computer-vision

An aggregate metric tells you a model is worse than you hoped and nothing about why. Evaluation that writes results back onto each sample turns a number into a set of images you can look at. This lesson covers the evaluation methods and their protocols, per-sample true and false positive counts, how a confusion matrix cell becomes a view, and where this workflow stops.

A query can only ask about what is recorded, so the harder half of curation is generating fields that surface where to look. This lesson covers the analysis methods FiftyOne bundles as the Brain: embedding visualisation and its four reduction methods, uniqueness and representativeness, mistakenness and hardness, and similarity indexes for near-duplicate detection and text search.

A view is a filtered, sorted or sliced window onto a dataset, built by chaining stages and computed lazily without touching the underlying data. This lesson covers what that buys, the distinction between filtering samples and filtering the labels inside them that catches nearly everyone, how views compose with the App and with evaluation, and when a view should be turned into a real subset.

Most computer vision projects are limited by their dataset rather than their architecture, and most teams cannot see their dataset. This lesson covers why a tool for inspecting visual data exists, the data model FiftyOne uses to make a dataset queryable rather than a folder of files, why it sits on a database, and where curation fits against annotation and experiment tracking.

What differentiable rendering is actually used for, how it relates to NeRF and Gaussian splatting, and the four failure modes that make inverse rendering harder than the optimisation loop suggests.

How the field learned to represent people influencing each other: the physics-inspired force model, the Social LSTM pooling layer that replaced hand-designed rules with learned ones, and the attention and graph architectures that followed.

Why forecasting human motion is not a physics problem: the multimodality that makes a single correct answer impossible, the social conventions people navigate by, and the joint prediction problem where everyone is predicting everyone else.

From photons to 3D geometry: the pinhole model, intrinsic matrix K, lens distortion, feature matching, stereo depth, and where CNNs help (and fail) in robot perception pipelines.

Understand why fully-connected layers fail at image scale, then build up the CNN toolkit: convolutions, kernels, stride, padding, feature maps, pooling, and parameter sharing. Finish with the ResNet residual connection idea that unlocked networks of 100+ layers.