yjh0410 / ODLab-World

General Object Detection for Open World

Requirements

• We recommend you to use Anaconda to create a conda environment:

conda create -n odlab python=3.10

• Then, activate the environment:

conda activate odlab

• Requirements:

1. Install necessary libraies

pip install -r requirements.txt 

2. (optional) Compile MSDeformableAttention ops for DETR series

cd ./ppdet/modeling/transformers/ext_op/

python setup_ms_deformable_attn_op.py install

See details

My environment:

• PyTorch = 2.2.0
• Torchvision = 0.17.0

At least, please make sure your torch is version 1.x.

Experiments

VOC

• Download VOC.

cd <ODLab-World>
cd dataset/scripts/
sh VOC2007.sh
sh VOC2012.sh

• Check VOC

cd <ODLab-World>
python dataset/voc.py

COCO

• Download COCO.

cd <ODLab-World>
cd dataset/scripts/
sh COCO2017.sh

• Clean COCO

cd <ODLab-World>
cd tools/
python clean_coco.py --root path/to/coco --image_set val
python clean_coco.py --root path/to/coco --image_set train

• Check COCO

cd <ODLab-World>
python dataset/coco.py

Train

We kindly provide a script train.sh to run the training code. You need to follow the following format to use this script：

bash train.sh <model> <data> <data_path> <batch_size> <num_gpus> <master_port> <resume_weight>

For example, we use this script to train YOLO-N from the epoch-0:

bash train.sh yolo_n coco path/to/coco 128 4 1699 None

We can also continue training from existing weights by passing the model's weight file to the resume parameter.

bash train.sh yolo_n coco path/to/coco 128 4 1699 path/to/yolo_n.pth

Train on custom dataset

Besides the popular datasets, we can also train the model on ourself dataset. To achieve this goal, you should follow these steps:

• Step-1: Prepare the images (JPG/JPEG/PNG ...) and use labelimg to make XML format annotation files.

CustomedDataset
|_ train
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|_ val
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|  ...

• Step-2: Make the configuration for our dataset.

cd <ODLab-World>
cd config/data_config

You need to edit the dataset_cfg defined in dataset_config.py. You can refer to the customed defined in dataset_cfg to modify the relevant parameters, such as num_classes, classes_names, to adapt to our dataset.

For example:

dataset_cfg = {
    'customed':{
        'data_name': 'AnimalDataset',
        'num_classes': 9,
        'class_indexs': (0, 1, 2, 3, 4, 5, 6, 7, 8),
        'class_names': ('bird', 'butterfly', 'cat', 'cow', 'dog', 'lion', 'person', 'pig', 'tiger', ),
    },
}

• Step-3: Convert customed to COCO format.

cd <ODLab-World>
cd tools
# convert train split
python convert_ours_to_coco.py --root path/to/dataset/ --split train
# convert val split
python convert_ours_to_coco.py --root path/to/dataset/ --split val

Then, we can get a train.json file and a val.json file, as shown below.

CustomedDataset
|_ train
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|     |_ train.json
|_ val
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|     |_ val.json
|  ...

• Step-4 Check the data.

cd <ODLab-World>
cd dataset
# convert train split
python customed.py --root path/to/dataset/ --split train
# convert val split
python customed.py --root path/to/dataset/ --split val

• Step-5 Train

For example:

cd <ODLab-World>
python train.py --root path/to/dataset/ -d customed -m yolo_n -bs 16 -p path/to/yolo_n_coco.pth

• Step-6 Test

For example:

cd <ODLab-World>
python test.py --root path/to/dataset/ -d customed -m yolo_n --weight path/to/checkpoint --show

• Step-7 Eval

For example:

cd <ODLab-World>
python eval.py --root path/to/dataset/ -d customed -m yolo_n --weight path/to/checkpoint

Deployment

1. ONNX export and an ONNXRuntime