How does retina Net work?

How does retina Net work?

Introduction. RetinaNet is one of the best one-stage object detection models that has proven to work well with dense and small scale objects. RetinaNet has been formed by making two improvements over existing single stage object detection models – Feature Pyramid Networks (FPN) [1] and Focal Loss [2].

What is focal loss function?

Focal loss explanation Focal loss is just an extension of the cross-entropy loss function that would down-weight easy examples and focus training on hard negatives. So to achieve this, researchers have proposed: (1- pt)γ to the cross-entropy loss, with a tunable focusing parameter γ≥0.

Where is focal loss used?

A Focal Loss function addresses class imbalance during training in tasks like object detection. Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples.

Can focal loss be used for classification?

Binary classification Focal Loss can be interpreted as a binary cross-entropy function multiplied by a modulating factor (1- pₜ)^γ which reduces the contribution of easy-to-classify samples. Reducing the loss of easy to classify examples allows the training to focus more on hard-to-classify ones”.

Is RetinaNet better than faster RCNN?

Using larger scales allows RetinaNet to surpass the accuracy of all two-stage approaches, while still being faster. Except YOLOv2 (which targets on extremely high frame rate), RetinaNet outperforms SSD, DSSD, R-FCN and FPN.

Is Focal Loss useful?

Therefore, Focal Loss is particularly useful in cases where there is a class imbalance. Another example, is in the case of Object Detection when most pixels are usually background and only very few pixels inside an image sometimes have the object of interest.

What does alpha and gamma do in focal loss?

When γ = 0, focal loss is equivalent to categorical cross-entropy, and as γ is increased the effect of the modulating factor is likewise increased (γ = 2 works best in experiments). α(alpha): balances focal loss, yields slightly improved accuracy over the non-α-balanced form.

How do you use focal loss in keras?

2 Answers

1. Binary model. compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=[“accuracy”], optimizer=adam)
2. Categorical model. compile(loss=[categorical_focal_loss(alpha=[[.25, .25, .25]], gamma=2)], metrics=[“accuracy”], optimizer=adam)

How focal loss fixes the class imbalance?

The Focal Loss is designed to address the one-stage object detection scenario in which there is an extreme imbalance between foreground and background classes during training (e.g., 1:1000). Focal loss function acts as a more effective alternative to previous approaches for dealing with class imbalance.

How is focal loss used in retinanet detector?

In RetinaNet, an one-stage detector, by using focal loss, lower loss is contributed by “easy” negative samples so that the loss is focusing on “hard” samples, which improves the prediction accuracy.

Can a retinal detachment cause permanent vision loss?

Retinal detachment can cause permanent vision loss — but getting treatment right away can help protect your vision. What is retinal detachment? Retinal detachment is an eye problem that happens when your retina (a light-sensitive layer of tissue in the back of your eye) is pulled away from its normal position at the back of your eye.

What kind of network does retinanet use?

We will focus on the superior SSD. The image is fed to a standard architecture for high-quality image classification. Any classification layers at the end of the network are again truncated. The SSD paper uses a VGG-16 network, but other networks such as R esNets work as well.

How many boxes are there in retinanet object detection?

RetinaNet: ~100k. RetinaNet can have ~100k boxes with the resolve of class imbalance problem using focal loss. 2. Focal Loss 2.1. Cross Entropy (CE) Loss