RT-DETR数据集格式

### RT-DETR Model Dataset Format Requirements For the RT-DETR (Real-Time Detection Transformer) model, specific dataset formats are required to ensure optimal performance during training and inference operations. Typically, datasets used with such models need to be structured in a way that supports object detection tasks effectively. The COCO format is widely accepted as it provides comprehensive annotations including bounding boxes, segmentation masks, keypoints, etc.[^1]. This format includes JSON files where each image has corresponding annotation information stored alongside metadata about categories and licenses. #### Conversion Methods from Other Formats to COCO To convert existing datasets into this compatible structure: When converting from other common formats like VOC or YOLO, tools provided by libraries such as PyTorch's torchvision can facilitate these transformations efficiently without manual intervention on large-scale projects. ```python from pycocotools.coco import COCO import json def voc_to_coco(voc_path, output_json_file): coco_output = { "images": [], "annotations": [], "categories": [] } # Add code here for processing VOC data with open(output_json_file, 'w') as f: json.dump(coco_output, f) voc_to_coco('path/to/voc', 'output.json') ``` In cases involving custom proprietary systems requiring migration of their unique structures towards standardization through adaptation processes similar to those mentioned above may also apply depending upon specifics involved within individual scenarios.