YOLOv1

Introduced by Redmon et al. in You Only Look Once: Unified, Real-Time Object Detection

YOLOv1 is a single-stage object detection model. Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.

The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image.

Source: You Only Look Once: Unified, Real-Time Object Detection

Latest Papers

PAPER DATE
Machine learning approach of automatic identification and counting of blood cells
| Mohammad Mahmudul AlamMohammad Tariqul Islam
2019-09-05
Light-Weight RetinaNet for Object Detection
| Yixing LiFengbo Ren
2019-05-24
You Only Look Once: Unified, Real-Time Object Detection
| Joseph RedmonSantosh DivvalaRoss GirshickAli Farhadi
2015-06-08

Tasks

TASK PAPERS SHARE
Object Detection 3 42.86%
Blood Cell Count 1 14.29%
CBC TEST 1 14.29%
Medical Diagnosis 1 14.29%
Real-Time Object Detection 1 14.29%

Categories