Convert annotation formats

How To Convert Marmot XML to YOLOv5 PyTorch TXT

In this guide, we show you how to convert data between the Marmot and YOLOv5 formats for free. You can use your converted data to train YOLO-NAS models and other models that support the YOLOv5 format.

Marmot XML

Marmot is an obscure Chinese annotation format created by researchers at Peking University.

Below, learn the structure of Marmot XML.

marmot.ocr.xml
<?xml version="1.0" encoding="UTF-8"?>
<Page CropBox="0000000000000000 408a500000000000 4082980000000000 0000000000000000" PageNum="3" PageType="3">
    <Contents>
        <Leafs Label="Char">
            <Leaf BBox="4049d51666666667 4082b47f33333333 404d6f8ccccccccd 40828ee266666666" LID="1" Label="Char" PID="0-0" PLID="4273" />
            <Leaf BBox="404d99d5810624dd 4082c8a866666666 404ebe724dd2f1aa 4082900000000000" LID="2" Label="Char" PID="0-1" PLID="4273" />
        </Leafs>
        <Leafs Label="Image">
            <Leaf BBox="404f333333333333 408881999999999a 4071700000000000 40834b3333333334" LID="4146" Label="Image" PID="372" PLID="4405" />
        </Leafs>
        <Leafs Label="Path">
            <Leaf BBox="4075433333333333 40733ccccccccccd 40787ccccccccccd 40733ccccccccccd" LID="4147" Label="Path" PID="293-0" PLID="4411" />
            <Leaf BBox="40787ccccccccccd 4073466666666667 40787ccccccccccd 40733ccccccccccd" LID="4148" Label="Path" PID="293-1" PLID="4411" />
        </Leafs>
        <Composites Label="Textline">
            <Composite BBox="404a10600000001a 406702a1999999a3 4072152f3b645a1e 4065d98e6666666f" CLIDs="1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398" LID="4400" Label="Textline" PLID="4406" />
            <Composite BBox="407beee000000005 4072ef5800000010 407c6b28ccccccd2 4072733333333343" CLIDs="3292 3293" LID="4401" Label="Textline" PLID="4411" />
            <Composite BBox="407e68799999999f 4072ef5800000010 407ee37e6666666c 407271e333333343" CLIDs="3294 3295" LID="4402" Label="Textline" PLID="4411" />
            <Composite BBox="40797ee000000005 4072ef5800000010 4079f780ccccccd2 4072733333333343" CLIDs="3290 3291" LID="4403" Label="Textline" PLID="4411" />
        </Composites>
        <Composites Label="Formula">
            <Composite BBox="40616fa715810629 408097e502ca57a8 40692d8742de00d6 407fb89af212d771" CLIDs="243 236 246 245 235 233 238 242 229 234 239 231 240 230 237 244 241 232" LID="4404" Label="Formula" PLID="4423" />
        </Composites>
        <Composites Label="Figure">
            <Composite BBox="404f333333333333 408881999999999a 4071700000000000 4083020f99999999" CLIDs="4146 4290" LID="4405" Label="Figure" PLID="4423" />
        </Composites>
        <Composites Label="List">
            <Composite BBox="4073320c00000002 40864a4200000003 40810c1f978d4fdf 4081ab5933333338" CLIDs="4328 4330 4329 4331 4333 4332 4335 4334 4338 4337 4336 4339 4399" LID="4408" Label="List" PLID="4423" />
            <Composite BBox="4073320c00000002 40813a4200000005 408106d3eb1c432d 407b842b33333341" CLIDs="4343 4347 4344 4342 4346 4349 4348 4345 4350 4398" LID="4409" Label="List" PLID="4423" />
        </Composites>
        <Composites Label="TableCaption">
            <Composite BBox="4074145c00000005 4074dd95999999a9 4080921e74bc6a80 4073adb8ccccccdc" CLIDs="4357 4358" LID="4410" Label="TableCaption" PLID="4412" />
        </Composites>
        <Composites Label="TableBody">
            <Composite BBox="4075433333333333 4073466666666667 407fe00000000000 406fb9999999999a" CLIDs="4212 4228 4168 4236 4193 4169 4161 4245 4361 4174 4181 4172 4196 4235 4206 4227 4199 4401 4162 4182 4360 4192 4184 4204 4230 4175 4211 4151 4187 4200 4226 4366 4376 4242 4156 4197 4159 4213 4371 4365 4153 4233 4403 4194 4225 4189 4154 4222 4251 4210 4246 4171 4223 4207 4150 4186 4149 4217 4163 4367 4220 4370 4237 4155 4176 4170 4201 4375 4249 4218 4157 4243 4152 4178 4214 4241 4232 4244 4185 4359 4173 4377 4205 4195 4164 4160 4188 4166 4179 4183 4372 4208 4234 4209 4191 4238 4165 4180 4203 4221 4147 4248 4177 4216 4229 4148 4202 4239 4198 4224 4240 4231 4158 4402 4215 4247 4167 4250 4219 4190 4362" LID="4411" Label="TableBody" PLID="4412" />
        </Composites>
        <Composites Label="Table">
            <Composite BBox="4074145c00000005 4074dd95999999a9 4080921e74bc6a80 406fb9999999999a" CLIDs="4410 4411" LID="4412" Label="Table" PLID="4423" />
        </Composites>
        <Composites Label="Paragraph">
            <Composite BBox="4049c85666666676 407f748400000000 407220a1141205be 407a904c00000002" CLIDs="4285 4287 4286 4284 4288 4283 4289" LID="4416" Label="Paragraph" PLID="4423" />
            <Composite BBox="4049aed666666676 40797e1d9999999b 40570056a4a8c15c 40790ae333333334" CLIDs="4291" LID="4417" Label="Paragraph" PLID="4423" />
        </Composites>
        <Composites Label="Body">
            <Composite BBox="4049aed666666676 408881999999999a 40810c1f978d4fdf 404d33066666665f" CLIDs="4407 4412 4418 4414 4405 4422 4420 4409 4416 4406 4419 4413 4404 4417 4421 4408" LID="4423" Label="Body" PLID="0" />
        </Composites>
    </Contents>
</Page>

