The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label CathAction@8b04056f0f4fa4b04d8454728f000730af0d5560
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label CathAction@8b04056f0f4fa4b04d8454728f000730af0d5560Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CathAction Dataset
CathAction is large-scale dataset designed for advancing catheterization understanding. CathAction comprises annotated frames focused on catheterization understanding and collision detection, along with groundtruth masks dedicated to catheter and guidewire segmentation.
Please fill out the download form and agree to our license prior to downloading the dataset.
Dataset Structure:
1. Catheterization Action understanding
The CathAction dataset encompasses annotated frames for catheterization action understanding task such as catheterization anticipation and action recognition.
These are five classes: advance catheter, retract catheter, advance guidewire, retract guidewire, and rotate.
The dataset is organized into the following folders and files:
- video_frames/: Contains extracted video frames for each video.
- feature_extractions/: Contains pre-extracted RGB features, extracted using this code.
- training.csv: Groundtruth CSV file for training data.
- validation.csv: Groundtruth CSV file for validation data.
Annotation File Structure
The annotation files (training.csv and validation.csv) contain four columns, with the following structure:
| Column Name | Type | Example | Description |
|---|---|---|---|
video_id |
string | video_1 |
ID of the video where the action segment is located. |
start_frame |
int | 430 |
Start frame of the action. |
stop_frame |
int | 643 |
End frame of the action. |
all_action_classes |
list of int(s) | [1] |
List of numeric IDs for all detected action classes in the segment. |
The frames and pre-extracted RGB features are located in the video_frames and feature_extractions folders, respectively, and can be generated using this code.
Usage
- Catheterization Action Recognition and Anticipation Models: Use the
start_frameandstop_framevalues, along with the ground truthall_action_classesin the CSV file, to train models that recognize action segments and anticipate future catheter actions.
2. Collision Detection
The CathAction dataset is designed for the collision detection task, which involves identifying whether the tip of the catheter or guidewire comes into contact with the blood vessel wall.
The dataset is organized as follows:
images/: Contains images related to collision and normal events.
labels/: Contains annotation files for each image, detailing information on bounding boxes and object classes, including collision occurrences and the normal class for the corresponding image
train_phantom.txt: A text file listing paths to training images and labels for the "phantom" data source in the collision detection task.
valid_animal.txt: A text file listing paths to validation images and labels for the "animal" source data.
valid_phantom.txt: A text file listing paths to validation images and labels for the "phantom" source data.
Each .txt file contains a list of image and label paths for its respective category and split (train/validation), enabling easy access and organization for model training and evaluation.
Usage
- Training: Use
train_phantom.txtto load training data for the phantom data source. - Validation: Use
valid_animal.txtandvalid_phantom.txtfor validating model performance on different data sources, specifically focusing on the 'animal' and 'phantom' data.
For more information, please visit our webpage.
For inquiries or assistance, please contact the authors at this link.
Best regards,
Authors
- Downloads last month
- 94