DeepLab models, first debuted in ICLR ‘14, are a series of deep learning architectures designed to tackle the problem of semantic segmentation. After making iterative refinements through the years, ...
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YOLOR – Paper Explanation & Inference – An In-Depth Analysis
In recent years, we have seen tremendous progress in the YOLO series, now hosting both anchor-free and anchor-based object detection models. Instead of focusing solely on architectural changes, YoloR ...
Roadmap To an Automated Image Annotation Tool Using OpenCV Python
Annotation is the most crucial part of a Deep Learning project. It is a deciding factor in how well a model learns. However, it is very tedious and time-consuming. One solution is to use an automated ...
Performance Comparison of YOLO Object Detection Models – An Intensive Study
If you are undertaking an object detection project, the probability is high that you would choose one of the many YOLO models. Going by the number of YOLO object detection models out there, it's a ...
FCOS- Anchor Free Object Detection Explained
FCOS: Fully Convolutional One-stage Object Detection is an anchor-free (anchorless) object detector. It solves object detection problems in a per-pixel prediction fashion, similar to segmentation. ...
YOLOv6 Custom Dataset Training – Underwater Trash Detection
The field of object detection is coming across a new YOLO model release every few months. Although reading the paper should be the first step towards exploring the model, we can evaluate the model ...
