Editing Computer Vision (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Pixel''' — The smallest unit of a digital image, containing color information (RGB channels with values 0–255).
* '''Image classification''' — Assigning a label to an entire image (e.g., "cat" or "dog").
* '''Object detection''' — Identifying and localizing multiple objects within an image using bounding boxes.
* '''Semantic segmentation''' — Classifying every pixel in an image with a class label (e.g., sky, road, person).
* '''Instance segmentation''' — Like semantic segmentation but distinguishing individual object instances.
* '''Convolution''' — A mathematical operation that slides a small filter (kernel) across an image to produce a feature map highlighting learned patterns.
* '''Kernel/Filter''' — A small matrix of learnable weights applied during convolution (e.g., 3×3 or 5×5).
* '''Pooling''' — A downsampling operation that reduces spatial dimensions, retaining important features (max pooling, average pooling).
* '''Feature map''' — The output of a convolution layer representing activations at each spatial position.
* '''Bounding box''' — A rectangle (x, y, width, height) used to localize an object in an image.
* '''IoU (Intersection over Union)''' — A metric measuring overlap between predicted and ground-truth bounding boxes.
* '''Anchor boxes''' — Predefined bounding box shapes used in object detection models like YOLO and Faster R-CNN.
* '''Data augmentation''' — Artificially increasing training set diversity through transformations: flipping, rotation, cropping, color jitter.
* '''Transfer learning''' — Using a model pre-trained on a large dataset (e.g., ImageNet) as a starting point for a new task.
* '''ResNet''' — Residual Network; a CNN architecture with skip connections that enabled training of very deep networks.
* '''Vision Transformer (ViT)''' — A transformer applied directly to patches of an image rather than using convolutions.
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