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 | Dataset | Weights | P | R | [email protected] | [email protected]:.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 |
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Using PIP
pip install -r requirements.txt
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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.
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| 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.