wentao-yuan/robopoint-v1-vicuna-v1.5-13b
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Error code: InfoError Exception: ConnectionError Message: Couldn't reach https://huggingface.co/datasets/wentao-yuan/robopoint-data/resolve/aaa875d11859d8ad546d5208784dd62c6699842b/.huggingface.yaml (error 500) Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 211, in compute_first_rows_from_streaming_response info = get_dataset_config_info(path=dataset, config_name=config, token=hf_token) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 478, in get_dataset_config_info builder = load_dataset_builder( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2266, in load_dataset_builder dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1889, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1225, in get_module standalone_yaml_path = cached_path( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 201, in cached_path output_path = get_from_cache( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 635, in get_from_cache raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://huggingface.co/datasets/wentao-yuan/robopoint-data/resolve/aaa875d11859d8ad546d5208784dd62c6699842b/.huggingface.yaml (error 500)
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This dataset contains 1432K image-QA instances used to fine-tune RoboPoint, a VLM for spatial affordance prediction. It consists of the following parts:
robopoint_1432k.json
contains a list of conversations with image references. An example looks like the following
{
"id": "region_ref/1033888784-63bd2a7_cam05_obj5-obj18_left",
"image": "region_ref/1033888784-63bd2a7_cam05_obj5-obj18.png",
"conversations": [
{
"from": "human",
"value": "<image>\nIn the image, there is an item encased within a red rectangle. Pinpoint several points within the vacant space situated to the left of the object that is highlighted. Your answer should be formatted as a list of tuples, i.e. [(x1, y1), (x2, y2), ...], where each tuple contains the x and y coordinates of a point satisfying the conditions above. The coordinates should be between 0 and 1, indicating the normalized pixel locations of the points in the image."
},
{
"from": "gpt",
"value": "[(0.461, 0.527), (0.498, 0.521), (0.481, 0.521), (0.445, 0.529)]"
}
]
}
The images
folder contains reference images in compressed files. To start training, first combine any multi-part files:
cat region_ref.tar.gz.part_* > region_ref.tar.gz
Then, decompress
tar -xzvf region_ref.tar.gz
If you find our work helpful, please consider citing our paper.
@article{yuan2024robopoint,
title={RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics},
author={Yuan, Wentao and Duan, Jiafei and Blukis, Valts and Pumacay, Wilbert and Krishna, Ranjay and Murali, Adithyavairavan and Mousavian, Arsalan and Fox, Dieter},
journal={arXiv preprint arXiv:2406.10721},
year={2024}
}