COCO format

[zivp100]

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Question

Hey, I have a dataset for object detection in a COCO format, but it is different then the format you are looking for. My json file looks like the attached. Is there a way for me to load my data? custom_train (4).json

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[github-actions[bot]]

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[pderrenger]

@zivp100 hello,

Thank you for reaching out! If your dataset is in COCO format but differs from the expected structure for Ultralytics HUB, you can convert it to the required format. Ultralytics HUB datasets follow the same structure and label formats as YOLOv5 and YOLOv8 datasets.

Here are the steps to convert your COCO format dataset to the required format:

1. Extract Annotations and Images: Ensure your images and annotations are organized in a directory structure similar to the example below:

   dataset/
   ├── images/
   │   ├── train/
   │   ├── val/
   └── labels/
       ├── train/
       └── val/

2. Convert COCO JSON to YOLO Format: You can use a script to convert your COCO annotations to YOLO format. Here is an example script to help you get started:

   import json
   import os
   
   def convert_coco_to_yolo(coco_json_path, output_dir):
       with open(coco_json_path) as f:
           data = json.load(f)
   
       images = {image['id']: image for image in data['images']}
       annotations = data['annotations']
   
       for ann in annotations:
           image_id = ann['image_id']
           image_info = images[image_id]
           image_filename = image_info['file_name']
           image_width = image_info['width']
           image_height = image_info['height']
   
           category_id = ann['category_id']
           bbox = ann['bbox']
           x_center = (bbox[0] + bbox[2] / 2) / image_width
           y_center = (bbox[1] + bbox[3] / 2) / image_height
           width = bbox[2] / image_width
           height = bbox[3] / image_height
   
           yolo_annotation = f"{category_id} {x_center} {y_center} {width} {height}\n"
   
           label_filename = os.path.splitext(image_filename)[0] + '.txt'
           label_filepath = os.path.join(output_dir, label_filename)
   
           with open(label_filepath, 'a') as label_file:
               label_file.write(yolo_annotation)
   
   # Example usage
   convert_coco_to_yolo('path/to/custom_train.json', 'path/to/output/labels/train')

3. Create Dataset YAML File: Ensure you have a dataset YAML file in the root directory of your dataset. It should look something like this:

   path: ../datasets/your_dataset_name
   train: images/train
   val: images/val
   test:  # optional
   names:
     0: class_name_1
     1: class_name_2
     # Add all your class names here

4. Zip and Upload: Once your dataset is structured correctly, zip the directory and upload it to Ultralytics HUB. You can follow the detailed instructions in the Ultralytics HUB Datasets documentation.

If you encounter any issues during the conversion or upload process, please let us know, and we'll be happy to assist further. 😊

[sergiuwaxmann]

@zivp100 Unfortunately, you need to use the YOLO format we support.