1. Download COCO2017 from OpenDataLab or COCO.
2. Modify paths in coco_detection.yml

train_dataloader:
    img_folder: /data/COCO2017/train2017/
    ann_file: /data/COCO2017/annotations/instances_train2017.json
val_dataloader:
    img_folder: /data/COCO2017/val2017/
    ann_file: /data/COCO2017/annotations/instances_val2017.json

To train on your custom dataset, you need to organize it in the COCO format. Follow the steps below to prepare your dataset:

1. Set remap_mscoco_category to False:

   This prevents the automatic remapping of category IDs to match the MSCOCO categories.

   remap_mscoco_category: False

2. Organize Images:

   Structure your dataset directories as follows:

   dataset/
   ├── images/
   │   ├── train/
   │   │   ├── image1.jpg
   │   │   ├── image2.jpg
   │   │   └── ...
   │   ├── val/
   │   │   ├── image1.jpg
   │   │   ├── image2.jpg
   │   │   └── ...
   └── annotations/
       ├── instances_train.json
       ├── instances_val.json
       └── ...

   • images/train/: Contains all training images.
   • images/val/: Contains all validation images.
   • annotations/: Contains COCO-formatted annotation files.

3. Convert Annotations to COCO Format:

   If your annotations are not already in COCO format, you'll need to convert them. You can use the following Python script as a reference or utilize existing tools:

   import json
   
   def convert_to_coco(input_annotations, output_annotations):
       # Implement conversion logic here
       pass
   
   if __name__ == "__main__":
       convert_to_coco('path/to/your_annotations.json', 'dataset/annotations/instances_train.json')

4. Update Configuration Files:

   Modify your custom_detection.yml.

   task: detection
   
   evaluator:
     type: CocoEvaluator
     iou_types: ['bbox', ]
   
   num_classes: 777 # your dataset classes
   remap_mscoco_category: False
   
   train_dataloader:
     type: DataLoader
     dataset:
       type: CocoDetection
       img_folder: /data/yourdataset/train
       ann_file: /data/yourdataset/train/train.json
       return_masks: False
       transforms:
         type: Compose
         ops: ~
     shuffle: True
     num_workers: 4
     drop_last: True
     collate_fn:
       type: BatchImageCollateFunction
   
   val_dataloader:
     type: DataLoader
     dataset:
       type: CocoDetection
       img_folder: /data/yourdataset/val
       ann_file: /data/yourdataset/val/ann.json
       return_masks: False
       transforms:
         type: Compose
         ops: ~
     shuffle: False
     num_workers: 4
     drop_last: False
     collate_fn:
       type: BatchImageCollateFunction

1. Training

   CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml --use-amp --seed=0

2. Testing

   CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml --test-only -r model.pth

3. Tuning

   CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --master_port=7777 --nproc_per_node=4 train.py -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml --use-amp --seed=0 -t model.pth

For example, if you want to double the total batch size when training RT-DETRv4-L on COCO2017, here are the steps you should follow:

1. Modify your dataloader.yml to increase the total_batch_size:

   train_dataloader:
       total_batch_size: 64  # Previously it was 32, now doubled

2. Modify your rtv4_hgnetv2_l_coco.yml. Here’s how the key parameters should be adjusted:

   optimizer:
     type: AdamW
     params:
       -
         params: '^(?=.*backbone)(?!.*norm|bn).*$'
         lr: 0.000025  # doubled, linear scaling law
       -
         params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn)).*$'
         weight_decay: 0.
   
   lr: 0.0005  # doubled, linear scaling law
   betas: [0.9, 0.999]
   weight_decay: 0.0001  # need a grid search
   
   ema:  # added EMA settings
       decay: 0.9998  # adjusted by 1 - (1 - decay) * 2
       warmups: 500  # halved
   
   lr_warmup_scheduler:
       warmup_duration: 250  # halved

If you'd like to train RT-DETRv4 on COCO2017 with an input size of 320x320, follow these steps:

1. Modify your dataloader.yml:

   train_dataloader:
     dataset:
         transforms:
             ops:
                 - {type: Resize, size: [320, 320], }
     collate_fn:
         base_size: 320
   
   val_dataloader:
     dataset:
         transforms:
             ops:
                 - {type: Resize, size: [320, 320], }

2. Modify your rtv4_base.yml (or the relevant base config file):

   eval_spatial_size: [320, 320]

1. Setup

   pip install onnx onnxsim

2. Export onnx

   python tools/deployment/export_onnx.py --check -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml -r model.pth

3. Export tensorrt

   trtexec --onnx="model.onnx" --saveEngine="model.engine" --fp16

1. Setup

   pip install -r tools/inference/requirements.txt

2. Inference (onnxruntime / tensorrt / torch)

   Inference on images and videos is now supported.

   python tools/inference/onnx_inf.py --onnx model.onnx --input image.jpg  # or video.mp4
   python tools/inference/trt_inf.py --trt model.engine --input image.jpg
   python tools/inference/torch_inf.py -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml -r model.pth --input image.jpg --device cuda:0

1. Setup

   pip install -r tools/benchmark/requirements.txt

2. Model FLOPs, MACs, and Params

   python tools/benchmark/get_info.py -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml

3. TensorRT Latency

   python tools/benchmark/trt_benchmark.py --COCO_dir path/to/COCO2017 --engine_dir model.engine

1. Setup

   pip install fiftyone

2. Voxel51 Fiftyone Visualization (fiftyone)

   python tools/visualization/fiftyone_vis.py -c configs/rtv4/rtv4_hgnetv2_${model}_coco.yml -r model.pth

1. Auto Resume Training

   bash tools/reference/safe_training.sh

2. Converting Model Weights

   python tools/reference/convert_weight.py model.pth