Datasets:

uwipl
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RT-Pose

Tasks:

Keypoint Detection

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1K<n<10K

ArXiv:

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Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    ReadTimeout
Message:      (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: c4392bc4-879c-4169-8b04-c50c593af3d0)')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 164, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1729, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1686, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1024, in get_module
                  standalone_yaml_path = cached_path(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 178, in cached_path
                  resolved_path = huggingface_hub.HfFileSystem(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 175, in resolve_path
                  repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 121, in _repo_and_revision_exist
                  self._api.repo_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2682, in repo_info
                  return method(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2539, in dataset_info
                  r = get_session().get(path, headers=headers, timeout=timeout, params=params)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 602, in get
                  return self.request("GET", url, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request
                  resp = self.send(prep, **send_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send
                  r = adapter.send(request, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 93, in send
                  return super().send(request, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 635, in send
                  raise ReadTimeout(e, request=request)
              requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: c4392bc4-879c-4169-8b04-c50c593af3d0)')

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YAML Metadata Warning: The task_categories "pose-estimation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other

Paper

RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark (ECCV 2024)

RT-Pose introduces a human pose estimation (HPE) dataset and benchmark by integrating a unique combination of calibrated radar ADC data, 4D radar tensors, stereo RGB images, and LiDAR point clouds. This integration marks a significant advancement in studying human pose analysis through multi-modality datasets.

Dataset Details

Dataset Description

Sensors

The data collection hardware system comprises two RGB cameras, a non-repetitive horizontal scanning LiDAR, and a cascade imaging radar module.

Data Statics

We collect the dataset in 40 scenes with indoor and outdoor environments.

The dataset comprises 72,000 frames distributed across 240 sequences. The structured organization ensures a realistic distribution of human motions, which is crucial for robust analysis and model training.

Please check the paper for more details.

Curated by: Yuan-Hao Ho ([email protected]), Jen-Hao(Andy) Cheng([email protected]) from Information Processing Lab at University of Washington
License: CC BY-NC-SA

Dataset Sources

Repository including data processing and baseline method codes: RT-POSE
Paper: Paper

Uses

Download the dataset from Hugging Face (Total data size: ~1.2 TB)
Follow the data processing tool to process radar ADC samples into radar tensors. (Total data size of the downloaded data and saved radar tensors: ~41 TB)
Check the data loading and baseline method's training and testing codes in the same repo RT-POSE

Citation

BibTeX:

@article{rtpose2024, title={RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark}, author={Yuan-Hao Ho and Jen-Hao Cheng and Sheng Yao Kuan and Zhongyu Jiang and Wenhao Chai and Hsiang-Wei Huang and Chih-Lung Lin and Jenq-Neng Hwang}, journal={arXiv preprint arXiv:2407.13930}, year={2024} }

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