Learn about FCOS Object Detection and how AnchorFree methods works
Hello trend,
This is Satya Mallick from LearnOpenCV.com.
Almost all popular object detectors such as RetinaNet, SSD, YOLO, and Faster R-CNN rely on pre-defined anchor boxes.
Fully Convolutional One-stage (FCOS) Object Detection is an anchor-free (anchorless) object detector. It solves object detection problems in a per-pixel prediction fashion, similar to segmentation. Most of the recent anchor-free or anchorless deep learning-based object detectors use FCOS as a basis.
In today's blog post, you will learn about the following:
- Fundamentals of FCOS: The model architecture, ground truth encoding, prediction decoding, and loss function.
- The intuitive explanation of all fundamental concepts.
- Where FCOS stands compared to other object detection models.
- How to use FCOS with PyTorch and TorchVision on images and videos.
Without further ado, let's jump into the post
Accompanying Jupyter Notebook for using FCOS in PyTorch can be found on our Github repo. Your encouragement is the driving force behind our resources/blog. Do give us a star on Github.
Cheers!
Satya
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