Deep Learning

Usual object detection algorithms are all based on Anchors; although this generates many useless anchor boxes and brings a time-consuming NMS post-processing step, the accuracy of the algorithm is higher, because the anchor boxes basically cover all object regions. There is another approach in the detection field based on Anchor free, that is, without anchor boxes; as far as I know these are mainly Cornernet, Centernet (Keypoint Triplets for Object Detection) and Centernet (Objects as Points). This type of algorithm does not need to generate anchor boxes, and therefore does not need the time-consuming NMS post-processing. Here I mainly record the processing method of Centernet (Objects as Points). ...

Having studied so many deep learning algorithms, the loss function is always an unavoidable point, but fortunately the loss functions used in general are fairly fixed, with occasional changes made as things go. This post mainly lists some commonly used loss functions. L1-loss The L1 loss function, also known as Mean Absolute Error (MAE). Its mathematical expression is as follows: $$ MAE = \frac{1}{n}\sum^n_{i=1}\vert f(x_i)-y_i\vert $$where $n$ denotes the number of samples in the training batch, $f(x_i)$ denotes the value predicted by the model at sample $x_i$, and $y_i$ is the ground truth value corresponding to sample $x_i$. ...

At the end of 2019 and the beginning of 2020, Google Brain published two papers proposing a fast and accurate model for image classification and for object detection respectively, naming them EfficientNet and EfficientDet. The authors also adjusted the model parameters to obtain a series of efficient classification and detection models. EfficientNet From previous work and their own experiments, the authors summarized two observations: Scaling any single dimension of a model (depth, width, resolution) improves the model’s accuracy, but for very large scaling factors the accuracy gain diminishes In scaling a convolutional network, scaling all dimensions of the model in a balanced way yields better accuracy and speed compound scaling Based on the two observations above, the authors no longer use the traditional single-dimension model scaling but scale all dimensions of the model (depth, width and resolution). ...

IoU is an important metric in object detection; it measures how good a predicted box is by computing the ratio of the intersection to the union of the predicted box and the ground truth box. But the usual bounding box adjustment function generally adopts the L2 norm, that is, the loss is computed in an MSE manner, and some studies have shown that this is not the optimal optimization function, hence the IOU loss functions. ...