DETR revolutionizes object detection by integrating a transformer model, traditionally used in natural language processing, into the realm of computer vision. It simplifies the object detection pipeline by eliminating the need for many hand-designed components.
DETR addresses the complexity and inefficiencies of prior object detection systems, offering a streamlined and theoretically elegant approach.
DETR combines a Convolutional Neural Network (CNN) backbone with a transformer model. The CNN extracts features from input images, and the transformer processes these features to predict object classes and bounding boxes.
Extracts feature maps from the input image. Commonly, ResNet is used for this purpose.
Processes the feature map from the CNN. It consists of self-attention and feed-forward layers.
Generates object queries. Each query predicts an object in the image, using cross-attention with the encoder's output.
These networks predict the final class and bounding box for each object query.
Key parameters include the number of encoder and decoder layers, attention heads, and feed-forward network dimensions. DETR is trained with standard backpropagation using AdamW optimizer, learning rate scheduling, and a custom loss function. Techniques like dropout and layer normalization are used for stable training.
DETR treats object detection as a set prediction problem. Each decoder output corresponds to a prediction of an object.
The model processes the entire set of objects at once, in parallel, unlike traditional methods that process objects sequentially.
DETR uses the Hungarian algorithm for matching predicted and ground truth objects, calculating a combined loss of classification and bounding box regression.
Simplifies the object detection pipeline, handles occlusions and overlapping objects well, and is end-to-end trainable.
DETR can be slower to train due to the global nature of self-attention and might struggle with very small objects.