mean-average-precision

Most popular metrics used to evaluate object detection algorithms.

yolo(all versions) implementation in keras and tensorflow 2.5

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

Online meter ploter for pytorch. Real time ploting Accuracy, Loss, mAP, AUC, Confusion Matrix

Information Retrieval with Vector Space Model for News Article

Using Faster RCNN to detect Scratches/Spots and Dents on damaged cars data

Unified framework to parse, create and evaluate object detections from many frameworks (COCO, YOLO, PascalVOC, ImageNet, LabelMe, ...).

Understanding of use of mAP as a metric for Objects Detection problems

A flow to compile YOLOv3/SSD using TVM and run the compiled model on CPU to calculate mAP

All scripts related to yoloV4 sliding window

Evaluates a given detection by calculating the mAP of the bounding box detections results based on a given test set and the detector code. This repo uses for now the yolov3_detector as an example detector for illustration

Based on Faster R-CNN, we train model on our mask dataset and leverage data augmentation to preprocess our data. Mean average precision is introduced to evaluate the model performance. You'll see data augmentation and mAP evaluation in detailed explainations, and tutorials of faster-rcnn training

Improving the performance of the information retrieval system by normalization of data .Elasticsearch engine was used and bm25 model was used to compare the performance of the IR system.