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YOLTv4

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YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

This repository is built upon the impressive work of AlexeyAB's YOLOv4 implementation, which improves both speed and detection performance compared to YOLOv3 (which is implemented in SIMRDWN). We use YOLOv4 insead of "YOLOv5", since YOLOv4 is endorsed by the original creators of YOLO, whereas "YOLOv5" is not; furthermore YOLOv4 appears to have superior performance.

Below, we provide examples of how to use this repository with the open-source Rareplanes dataset.

Running YOLTv4

0. Installation

YOLTv4 is built to execute within a docker container on a GPU-enabled machine. The docker command creates an Ubuntu 16.04 image with CUDA 9.2, python 3.6, and conda.

  1. Clone this repository (e.g. to /yoltv4/).

  2. Download model weights to yoltv4/darknet/weights). See: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137 https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142

  3. Install nvidia-docker.

  4. Build docker file.

     nvidia-docker build -t yoltv4_image /yoltv4/docker

  5. Spin up the docker container (see the docker docs for options).

     NV_GPU=0 nvidia-docker run -it -v /local_data:/local_data -v /yoltv4:/yoltv4 -ti --ipc=host --name yoltv4_gpu0 yoltv4_image

  6. Compile the Darknet C program.

    First Set GPU=1 CUDNN=1, CUDNN_HALF=1, OPENCV=1 in /yoltv4/darknet/Makefile, then make:

     cd /yoltv4/darknet
 make

1. Train

A. Prepare Data

  1. Make YOLO images and labels (see yoltv4/notebooks/train_test_pipeline.ipynb for further details).

  2. Create a txt file listing the training images.

  3. Create file obj.names file with each desired object name on its own line.

  4. Create file obj.data in the directory yoltv4/darknet/data containing necessary files. For example:

    /yoltv4/darknet/data/rareplanes_train.data

     classes = 30
 train =  /local_data/cosmiq/wdata/rareplanes/train/txt/train.txt
 valid =  /local_data/cosmiq/wdata/rareplanes/train/txt/valid.txt
 names =  /yoltv4/darknet/data/rareplanes.name
 backup = backup/

  5. Prepare config files.

    See instructions here, or tweak /yoltv4/darknet/cfg/yoltv4_rareplanes.cfg.

B. Execute Training

  1. Execute.

     cd /yoltv4/darknet
 time ./darknet detector train data/rareplanes_train.data  cfg/yoltv4_rareplanes.cfg weights/yolov4.conv.137  -dont_show -mjpeg_port 8090 -map

  2. Review progress (plotted at: /yoltv4/darknet/chart_yoltv4_rareplanes.png).

2. Test

The easiest method is to edit yoltv4/configs/yoltv4_test.yaml to include the correct variables, and then simply run:

./test.sh configs/yoltv4_test.yaml

Alternately, on can run testing in parts, as detailed below:

A. Prepare Data

  1. Make sliced images (see yoltv4/notebooks/train_test_pipeline.ipynb for further details).

  2. Create a txt file listing the training images.

  3. Create file obj.data in the directory yoltv4/darknet/data containing necessary files. For example:

    /yoltv4/darknet/data/rareplanes_test.data classes = 30 train = valid = /local_data/cosmiq/wdata/rareplanes/test/txt/test.txt names = /yoltv4/darknet/data/rareplanes.name backup = backup/

B. Execute Testing

  1. Execute (proceeds at >80 frames per second on a Tesla P100):

     cd /yoltv4/darknet
 time ./darknet detector valid data/rareplanes_test.data cfg/yoltv4_rareplanes.cfg backup/ yoltv4_rareplanes_best.weights

  2. Post-process detections:

    A. Move detections into results directory

     mkdir /yoltv4/darknet/results/rareplanes_preds_v0
 mkdir  /yoltv4/darknet/results/rareplanes_preds_v0/orig_txt
 mv /yoltv4/darknet/results/comp4_det_test_*  /yoltv4/darknet/results/rareplanes_preds_v0/orig_txt/

    B. Stitch detections back together and make plots

     time python /yoltv4/yoltv4/post_process.py \
     --pred_dir=/yoltv4/darknet/results/rareplanes_preds_v0/orig_txt/ \
     --raw_im_dir=/local_data/cosmiq/wdata/rareplanes/test/images/ \
     --sliced_im_dir=/local_data/cosmiq/wdata/rareplanes/test/yoltv4/images_slice/ \
     --out_dir= /yoltv4/darknet/results/rareplanes_preds_v0 \
     --detection_thresh=0.25 \
     --slice_size=416 \
     --n_plots=8

Outputs will look something like the figures below:

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