Data preparation¶

Dataset format introduction¶

Download coco2017 YOLO format coco2017labels-segments and coco2017 original images train2017 , val2017 , then put the coco2017 original images into the coco2017 YOLO format images directory:

└─ coco2017_yolo
    ├─ annotations
        └─ instances_val2017.json
    ├─ images
        ├─ train2017   # coco2017 原始图片
        └─ val2017     # coco2017 原始图片
    ├─ labels
        ├─ train2017
        └─ val2017
    ├─ train2017.txt
    ├─ val2017.txt
    └─ test-dev2017.txt

Each line of the train.txt file corresponds to the relative path of a single image, for example:

./images/train2017/00000000.jpg
./images/train2017/00000001.jpg
./images/train2017/00000002.jpg
./images/train2017/00000003.jpg
./images/train2017/00000004.jpg
./images/train2017/00000005.jpg

The txt files in the train2017 folder under labels are the annotation information of the corresponding images, supporting both detect and segment formats.

Detect format: Usually each row has 5 columns, corresponding to the category id and the center coordinates xy and width and height wh after normalization of the annotation box

62 0.417040 0.206280 0.403600 0.412560
62 0.818810 0.197933 0.174740 0.189680
39 0.684540 0.277773 0.086240 0.358960
0 0.620220 0.725853 0.751680 0.525840
63 0.197190 0.364053 0.394380 0.669653
39 0.932330 0.226240 0.034820 0.076640

segment format: the first data in each line is the category id, followed by pairs of normalized coordinate points x, y

45 0.782016 0.986521 0.937078 0.874167 0.957297 0.782021 0.950562 0.739333 0.825844 0.561792 0.714609 0.420229 0.657297 0.391021 0.608422 0.4 0.0303438 0.750562 0.0016875 0.811229 0.003375 0.889896 0.0320156 0.986521
45 0.557859 0.143813 0.487078 0.0314583 0.859547 0.00897917 0.985953 0.130333 0.984266 0.184271 0.930344 0.386521 0.80225 0.480896 0.763484 0.485396 0.684266 0.39775 0.670781 0.3955 0.679219 0.310104 0.642141 0.253937 0.561234 0.155063 0.559547 0.137083
50 0.39 0.727063 0.418234 0.649417 0.455297 0.614125 0.476469 0.614125 0.51 0.590583 0.54 0.569417 0.575297 0.562354 0.601766 0.56 0.607062 0.536479 0.614125 0.522354 0.637063 0.501167 0.665297 0.48 0.69 0.477646 0.698828 0.494125 0.698828 0.534125 0.712938 0.529417 0.742938 0.548229 0.760594 0.564708 0.774703 0.550583 0.778234 0.536479 0.781766 0.531771 0.792359 0.541167 0.802937 0.555292 0.802937 0.569417 0.802937 0.576479 0.822359 0.576479 0.822359 0.597646 0.811766 0.607062 0.811766 0.618833 0.818828 0.637646 0.820594 0.656479 0.827641 0.687063 0.827641 0.703521 0.829406 0.727063 0.838234 0.708229 0.852359 0.729417 0.868234 0.750583 0.871766 0.792938 0.877063 0.821167 0.884125 0.861167 0.817062 0.92 0.734125 0.976479 0.711172 0.988229 0.48 0.988229 0.494125 0.967063 0.517062 0.912937 0.508234 0.832937 0.485297 0.788229 0.471172 0.774125 0.395297 0.729417
45 0.375219 0.0678333 0.375219 0.0590833 0.386828 0.0503542 0.424156 0.0315208 0.440797 0.0281458 0.464 0.0389167 0.525531 0.115583 0.611797 0.222521 0.676359 0.306583 0.678875 0.317354 0.677359 0.385271 0.66475 0.394687 0.588594 0.407458 0.417094 0.517771 0.280906 0.604521 0.0806562 0.722208 0.0256719 0.763917 0.00296875 0.809646 0 0.786104 0 0.745083 0 0.612583 0.03525 0.613271 0.0877187 0.626708 0.130594 0.626708 0.170437 0.6025 0.273844 0.548708 0.338906 0.507 0.509906 0.4115 0.604734 0.359042 0.596156 0.338188 0.595141 0.306583 0.595141 0.291792 0.579516 0.213104 0.516969 0.129042 0.498297 0.100792 0.466516 0.0987708 0.448875 0.0786042 0.405484 0.0705208 0.375219 0.0678333 0.28675 0.108375 0.282719 0.123167 0.267078 0.162854 0.266062 0.189083 0.245391 0.199833 0.203516 0.251625 0.187375 0.269771 0.159641 0.240188 0.101125 0.249604 0 0.287271 0 0.250271 0 0.245563 0.0975938 0.202521 0.203516 0.145354 0.251953 0.123167 0.28675 0.108375
49 0.587812 0.128229 0.612281 0.0965625 0.663391 0.0840833 0.690031 0.0908125 0.700109 0.10425 0.705859 0.133042 0.700109 0.143604 0.686422 0.146479 0.664828 0.153188 0.644672 0.157042 0.629563 0.175271 0.605797 0.181021 0.595 0.147437
49 0.7405 0.178417 0.733719 0.173896 0.727781 0.162583 0.729484 0.150167 0.738812 0.124146 0.747281 0.0981458 0.776109 0.0811875 0.804094 0.0845833 0.814266 0.102667 0.818516 0.115104 0.812578 0.133208 0.782906 0.151292 0.754063 0.172771
49 0.602656 0.178854 0.636125 0.167875 0.655172 0.165125 0.6665 0.162375 0.680391 0.155521 0.691719 0.153458 0.703047 0.154146 0.713859 0.162375 0.724156 0.174729 0.730844 0.193271 0.733422 0.217979 0.733938 0.244063 0.733422 0.281813 0.732391 0.295542 0.728266 0.300354 0.702016 0.294854 0.682969 0.28525 0.672156 0.270146
49 0.716891 0.0519583 0.683766 0.0103958 0.611688 0.0051875 0.568828 0.116875 0.590266 0.15325 0.590266 0.116875 0.613641 0.0857083 0.631172 0.0857083 0.6565 0.083125 0.679875 0.0883125 0.691563 0.0961042 0.711031 0.0649375

instances_val2017.json is the verification set annotation in coco format, which can directly call coco api for map calculation.

During training & reasoning, you need to modify train_set, val_set, test_set in configs/coco.yaml to the actual data path

For actual examples of using MindYOLO kit to complete custom dataset finetune, please refer to Finetune