The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: ImportError
Message: To be able to use yiqun/coco_stuff, you need to install the following dependency: pycocotools.
Please install it using 'pip install pycocotools' for instance.
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 347, in get_dataset_config_names
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 1880, in dataset_module_factory
return HubDatasetModuleFactoryWithScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module
local_imports = _download_additional_modules(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules
raise ImportError(
ImportError: To be able to use yiqun/coco_stuff, you need to install the following dependency: pycocotools.
Please install it using 'pip install pycocotools' for instance.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Load COCO Stuff dataset from local directory.
Usage:
from datasets import load_dataset
name = "stuff-only" # choose from ["stuff-only", "stuff-thing"]
DATA_DIR = "/path/to/COCOStuff"
split = "val" # choose from ["train", "val"]
ds = load_dataset("yiqun/coco_stuff", name=name, data_dir=DATA_DIR, split=split)
Structure of DATA_DIR:
|_ annotations
|_ # images
|_ images
|_ # images
|_ labels.txt
|_ stuff_train2017.json
|_ stuff_val2017.json
NOTE: This script returns the path to image and stuff-thing segmentation maps to reduce the dataset generation time, users need to read them. Also, according to the official README:
Caffe-compatible stuff-thing maps. We suggest using the stuffthingmaps, as they provide all stuff and thing labels in a single .png file per image. Note that the .png files are indexed images, which means they store only the label indices and are typically displayed as grayscale images. To be compatible with most Caffe-based semantic segmentation methods, thing+stuff labels cover indices 0-181 and 255 indicates the 'unlabeled' or void class.
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