> For the complete documentation index, see [llms.txt](https://annotate-docs.dwaste.live/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://annotate-docs.dwaste.live/fundamentals/set-up-and-run/outputs.md).
# Outputs
One can download the annotated image into various formats. Following are the examples of annotated image of an orange.
Annotated Image
Masked of Annotated Image
The configuration can be downloaded using built in configuration or using YOLO format.
```json
{
"orange.png":{
"configuration":[
{
"image-name":"orange.png",
"regions":[
{
"region-id":"47643630436867834",
"image-src":"http://127.0.0.1:5000/uploads/orange.png",
"class":"Orange",
"comment":"",
"tags":"",
"points":[
[
0.4685613390092879,
0.7693498452012384
],
[
0.6781491873065015,
0.6640866873065016
],
[
0.723921246130031,
0.5092879256965944
],
[
0.7480118034055728,
0.34055727554179566
],
[
0.5841960139318886,
0.14705882352941177
],
[
0.41917569659442727,
0.13312693498452013
],
[
0.30113196594427244,
0.22755417956656346
],
[
0.21079237616099072,
0.4411764705882353
],
[
0.26620065789473685,
0.6764705882352942
],
[
0.4011077786377709,
0.7879256965944272
]
]
},
{
"region-id":"5981359766055432",
"image-src":"http://127.0.0.1:5000/uploads/orange.png",
"class":"Apple",
"comment":"",
"tags":"",
"x":[
0.1770655959752322
],
"y":[
0.11764705882352941
],
"w":[
0.5854005417956657
],
"h":[
0.6981424148606811
]
}
],
"color-map":{
"Orange":[
244,
67,
54
],
"Apple":[
33,
150,
243
]
}
}
]
}
}
```
### **YOLO Format**
[YOLO format](https://docs.ultralytics.com/datasets/detect/#ultralytics-yolo-format) is also supported by A.Lab. Below is an example of annotated ripe and unripe tomatoes. In this example, `0` represents ripe tomatoes and `1` represents unripe ones.
Tomatoes Image
Annotating it in A.Lab.
Annotating Tomatoes
The label of the above image are as follows:
```
0 0.213673 0.474717 0.310212 0.498856
0 0.554777 0.540507 0.306350 0.433638
1 0.378432 0.681239 0.223970 0.268879
```
Applying the generated labels we get following results.
Annotated Tomatoes using Label values
### Normalization process of YOLO annotations
**Example Conversion**
To convert non-normalized bounding box coordinates (xmax, ymax, xmin, ymin) to YOLO format (xcenter, ycenter, width, height):
YOLO Annotation Normalization (Credit: Leandro de Oliveira)
```python
# Assuming row contains your bounding box coordinates
row = {'xmax': 400, 'xmin': 200, 'ymax': 300, 'ymin': 100}
class_id = 0 # Example class id (replace with actual class id)
# Image dimensions
WIDTH = 640 # annotated image width
HEIGHT = 640 # annotated image height
# Calculate width and height of the bounding box
width = row['xmax'] - row['xmin']
height = row['ymax'] - row['ymin']
# Calculate the center of the bounding box
x_center = row['xmin'] + (width / 2)
y_center = row['ymin'] + (height / 2)
# Normalize the coordinates
normalized_x_center = x_center / WIDTH
normalized_y_center = y_center / HEIGHT
normalized_width = width / WIDTH
normalized_height = height / HEIGHT
# Create the annotation string in YOLO format
content = f"{class_id} {normalized_x_center} {normalized_y_center} {normalized_width} {normalized_height}"
print(content)
```
The above conversion will give us YOLO format string.
```txt
0 0.46875 0.3125 0.3125 0.3125
```
### **COCO Format**
[COCO JSON format](https://roboflow.com/formats/coco-json) is also supported by A.Lab. Below is an example of annotated ripe and unripe tomatoes. In this example, `0` represents ripe tomatoes and `1` represents unripe ones.
```json
{
"info": {
"description": "COCO Format Annotations",
"url": "http://127.0.0.1:5000/",
"version": "1.0",
"year": 2026,
"contributor": "Annotate Lab",
"date_created": "2026-03-25T23:43:54.234369"
},
"licenses": [
{
"id": 1,
"name": "Unknown",
"url": ""
}
],
"images": [
{
"id": 1,
"file_name": "glass_543.jpg",
"width": 474,
"height": 840,
"license": 1,
"date_captured": "2026-03-25T23:43:54.240259",
"original_name": "glass_543"
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 0,
"bbox": [27.71, 189.24, 147.04, 419.04],
"area": 61612.0,
"segmentation": [],
"iscrowd": 0
},
{
"id": 2,
"image_id": 1,
"category_id": 0,
"bbox": [190.21, 272.89, 145.21, 364.25],
"area": 52920.0,
"segmentation": [],
"iscrowd": 0
},
{
"id": 3,
"image_id": 1,
"category_id": 1,
"bbox": [126.22, 459.97, 106.16, 225.86],
"area": 23960.0,
"segmentation": [],
"iscrowd": 0
}
],
"categories": [
{
"id": 0,
"name": "ripe",
"supercategory": "tomato"
},
{
"id": 1,
"name": "unripe",
"supercategory": "tomato"
}
]
}
```