DataGradients is an open-source python based library designed for computer vision dataset analysis.
Extract valuable insights from your datasets and get comprehensive reports effortlessly.
- Corrupted data
- Labeling errors
- Underlying biases, and more.
- Informed decisions based on data characteristics.
- Object size and location distributions.
- High frequency details.
- Define the correct NMS and filtering parameters.
- Identify class distribution issues.
- Calibrate metrics for your unique dataset.
Non-exhaustive list of supported features.
- General Image Metrics: Explore key attributes like resolution, color distribution, and average brightness.
- Class Overview: Get a snapshot of class distributions, most frequent classes, and unlabelled images.
- Positional Heatmaps: Visualize where objects tend to appear within your images.
- Bounding Box & Mask Details: Delve into dimensions, area coverages, and resolutions of objects.
- Class Frequencies Deep Dive: Dive deeper into class distributions, understanding anomalies and rare classes.
- Detailed Object Counts: Examine the granularity of components per image, identifying patterns and outliers.
- And many more!
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Example of pages from the Report
Example of specific features
Check out the pre-computed dataset analysis for a deeper dive into reports.
- Installation
- Quick Start
- Feature Configuration
- Dataset Extractors
- Pre-computed Dataset Analysis
- License
You can install DataGradients directly from the GitHub repository.
pip install data-gradients
- Dataset: Includes a Train set and a Validation or a Test set.
- Dataset Iterable: A method to iterate over your Dataset providing images and labels. Can be any of the following:
- PyTorch Dataloader
- PyTorch Dataset
- Generator that yields image/label pairs
- Any other iterable you use for model training/validation
- One of:
- Class Names: Either the list of all class names in the dataset OR dictionary mapping of
class_id->class_name. - Number of classes: Indicate how many unique classes are in your dataset. Ensure this number is greater than the highest class index (e.g., if your highest class index is 9, the number of classes should be at least 10).
- Class Names: Either the list of all class names in the dataset OR dictionary mapping of
Please ensure all the points above are checked before you proceed with DataGradients.
Example
from torchvision.datasets import CocoDetection
train_data = CocoDetection(...)
val_data = CocoDetection(...)
class_names = ["person", "bicycle", "car", "motorcycle", ...]
# OR
# class_names = {0: "person", 1:"bicycle", 2:"car", 3: "motorcycle", ...}Good to Know - DataGradients will try to find out how the dataset returns images and labels.
- If something cannot be automatically determined, you will be asked to provide some extra information through a text input.
- In some extreme cases, the process will crash and invite you to implement a custom dataset extractor
Heads up - DataGradients provides a few out-of-the-box dataset/dataloader implementation. You can find more dataset implementations in PyTorch or SuperGradients.
You are now ready to go, chose the relevant analyzer for your task and run it over your datasets!
Image Classification
from data_gradients.managers.classification_manager import ClassificationAnalysisManager
train_data = ... # Your dataset iterable (torch dataset/dataloader/...)
val_data = ... # Your dataset iterable (torch dataset/dataloader/...)
class_names = ... # [<class-1>, <class-2>, ...]
analyzer = ClassificationAnalysisManager(
report_title="Testing Data-Gradients Classification",
train_data=train_data,
val_data=val_data,
class_names=class_names,
)
analyzer.run()Object Detection
from data_gradients.managers.detection_manager import DetectionAnalysisManager
train_data = ... # Your dataset iterable (torch dataset/dataloader/...)
val_data = ... # Your dataset iterable (torch dataset/dataloader/...)
class_names = ... # [<class-1>, <class-2>, ...]
analyzer = DetectionAnalysisManager(
report_title="Testing Data-Gradients Object Detection",
train_data=train_data,
val_data=val_data,
class_names=class_names,
)
analyzer.run()Semantic Segmentation
from data_gradients.managers.segmentation_manager import SegmentationAnalysisManager
train_data = ... # Your dataset iterable (torch dataset/dataloader/...)
val_data = ... # Your dataset iterable (torch dataset/dataloader/...)
class_names = ... # [<class-1>, <class-2>, ...]
analyzer = SegmentationAnalysisManager(
report_title="Testing Data-Gradients Segmentation",
train_data=train_data,
val_data=val_data,
class_names=class_names,
)
analyzer.run()Example
You can test the segmentation analysis tool in the following example which does not require you to download any additional data.
Once the analysis is done, the path to your pdf report will be printed. You can find here examples of pre-computed dataset analysis reports.
The feature configuration allows you to run the analysis on a subset of features or adjust the parameters of existing features. If you are interested in customizing this configuration, you can check out the documentation on that topic.
Ensuring Comprehensive Dataset Compatibility
DataGradients is adept at automatic dataset inference; however, certain specificities, such as nested annotations structures or unique annotation format, may necessitate a tailored approach.
To address this, DataGradients offers extractors tailored for enhancing compatibility with diverse dataset formats.
For an in-depth understanding and implementation details, we encourage a thorough review of the Dataset Extractors Documentation.
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Example notebook on Colab |
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This project is released under the Apache 2.0 license.