A digital image is a grid. Each cell records colour or brightness at one position. Some systems work with small groups called patches, but the input still describes positions across an image.
See means look and report
The first everyday job is seeing. A classifier chooses a label. A detector finds objects and their locations. A segmenter marks which pixels belong to each object. These outputs describe something already in the image.
Fashion-MNIST ClothingCNN from the forward course is a see-and-classify model. It takes in a small grayscale pixel grid and returns one of ten clothing classes. It does not draw a new shirt.
Make means produce pixels
The second everyday job is making. A generative image model receives a description, an image, or an editing request and produces pixel values for a new or changed image. Its output is an image rather than a clothing label.
Bigger vision systems may classify, detect, caption, answer questions, or generate pictures. Their outputs differ. They still need an image-shaped input path when they start with a picture.
From the metal to the model starts with Metal rather than pixels or labels. It shows that every one of these choices still has a runtime and hardware path underneath. Profiling asks what that path costs on the real chip.
Next, put language and image architectures side by side.
Key concepts
- A digital image is a grid of visual values. Some systems also use patches.
- See jobs turn pixels into information: a label, boxes, or a mask.
- Fashion-MNIST ClothingCNN is see-and-classify. It does not draw a new shirt.
- Make jobs take a prompt or edit and produce new or changed pixels.
Takeaways
- You can split image models into see versus make.
- You can place ClothingCNN in the classify bucket, not the generative one.
- You can name classifier, detector, and segmenter outputs without mixing them.