Image Classification and Foundational Vision Models

March 7, 2022 · 3 min read

Process #

1. Collect data

2. Define the model

   Usually a function containing parameter variables: \(y=F_{\Theta}(X)\)

   Example: \(y=\sigma\left(\Theta^{T} X\right)\)

3. Train Find the optimal parameters \(\Theta^{*}\) so that the model \(y=F_{\Theta^{*}}(X)\) achieves the highest accuracy on the training set

4. Predict For a new image \(\hat{X}\) , use the trained model to predict its class, namely \(\hat{y}=F_{\Theta^{*}}(\hat{X})\)