Quick-start
To start using the mAP you need to clone the repo:
git clone https://github.com/TianyouChen/mAP.git
Running the code
Step by step:
1.Create the ground-truth files 2.Move the ground-truth files into the folder ground-truth/ 3.Create the predicted objects files 4.Move the predictions files into the folder predicted/ 5.Run the code: python main.py
Optional (if you want to see the animation):
6.Insert the images into the folder images/
Create the ground-truth files
Create a separate ground-truth text file for each image. Use matching names (e.g. image: "image_1.jpg", ground-truth: "image_1.txt"; "image_2.jpg", "image_2.txt"...). In these files, each line should be in the following format: <class_name> [] The difficult parameter is optional, use it if you want to ignore a specific prediction. E.g. "image_1.txt": tvmonitor 2 10 173 238 book 439 157 556 241 book 437 246 518 351 difficult pottedplant 272 190 316 259 Create the predicted objects files
Create a separate predicted objects text file for each image. Use matching names (e.g. image: "image_1.jpg", predicted: "image_1.txt"; "image_2.jpg", "image_2.txt"...). In these files, each line should be in the following format: <class_name> E.g. "image_1.txt": tvmonitor 0.471781 0 13 174 244 cup 0.414941 274 226 301 265 book 0.460851 429 219 528 247 chair 0.292345 0 199 88 436 book 0.269833 433 260 506 336