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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: split |
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dtype: string |
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- name: width |
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dtype: int64 |
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- name: height |
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dtype: int64 |
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- name: bboxes |
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dtype: string |
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- name: labels |
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dtype: string |
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- name: gaze_item |
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dtype: int64 |
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- name: gazeIdx |
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dtype: int64 |
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- name: gaze_cx |
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dtype: int64 |
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- name: gaze_cy |
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dtype: int64 |
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- name: hx |
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dtype: int64 |
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- name: hy |
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dtype: int64 |
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- name: seg |
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dtype: string |
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- name: occluded |
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dtype: bool |
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- name: person_num |
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dtype: int64 |
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- name: cam_num |
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dtype: int64 |
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splits: |
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- name: test |
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num_bytes: 6289998121.391 |
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num_examples: 7391 |
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download_size: 6286282416 |
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dataset_size: 6289998121.391 |
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--- |
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The dataset features/columns here are almost similar to the original github instruction (please read the github documentation first to understand the dataset): https://github.com/upeee/GOO-GAZE2021/blob/main/dataset/gooreal-download.txt |
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To download gooreal in huggingface, run the code below (https://huggingface.co/docs/datasets/v1.10.0/loading_datasets.html#from-the-huggingface-hub): |
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from datasets import load_dataset</br> |
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dataset = load_dataset("markytools/goorealv3") |
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The image datasets will be stored in ""~/.cache/huggingface", so you need to delete the files here if you want to free up space. |
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</br> |
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The "bboxes" and "labels" features are in string format, so you can use the code below to convert the string into list:</br> |
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import ast</br> |
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listOfBboxes = ast.literal_eval(dataset["test"]["bboxes"][0])</br> |
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</br> |
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The feature "seg" is now in string format instead of numpy ndarray. This is an optional feature, and you can manually download the files here (https://huggingface.co/datasets/markytools/goosegmv3) using wget commandline. The files are in .npy so load it using np.load (https://numpy.org/doc/stable/reference/generated/numpy.load.html). |
