A single-stage object detector using focal loss to handle class imbalance.
This concept is essential for understanding computer vision and forms a key part of modern AI systems.
Related Concepts
- Object Detection
- Focal Loss
- FPN
Reference 19 stops to get here
RetinaNet
A single-stage object detector using focal loss to handle class imbalance.
Your route here
19 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Classification ✓ understood
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
- Imbalanced Dataset ✓ understood
A dataset where classes have significantly different numbers of examples, causing models to bias toward majority classes.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Loss Function ✓ understood
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
- Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
- Activation Function ✓ understood
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
- Softmax ✓ understood
A function that turns a list of scores (logits) into probabilities that are all positive and sum to 1; the standard output of classifiers and language models.
- Cross-Entropy Loss ✓ understood
A loss function for classification that measures the difference between predicted and true probability distributions.
- Focal Loss ✓ understood
A modified cross-entropy loss that down-weights easy examples, helping with class imbalance.
- Convolution ✓ understood
A mathematical operation that applies filters/kernels to input data to extract features like edges, textures, and patterns.
- Convolutional Neural Network ✓ understood
A neural network that scans images with small learned filters, reusing the same weights at every position to build up from edges to whole objects.
- Deep Learning ✓ understood
A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.
- Computer Vision ✓ understood
The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.
- Bounding Box ✓ understood
A rectangular box defined by coordinates that localizes an object in an image, used in object detection.
- Object Detection ✓ understood
Finding every object of interest in an image and giving each a class label, a confidence score and a bounding box.
- Feature Pyramid Network ✓ understood
A CNN architecture creating multi-scale feature representations for detecting objects at different sizes.
- RetinaNet · you are here ✓ understood
Where it sits
RetinaNet
Leads to
Nothing yet: a destination in its own right.
Explore nearby
Vision & Multimodal YOLO You Only Look Once - a real-time object detection architecture treating detection as regression. Vision & Multimodal Faster R-CNN An object detection architecture with RPN for efficient region proposals. Vision & Multimodal Anchor Box Predefined boxes of various sizes and ratios serving as references for object detection.