# Image Classification

- [Failure Mode Analysis](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/failure-mode-analysis.md): Analyze common errors in classification tasks. Refine training and improve model accuracy.
- [Labelling Quality Test](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/labelling-quality-test.md)
- [Class Imbalance](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/class-imbalance.md): The Class Imbalance Test is designed to assess the distribution of classes within a dataset, particularly in the context of machine learning tasks like object detection.
- [Drift Detection](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/drift-detection.md): The Drift Detection Test allows users to identify shifts between training and field/test datasets
- [Near Duplicates Test](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/near-duplicates-test.md): The Near Duplicate Detection Test in RagaAI is designed to identify both exact and near duplicates within your image dataset.
- [Data Leakage Test](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/data-leakage-test.md): The Data Leakage Test results provide insights into the presence of data leakage from the training dataset to the test/validation dataset.
- [Outlier Detection](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/outlier-detection.md): The Outlier Detection Test in RagaAI is crucial for identifying anomalies in your dataset.
- [Active Learning](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/active-learning.md): The Active Learning Test in RagaAI optimises dataset by selecting the most representative data points within a specified budget.
- [Image Property Drift Detection](https://docs.raga.ai/ragaai-prism/test-inventory/image-classification/image-property-drift-detection.md): The Image Property Drift Detection Test is designed to monitor the shifts in individual image properties over time.


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