2.0/3.0.0 squad_question_generation/split_du/3.0.0 squad_question_generation/split_zhou/3.0.0 stanford_dogs/0.2.0 +stanford_hydra_dataset_converted_externally_to_rlds/0.1.0 +stanford_kuka_multimodal_dataset_converted_externally_to_rlds/0.1.0 +stanford_mask_vit_converted_externally_to_rlds/0.1.0 stanford_online_products/1.0.0 +stanford_robocook_converted_externally_to_rlds/0.1.0 star_cfq/equal_weighting_0.1x_b_cfq_0.1x_half_l_cfq/1.1.0 star_cfq/equal_weighting_0.1x_b_cfq_0.1x_half_n_cfq/1.1.0 star_cfq/equal_weighting_0.1x_b_cfq_0.1x_half_x_cfq/1.1.0 @@ -3148,11 +3200,9 @@ super_glue/rte/1.0.2 super_glue/wic/1.0.2 super_glue/wsc.fixed/1.0.2 super_glue/wsc/1.0.2 -svhn_cropped/3.0.0 svhn_cropped/3.1.0 symmetric_solids/1.0.0 -tao/480_640/1.0.0 -tao/full_resolution/1.0.0 +taco_play/0.1.0 tatoeba/tatoeba_af/1.0.0 tatoeba/tatoeba_ar/1.0.0 tatoeba/tatoeba_bg/1.0.0 @@ -3209,9 +3259,11 @@ tedlium/release2/1.0.1 tedlium/release3/1.0.1 tf_flowers/3.0.1 the300w_lp/1.0.0 +tidybot/0.1.0 tiny_shakespeare/1.0.0 titanic/2.0.0 titanic/4.0.0 +tokyo_u_lsmo_converted_externally_to_rlds/0.1.0 trec/1.0.0 trivia_qa/rc.nocontext/1.1.0 trivia_qa/rc/1.1.0 @@ -3223,6 +3275,9 @@ ucf101/ucf101_1/2.0.0 ucf101/ucf101_1_256/2.0.0 ucf101/ucf101_2/2.0.0 ucf101/ucf101_3/2.0.0 +ucsd_kitchen_dataset_converted_externally_to_rlds/0.1.0 +ucsd_pick_and_place_dataset_converted_externally_to_rlds/0.1.0 +uiuc_d3field/0.1.0 unified_qa/ai2_science_elementary/1.0.0 unified_qa/ai2_science_middle/1.0.0 unified_qa/ambigqa/1.0.0 @@ -3492,10 +3547,18 @@ universal_dependencies/zh_gsdsimp/1.0.1 universal_dependencies/zh_hk/1.0.1 universal_dependencies/zh_pud/1.0.1 unnatural_instructions/0.0.1 +usc_cloth_sim_converted_externally_to_rlds/0.1.0 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a/tensorflow_datasets/testing/metadata/utokyo_xarm_bimanual_converted_externally_to_rlds/0.1.0/dataset_info.json b/tensorflow_datasets/testing/metadata/utokyo_xarm_bimanual_converted_externally_to_rlds/0.1.0/dataset_info.json new file mode 100644 index 00000000000..0ae0adb191d --- /dev/null +++ b/tensorflow_datasets/testing/metadata/utokyo_xarm_bimanual_converted_externally_to_rlds/0.1.0/dataset_info.json @@ -0,0 +1,34 @@ +{ + "citation": "@misc{matsushima2023weblab,\n title={Weblab xArm Dataset},\n author={Tatsuya Matsushima and Hiroki Furuta and Yusuke Iwasawa and Yutaka Matsuo},\n year={2023},\n}", + "description": "xArm bimanual setup folding towel", + "fileFormat": "array_record", + "location": { + "urls": [ + "--" + ] + }, + "moduleName": "tensorflow_datasets.robotics.rtx.rtx", + "name": "utokyo_xarm_bimanual_converted_externally_to_rlds", + "releaseNotes": { + "0.1.0": "Initial release." + }, + "splits": [ + { + "filepathTemplate": 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