region:us
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: split
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: bboxes
    dtype: string
  - name: labels
    dtype: string
  - name: cab
    dtype: int64
  - name: hum
    dtype: int64
  - name: light
    dtype: float64
  - name: cam
    dtype: int64
  - name: env
    dtype: int64
  - name: gaze_item
    dtype: int64
  - name: gazeIdx
    dtype: int64
  - name: gaze_cx
    dtype: int64
  - name: gaze_cy
    dtype: int64
  - name: hx
    dtype: int64
  - name: hy
    dtype: int64
  - name: pitch
    dtype: float64
  - name: yaw
    dtype: float64
  - name: roll
    dtype: float64
  - name: seg
    dtype: string
  - name: segm_gazeIdx
    dtype: int64
  - name: occluded
    dtype: int64
  splits:
  - name: train
    num_bytes: 99500978350.0
    num_examples: 172800
  - name: test
    num_bytes: 11081866319.6
    num_examples: 19200
  download_size: 110113558133
  dataset_size: 110582844669.6
---
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/goosynth-download.txt

To download goosynthtrain in huggingface, run the code below (https://huggingface.co/docs/datasets/v1.10.0/loading_datasets.html#from-the-huggingface-hub):

from datasets import load_dataset</br>
dataset = load_dataset("markytools/goosyntheticv3")

The image datasets will be stored in ""~/.cache/huggingface", so you need to delete the files here if you want to free up space.

The only difference here is that there is a new feature name called "splits", ["train", "test"] </br>
The "bboxes" and "labels" features are in string format, so you can use the code below to convert the string into list:</br>
import ast</br>
listOfBboxes = ast.literal_eval(dataset["test"]["bboxes"][0])</br>
</br>
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).