YOLOv5 PyTorch TXT

A modified version of YOLO Darknet annotations that adds a YAML file for model config.

Below, learn the structure of YOLOv5 PyTorch TXT.

Each image has one txt file with a single line for each bounding box. The format of each row is

class_id center_x center_y width height

where fields are space delimited, and the coordinates are normalized from zero to one.

Note: To convert to normalized xywh from pixel values, divide x (and width) by the image's width and divide y (and height) by the image's height.

001.txt
1 0.617 0.3594420600858369 0.114 0.17381974248927037
1 0.094 0.38626609442060084 0.156 0.23605150214592274
1 0.295 0.3959227467811159 0.13 0.19527896995708155
1 0.785 0.398068669527897 0.07 0.14377682403433475
1 0.886 0.40879828326180256 0.124 0.18240343347639484
1 0.723 0.398068669527897 0.102 0.1609442060085837
1 0.541 0.35085836909871243 0.094 0.16952789699570817
1 0.428 0.4334763948497854 0.068 0.1072961373390558
1 0.375 0.40236051502145925 0.054 0.1351931330472103
1 0.976 0.3927038626609442 0.044 0.17167381974248927

The `data.yaml` file contains configuration values used by the model to locate images and map class names to class_id's.

data.yaml
train: ../train/images
val: ../valid/images

nc: 3
names: ['head', 'helmet', 'person']

Step 1: Create a free Roboflow public workspace

Roboflow is a universal conversion tool for computer vision annotation formats. The Public plan is the best way for those exploring personal projects, class assignments, and other experiments to try Roboflow. To convert your dataset, start by creating a free workspace on the Public plan.

Create Roboflow Workspace

Step 2: Upload your data into Roboflow

Once your account has been created, click Create New Project. (opens in a new tab)

Roboflow's create project form, with Object Detection selected as the project type

Upload your data to Roboflow by dragging and dropping your Marmot XML images and annotations into the upload space.

Roboflow's upload page, with a drag-and-drop area for images, annotations, videos and PDFs

Step 3: Generate Dataset Version

Next, click "Generate New Version" to generate a new version of your dataset:

Roboflow's Generate a Dataset Version page, showing the source images and train/test split for a new version

You can then apply any preprocessing or augmentation steps to your dataset:

The augmentation step of a new dataset version, with 90° Rotate and Brightness added

Step 4: Export Dataset Version

After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. Choose YOLOv5 PyTorch TXT when asked in what format you want to export your data. You will see a dropdown with various options like this:

Roboflow's Download dialog, with the export format list open and COCO highlighted

Congratulations, you have successfully converted your dataset from Marmot XML format to YOLOv5 PyTorch TXT format!

Video Tutorial

Convert Computer Vision Annotation Formats Tutorial

Want to dive deeper into converting annotation formats with Roboflow? In the tutorial below, we explore how to convert your data in the Roboflow dashboard. We also discuss rejecting annotations and train-test-validation splits.

Frequently Asked Questions

Yes! It is free to convert Marmot XML data into the YOLOv5 PyTorch TXT format on the Roboflow platform.

If you have between a few and a few thousand images, converting data between these formats will be quick. But, the time it takes to convert between data formats increases with the more images you have.

Show guide

Convert Marmot XML to other formats

Convert other formats to YOLOv5 PyTorch TXT

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