The PCB Classification model detects PCB missing component defects using component level images extracted from a PCB.
The training algorithm optimizes the network to minimize the cross-entropy loss.
This model was trained using the classification_pyt entrypoint in TAO Toolkit 5.0.
The primary use case intended for these models is detecting missing component defects in RGB PCB component level images. The model can be used to classify objects from photos and videos by using appropriate video or image decoding and pre-processing. The model is a binary classifier which predicts whether a component is present or missing.
The datasheet for the model is captured in its model card hosted at NGC.