One of the important step in Object detection is to convert the Annotation file to corresponding format based on the Object detection model used.
- Pascal VOC
- COCO
- YOLO
The annotation format originally created for the Visual Object Challenge (VOC) has become a common interchange format for object detection labels. It's well-specified and can be exported from many labeling tools including CVAT, VoTT, and RectLabel.
Pascal VOC format is normally a .xml file. VOC format is as below.
<annotation>
<folder>images</folder>
<filename>maksssksksss0.png</filename>
<size>
<width>512</width>
<height>366</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>without_mask</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<occluded>0</occluded>
<difficult>0</difficult>
<bndbox>
<xmin>79</xmin>
<ymin>105</ymin>
<xmax>109</xmax>
<ymax>142</ymax>
</bndbox>
</object>
<object>
<name>with_mask</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<occluded>0</occluded>
<difficult>0</difficult>
<bndbox>
<xmin>185</xmin>
<ymin>100</ymin>
<xmax>226</xmax>
<ymax>144</ymax>
</bndbox>
</object>
</annotation>
The COCO bounding box format is [top left x position, top left y position, width, height]. The category id corresponds to a single category specified in the categories section. Each annotation also has an id (unique to all other annotations in the dataset).
COCO format is usually a .json file.
{
"images": [
{
"file_name": "918121_1557903451205255_561212074_n.jpg",
"height": 640,
"width": 640,
"id": "918121_1557903451205255_561212074_n"
}
],
"type": "instances",
"annotations": [
{
"area": 154192,
"iscrowd": 0,
"image_id": "918121_1557903451205255_561212074_n",
"bbox": [
66,
202,
419,
368
],
"category_id": 4,
"id": 1,
"ignore": 0,
"segmentation": []
},
{
"area": 79530,
"iscrowd": 0,
"image_id": "918121_1557903451205255_561212074_n",
"bbox": [
273,
47,
330,
241
],
"category_id": 4,
"id": 2,
"ignore": 0,
"segmentation": []
},
{
"area": 78430,
"iscrowd": 0,
"image_id": "918121_1557903451205255_561212074_n",
"bbox": [
0,
92,
310,
253
],
"category_id": 4,
"id": 3,
"ignore": 0,
"segmentation": []
}
],
"categories": [
{
"supercategory": "none",
"id": 0,
"name": "bracelet"
},
{
"supercategory": "none",
"id": 1,
"name": "earring"
},
{
"supercategory": "none",
"id": 2,
"name": "necklace"
},
{
"supercategory": "none",
"id": 3,
"name": "ring"
},
{
"supercategory": "none",
"id": 4,
"name": "watch"
}
]
}
In YOLO labeling format, a . txt file with the same name is created for each image file in the same directory. Each . txt file contains the annotations for the corresponding image file, that is object class, object coordinates, height and width.
<object-class> <x> <y> <width> <height>
Example:
car 45 55 29 67
bus 99 83 28 44
- If you want to convert Pascal VOC format to .txt then check the
README.mdfile in convert_VOC - If you want to convert Pascal VOC format to YOLO then check the
README.mdfile in convert_YOLO - If you want to convert Pascal VOC format to COCO then check the
README.mdfile in convert_VOC2COCO - If you want to convert COCO format to .txt then check the
README.mdfile in convert_COCO
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