Object Detection

Adding object detection datasets.

Uploading Object Detection Data

Object detection datasets require images and annotations.

Select your folder(s) of images and annotations.

The best way to do this is to click-and-drag the whole folder of images and annotations directly into Roboflow. Roboflow supports many annotation formats and dragging and dropping should just work. If your annotation format is not included in the list below: contact help@roboflow.ai and we will help add your annotation format into the list of supported formats.

You will know that your upload is successful when you see the progress bar move all the way to the right and you see you images enclosed in bounding boxes.

Supported Annotation Formats

Roboflow supports a wide array of annotation formats.

Supported annotation formats:

PASCAL VOC XML

PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) is a Network of Excellence funded by the European Union. From 2005 - 2012, PASCAL ran the Visual Object Challenge (VOC). PASCAL annually released object detection datasets and reported benchmarks. (An aggregated PASCAL VOC dataset is available here.)

<annotation>
<folder>train</folder>
<filename>01.jpg</filename>
<path>/roboflow/data/train/01.png</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>224</width>
<height>224</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>21</name>
<pose>Frontal</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<occluded>0</occluded>
<bndbox>
<xmin>82</xmin>
<xmax>172</xmax>
<ymin>88</ymin>
<ymax>146</ymax>
</bndbox>
</object>
</annotation>

COCO JSON

The Common Objects in Context (COCO) dataset originated in a 2014 paper Microsoft published. The dataset "contains photos of 91 objects types that would be easily recognizable by a 4 year old." There are a total of 2.5 million labeled instances across 328,000 images. Given the sheer quantity and quality of data open sourced, COCO has become a standard dataset for testing and proving state of the art performance in new models. (The dataset is available here.)

See an example in our post on how to convert annotations to COCO JSON.

TensorFlow Object Detection CSV

TensorFlow object detection CSVs contain one bounding box per line in the CSV.

filename

width

height

class

xmin

ymin

xmax

ymax

image_1.jpg

480

270

queen

173

24

260

137

image_1.jpg

480

270

queen

165

135

253

251

image_2.jpg

960

540

jack

255

96

337

208

image_2.jpg

960

540

jack

261

124

543

370

VOTT CSV

Microsoft's Visual Object Tagging Tool CSVs.

YOLO DarkNet .txt

Files that contain a .txt file for each image and a label_map.txt (or labels.txt) mapping the numeric classID to a class name.

More Supported Annotation Formats

Tensorflow tfrecord

Amazon Groundtruth

Supervisely JSON

LabelMap pbtxt

LabelBox JSON

VIA JSON

Create ML JSON

ScaleAI JSON

VOT JSON

Marmot XML

RectLabel XML

Udacity

YOLO Keras

Open Image txt

If you don't see support for your annotations, write us in our support chat or help@roboflow.ai.

Supported Image Formats

Roboflow supports uploading images in several formats. The most common are JPG, PNG, and BMP.

For TIFF support, upgrade to our Pro plan.

If you don't see support for your image format, write us in our support chat or help@roboflow.ai.