What is loss function for object detection?

What is loss function for object detection?

Loss functions is a crucial factor that affecting the detection precision in object detection task. Firstly, by multiplying an IoU-based coefficient by the standard cross entropy loss in classification loss function, the correlation between localization and classification is established.

Which of the loss functions can be used in the case of object detection task?

Object Detection

  • Loss Functions. Classification Loss. Regression Loss.
  • Introduction to Object Detection. Viola-Jones. OverFeat. R-CNN. Fast R-CNN. Faster R-CNN. R-FCN. SSD. YOLO. YOLOv1. YOLOv2. YOLOv3. RetinaNet. Backbones. MobileNet. ResNeXt. FPN.
  • Recap.

What is Objectness loss?

With each box prediction is associated a prediction called ‘objectness’. The objectness loss term teaches the network to predict a correct IoU, while the coordinate loss teaches the network to predict a better box (which eventually pushes the IoU toward 1.0).

What is the loss function in YOLOv3?

The loss function composes of: the classification loss. the localization loss (errors between the predicted boundary box and the ground truth). the confidence loss (the objectness of the box).

What is SSD in object detection?

SSD is a single-shot detector. It has no delegated region proposal network and predicts the boundary boxes and the classes directly from feature maps in one single pass. To improve accuracy, SSD introduces: small convolutional filters to predict object classes and offsets to default boundary boxes.

What are the applications of object detection?

Object detection is applied in numerous territories of image processing, including picture retrieval, security, observation, computerized vehicle systems and machine investigation.

What is the accuracy of YOLOv3?

YOLOv3 is extremely fast and accurate. In mAP measured at . 5 IOU YOLOv3 is on par with Focal Loss but about 4x faster. Moreover, you can easily tradeoff between speed and accuracy simply by changing the size of the model, no retraining required!

What do the losses mean in TensorFlow object detection API?

Loss/RPNLoss/localization_loss/mul_1: Localization Loss or the Loss of the Bounding Box regressor for the RPN Loss/RPNLoss/objectness_loss/mul_1: Loss of the Classifier that classifies if a bounding box is an object of interest or background The losses for the Final Classifier:

Why are loss functions important in Yolo object detectors?

Often times, we use an open-sourced, prebuilt model, adjusting the last layers and the loss functions to accomplish our task. The loss functions of one-stage object detectors, where one CNN produces the bounding box and class predictions, can be somewhat unusual because the prediction tensors are used to construct the truth tensor.

How are loss functions used in regression tasks?

The loss functions for Regression tasks tend to take values directly from the last layer of the the network without having to turn those values into probabilities. You can refer to this article by Deep Learning Demystified to learn more about Loss Functions.

How are one shot object detection methods imbalanced?

One-shot object detection methods train the model on more than thousands grids with different scale, but the number of objects in one image is much less. The distribution of foreground (object) and background is extremely imbalanced. THe rest of the post is focused on the 3 different ways to overcome this problem.