Object classification is a computer vision technique that identifies and categorizes objects within an image or video using machine learning algorithms like deep neural networks to analyze visual features and make predictions about object classes. Object classification is crucial in applications such as self-driving cars, where vehicles must recognize and classify different types of objects on the road, and image recognition tasks, including identifying specific objects and detecting anomalies or defects in manufacturing processes. The Caltech 101 dataset is a popular benchmark for object recognition in computer vision, containing images from 101 object categories with diverse lighting conditions, backgrounds, and viewpoints. Object classification algorithms typically involve feature extraction and classification steps, and the task can be challenging due to variability in object appearance caused by factors such as lighting, occlusion, and pose. Advances in machine learning and computer vision techniques have significantly improved object classification accuracy in recent years, making it an increasingly important technology in various fields. Object classification enables algorithmic models to interpret and understand the visual world around them, extracting meaningful information such as object location, size, and orientation, which is critical for tasks like object tracking, detection, and recognition. The dataset contains 9146 images from 101 categories with diverse image sizes and aspect ratios, and low-level clutter/occlusion, making it a suitable choice for training object recognition models. Object classification algorithms can be divided into two groups: individual object recognition and category recognition, with the latter being more challenging due to variability in object appearance within categories. The dataset is widely used for benchmarking state-of-the-art object recognition models and has applications in various fields such as autonomous vehicles, facial recognition, surveillance systems, and medical imaging. Object classification is a fundamental component of many computer vision applications and is essential for tasks like object tracking, detection, and recognition. By analyzing the Caltech 101 dataset using Encord Active, we can assess data quality, label quality, model performance, and other metrics to improve the accuracy and robustness of object classification models.