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Patchmentation YOLOv5 🚀

This repository is forked from ultralytics/yolov5 and modified. For more information about the models, please visit the original repository.

This model YOLOv5 🚀 is used to benchmark patch augmentation performance of patchmentation.

Experiment Results and Comparison

Experiment Dataset Weights P R mAP@.5 mAP@.5:.95
S-base Pascal VOC 2007 yolov5s 0.685 0.558 0.586 0.327
S1 Pascal VOC 2007
with patch augmentation		 yolov5s 0.715 0.621 0.671 0.405
S2 Pascal VOC 2007
with patch augmentation and soft-edge		 yolov5s 0.717 0.624 0.67 0.403
S3 Pascal VOC 2007
with patch augmentation and negative-patch		 yolov5s 0.726 0.607 0.665 0.393
S4 Pascal VOC 2007
with patch augmentation, soft-edge, and negative-patch		 yolov5s 0.732 0.608 0.669 0.396
X-base Pascal VOC 2007 yolov5x 0.81 0.688 0.745 0.516
X1 Pascal VOC 2007
with patch augmentation		 yolov5x 0.817 0.719 0.776 0.556
X2 Pascal VOC 2007
with patch augmentation and soft-edge		 yolov5x 0.804 0.704 0.772 0.544
X3 Pascal VOC 2007
with patch augmentation and negative-patch		 yolov5x 0.796 0.718 0.776 0.549
X4 Pascal VOC 2007
with patch augmentation, soft-edge, and negative-patch		 yolov5x 0.818 0.704 0.767 0.543
PS-base Penn-Fudan-Ped yolov5s 0.358 0.234 0.288 0.099
PS1 Single image from Campus - Garden1
with patch augmentation from Penn-Fudan-Ped		 yolov5s 0.907 0.746 0.817 0.401
PX-base Penn-Fudan-Ped yolov5x 0.566 0.315 0.393 0.145
PX1 Single image from Campus - Garden1
with patch augmentation from Penn-Fudan-Ped		 yolov5x 0.9 0.79 0.838 0.431

Dependency

  • Using PIP

    pip install -r requirements.txt

  • Using Docker (recommended)

    docker pull jstnxu/patchmentation:yolov5
docker run -it --ipc=host --gpus all \
  -v {data_folder}:/patchmentation-dataset/data \
  -v {project_folder}:/workspace/runs/patchmentation \
  jstnxu/patchmentation:yolov5 /bin/bash

    • change {data_folder} to local path to load dataset.

    • change {project_folder} to local path to save outputs.

Arguments

Priority* Arguments Type Description
- --version one or more str Training version(s).
- --overwrite store_true Overwrite existing output / zip.
- --batch-size int Number of batch size. Required if train is true or test is true.
- --epochs int Number of epoch. Required if train is true.
- --data one or more str Dataset yaml configurations. If not given, will use predefined yaml in accordance with the version.
- --weights str Model weight. Default yolov5s.pt.
1 --train store_true Train the model. If overwrite is true, it will remove the output (if exists) before training.
2 --test store_true Test the model. If overwrite is true, it will remove the test output (if exists) before testing.
3 --zip store_true Zip the output. If overwrite is true, it will remove the output zip (if exists) before zipping.
4 --upload store_true Upload the output zip.
5 --remove-zip store_true Remove the output zip, if exists.
6 --download one or more url Download the output zip. If overwrite is true, it will remove the output zip (if exists) before downloading.
7 --unzip store_true Unzip the output zip. If overwrite is true, it will remove the output (if exists) before unzipping.
8 --plot store_true Generate more plot. If train is true, this method will also be called.
9 --remove store_true Remove the output, if exists.

*Smaller priority number will be executed first

This project was developed as part of thesis project, Computer Science, BINUS University.