Thread by @ai_fast_track

🧐7 things you should know about the Focal Loss:

📌 It was introduced in the RetinaNet paper to address the foreground-background class imbalance encountered during training of dense detectors (one-stage detectors)
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📌 It’s derived from the cross-entropy loss such that it down-weights the loss assigned to well-classified examples. It's used in the classification head.

📌 It’s used in many one-stage object detection models: EfficientDet, FCOS, VFNet, and many other models

📌 It can also be used in two-stage object detection models: e.g. Sparse R-CNN

📌 It crashes losses associated to easy examples: for a confidence score of 0.9, the focal loss is 100 times smaller than the cross-entropy loss (see figure here above)

📌 Thanks to the focal loss, RetinaNet was the first one-stage detector model to beat two-stage detector models

📌 Focal loss can also be used in classification centered tasks (not only in object detection tasks)

🤔 Are there other things you would add to this list?

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