# Object Detection

Object detection is a form of supervised learning where a model is trained to identify 
and categorize objects within images. AutoTrain simplifies the process, enabling you to
train a state-of-the-art object detection model by simply uploading labeled example images.


## Preparing your data

To ensure your object detection model trains effectively, follow these guidelines for preparing your data:


### Organizing Images


Prepare a zip file containing your images and metadata.jsonl.


```
Archive.zip
├── 0001.png
├── 0002.png
├── 0003.png
├── .
├── .
├── .
└── metadata.jsonl
```

Example for `metadata.jsonl`:

```
{"file_name": "0001.png", "objects": {"bbox": [[302.0, 109.0, 73.0, 52.0]], "category": [0]}}
{"file_name": "0002.png", "objects": {"bbox": [[810.0, 100.0, 57.0, 28.0]], "category": [1]}}
{"file_name": "0003.png", "objects": {"bbox": [[160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0]], "category": [2, 2]}}
```

Please note that bboxes need to be in COCO format `[x, y, width, height]`.


### Image Requirements

- Format: Ensure all images are in JPEG, JPG, or PNG format.

- Quantity: Include at least 5 images to provide the model with sufficient examples for learning.

- Exclusivity: The zip file should exclusively contain images and metadata.jsonl.
No additional files or nested folders should be included.


Some points to keep in mind:

- The images must be jpeg, jpg or png.
- There should be at least 5 images per split.
- There must not be any other files in the zip file.
- There must not be any other folders inside the zip folder.

When train.zip is decompressed, it creates no folders: only images and metadata.jsonl.

## Parameters

[[autodoc]] trainers.object_detection.params.ObjectDetectionParams
