ACSTNet: An improved YOLO X method for small object detection with pixel-level attention and parallel Swin Transformer

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Catalog

1. Environment

2. Achievement

3. Download file

4. How2train

5. How2predict

6. How2eval

1.Environment

torch version: 1.11.0+cu113
cuda version: 11.3
cudnn version: 8200
torchversion -V: 0.12.0+cu113

Python environment import:

pip install -r requirements.txt

2.Achievement

• Backbone feature extraction network: CSPNet with Swin Transform network structure is used.
• Neck layer: increase (ECSNFAM multi-attention structure.
• Categorical regression layer: Decoupled Head, in YoloX, Yolo Head is divided into two parts of categorical regression, and only integrated together in the final prediction.
• Tips used for training: Mosaic data enhancement, IOU and GIOU, learning rate cosine annealing decay.
• Anchor Free
• SimOTA

3.Weights Download

The weights required for training can be downloaded from Google Drive.
Link: https://drive.google.com/drive/folders/1cF7GUiqjay0WJ-lElzG-MrHZHpY3kuQu?usp=sharing

4.Train

Training RSOD dataset.

1. Preparation of the data set First download the RSOD dataset, then use the VOC format for training and place the file under the path: ACSTNet/dataset/RSOD-Dataset

2. Modify the parameters needed for training

3. Start training

python train.py

5.Predict

Training result prediction

We need to use two files to predict the training results, yolo.py and predict.py. We first need to go to yolo.py and modify model_path and classes_path, these two parameters must be modified. model_path points to the trained weights file, which is in the logs folder.

Once you have completed the changes you can run predict.py for testing.

python predict.py

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6.Eval

Evaluation of RSOD test sets

1. This paper uses the VOC format for evaluation. RSOD has divided the test set and its path is at:dataset/RSOD-Dataset/test_xywh.txt.
2. Modify model_path as well as classes_path inside yolo.py. model_path points to the trained weights file, in the logs folder. classes_path points to the txt corresponding to the detected category.
3. The evaluation results can be obtained by running get_map.py, and the evaluation results will be saved in the map_out folder.

python get_map.py

Reference

https://github.com/Megvii-BaseDetection/YOLOX