YOLOv7 for Oriented Object Detection

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

The code for the implementation of Yolov5_obb, Yolov7.

Getting Started

This repo is based on yolov7, yolov5_obb.

Requirements environment:

• Nvidia dGPU (Volta, Turing, Ampere, Ada Lovelace)
• Linux OS with driver 525+
• Docker with Compose plugin installed and configured with Nvidia Container Runtime
• git + lfs.

Prerequisites:

Clone the repository with all the necessary code.

git clone https://github.com/insight-platform/Yolo_V7_OBB_Pruning.git

Move to project folder

cd Yolo_V7_OBB_Pruning

Build docker image

make docker-image-build-notebook

Download all files and models to reproduce results

make download-data

A docker compose file has been prepared to make it easy to start the container. You can include additional volumes if you need. The container automatically runs JupyterLab (http://127.0.0.1:10000) and TensorBoard (http://127.0.0.1:6006) to track learning metrics.

Start container

docker-compose up -d

Stop container

docker-compose down

Download all pretrained models and experiments files

make download-data 

Preparing dataset

Open JupyterLab (http://127.0.0.1:10000) and run prepare_datasets.ipynb

Training

Connect to docker container

docker exec -it yolo_v7_obb_pruning_yolov7obb_1 /bin/bash

Single GPU training

# train yolov7 models
python /opt/app/yolov7obb/train_obb.py \
	--workers 8 \
	--device 0 \
	--epochs 600 \
	--global-batch-size 64 \
	--gpu-batch-size 8 \
	--single-cls \
	--plots_debug \
	--data /opt/app/experiments/fisheye_person_v1.0.0/data.yaml \
	--img 640 640 \
	--cfg /opt/app/experiments/fisheye_person_v1.0.0/yolov7.yaml \
	--weights /opt/app/weights/yolov7.pt \
	--name $SCRIPT_DIR_NAME \
	--hyp /opt/app/experiments/fisheye_person_v1.0.0/hyp.yaml \
	--project /opt/app/runs/train \
	--sparsity \
	--noautoanchor

Pruning model

python ./yolov7obb/pruning.py \
    --weights ./runs/train/fisheye_person_v1.0.0/weights/best_145.pt \
    --weights-ref ./runs/train/fisheye_person_v1.0.0/weights/init.pt \
    -o 1.2 \
    -r 32

Exporting model to onnx

python ./yolov7obb/export.py  \
   --weights /opt/app/runs/train/fisheye_person_v1.0.0/weights/best_145.pt  \
   --img-size 640 640 \
   --batch-size 1 \
   --onnx \
   --grid \
   --end2end \
   --simplify \
   --fp16

Inference and video generation

You can use notebook predict_video.ipynb to predict on video and generate video with bounding boxes.