PenguinDetector

This penguin detector model is implemented based on the YOLOv5 architecture developed by Ultralytics and rooted in the classic real-time object detection algorithm YOLO.

Usage

git clone https://github.com/Kejia928/PenguinDetector.git

Set up environment

The following shows the Python environment requirements for this detector. This is shown in the yolov5 folder requirements.txt.

The requirement can be directly installed by using pip:

cd yolov5
pip install -r requirements.txt

# YOLOv5 requirements
# Usage: pip install -r requirements.txt

# Base ------------------------------------------------------------------------
gitpython
ipython  # interactive notebook
matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.1.1
Pillow>=7.1.2
psutil  # system resources
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
thop>=0.1.1  # FLOPs computation
torch>=1.7.0  # see https://pytorch.org/get-started/locally (recommended)
torchvision>=0.8.1
tqdm>=4.64.0
# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012

# Logging ---------------------------------------------------------------------
tensorboard>=2.4.1
# clearml>=1.2.0
# comet

# Plotting --------------------------------------------------------------------
pandas>=1.1.4
seaborn>=0.11.0

# Export ----------------------------------------------------------------------
# coremltools>=6.0  # CoreML export
# onnx>=1.12.0  # ONNX export
# onnx-simplifier>=0.4.1  # ONNX simplifier
# nvidia-pyindex  # TensorRT export
# nvidia-tensorrt  # TensorRT export
# scikit-learn<=1.1.2  # CoreML quantization
# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
# tensorflowjs>=3.9.0  # TF.js export
# openvino-dev  # OpenVINO export

# Deploy ----------------------------------------------------------------------
# tritonclient[all]~=2.24.0

# Extras ----------------------------------------------------------------------
# mss  # screenshots
# albumentations>=1.0.3
# pycocotools>=2.0.6  # COCO mAP
# roboflow
# ultralytics  # HUB https://hub.ultralytics.com

Detect

To detect on a single image, fill the 'image path' in the below command line:

python yolov5/detect.py --weights model/yolov5s_best.pt --source 'image path' --save-txt

To detect on a video:

Directly run the run.py, specifying path to video file after the --video argument:

python run.py --video video/SP_N5_20170808_2_4.mp4

To specify a custom YOLO model file for detecting on a video, include the --model argument (default best model is used below):

python run.py --video video/SP_N5_20170808_2_4.mp4 --model model/yolov5s_best.pt

Training

Clone the dataset:

git clone https://github.com/Kejia928/local-detection-dataset.git

Run the yolov5 train.py:

python yolov5/train.py --data local-detection-dataset/data.yaml --weights yolov5s.pt --cfg yolov5/models/yolov5s.yaml --save-period=200 --img 640 --epochs 1000

Test

Run the yolov5 val.py:

python yolov5/val.py --weights model/yolo/yolov5n_best.pt --data local-detection-dataset/data.yaml --img 640