This repository contains the code for my Master thesis. I will cover different tasks in computer vision field, like object detection, semantic segmentation, instance segmentation and panoptic segmentation. I will be using different SOTA models.
The main goal of this work is to compare several object detection models in terms of speed and accuracy. In addition, I ran Mask RCNN to perform instance segmentation on BDD100K dataset.
Dataset used is BDD100K which is available here. In addition, the data is available on my google drive.
├─src
│ ├─config
│ │ ├─defaults.py # default configuration
│ ├─dataset
│ │ ├─bdd.py # Superclass dataset，Parent class
│ │ ├─bdd_detetcion.py # Subclass for detection task
│ │ ├─bdd_drivable_segmentation.py # Subclass for drivabel area segmetation task
│ │ ├─bdd_instance_segmentation.py # Subclass for instance segmetation task
│ │ ├─bdd_instance_segmentation_drivable.py # Subclass for instance segmentation with drivable area task
│ │ ├─bdd_utils.py # file contains useful method
│ ├─dbs
│ │ ├─test_db.json # pre-created test db
│ │ ├─train_db.json # pre-created train db
│ │ ├─val_db.json # pre-created val db
│ ├─models
│ │ ├─Detection
│ │ │ ├─detection_models.py # file contains the method to return the Faster RCNN model
│ │ │ ├─Faster_RCNN.py # Faster RCNN class
│ │ ├─Segmentation
│ │ │ ├─FCN.py # FCN class
│ │ │ ├─DeepLab.py # DeepLabv3+ class
│ │ │ ├─MaskRCNN.py # MaskRCNN class
│ │ │ ├─segmenation_models.py # file contains the method to return the models for segmentatation
│ ├─utils
│ │ ├─DataLoaders.py # dataloader function
| | ├─utils.py # useful function
| | ├─Data Augmentation.ipynb # Notebook contains tutorial for data augmentation
│ ├─BDD Detection.ipynb # Notebook contains tutorial how to use detection class
│ ├─BDD Drivable Area.ipynb # Notebook contains tutorial how to use drivable class
│ ├─BDD Instance Segmentation.ipynb # Notebook contains tutorial how to use instance segmentation class
│ ├─YOLO Format.ipynb # Notebook contains tutorial how convert xyxy format to yolo format
├─doc
│ ├─images # some images
├─scirpts
│ ├─data.py # download the data from google drive
│ ├─weights.py # download the weights from google drive
├─notebooks
│ ├─Faster RCNN Notebook.ipynb # Faster RCNN notebook
│ ├─FCN Notebook.ipynb # FCN notebook
├─api
│ ├─app.py # Flask App
├─yolov5 # yolov5 repository
├─yolov6 # yolov6 repository
├─yolov7 # yolov7 repository
├─dataset
│ ├─bdd100k
│ │ ├─images
│ │ │ ├─10K
│ │ │ ├─100K
│ │ ├─labels
│ │ │ ├─det_20
│ │ │ ├─drivable
│ │ │ ├─lane
│ │ │ ├─pan_seg
│ │ │ ├─ins_seg
│ │ │ ├─pan_seg
├─train.py # file contains the functions to train Faster RCNN and MASk RCNN
├─test.py # file contains the functions to evaluate the models
├─detect.py # file contains the functions to run inference
├─prepare.py # file used to prepare the data to YOLO algorithms
├─util.py # contains useful functions to train, test and detectPlease install requirements using:
pip install -R requirements.txtif you want to install the data manually you can download them from my google drive. Or you can run:
pip install gdown
python scripts/data.pymake sure you put the data in the right place. see the project structure section.
Weights can be dowloaded as well from my google drive. Or you can run:
python scripts/weights.pyNow let us clone some repositories (recommendation: Create new virtual environment when working with each one):
git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt
pip install "opencv-python-headless<4.3"git clone https://github.com/WongKinYiu/yolov7.git
cd yolov7
pip install -r requirements.txtgit clone https://github.com/meituan/YOLOv6.git
cd YOLOv6
pip install -r requirements.txtTo train Faster RCNN model run the following command:
python train.py --model fasterrcnn --data './data/fasterrcnn.yaml' --batch-size 1 --img-size 640 --total_epochs 20 --logger_path 'logs' --checkpoint_path 'checkpoints' --project 'Faster RCNN' --name 'version0' --num_workers 1 Where model to specify that we want Faster RCNN model, batch-size for batch size, img-size for resizing the images and finally --total_epochs for the epochs.
To train using Yolov5:
1- Create folder for the dataset inside yolov5 folder:
cd yolov5
mkdir dataset2- Create dataset.yaml file and place it inside yolov5/data (you can copy the one in data folder).
3- Prepare the data to be aligned with the YOLO format:
cd ..
python prepare.py --yolo_version yolov5 --dataset_path 'yolov5/dataset' --data './data/yolo.yaml'4- train the model:
cd yolov5
python train.py --img 640 --batch 32 --epochs 50 --data './data/dataset.yaml' --weights yolov5s.pt --optimizer Adam --name yolo5s_bddIn place of yolov5s.pt you can select bigger model like yolov5l.pt or yolov5x.pt.
For more information follow this Tutorials.
Almost the same as Yolov5
1- Create folder for the dataset inside yolov7 folder:
cd yolov7
mkdir dataset2- Create dataset.yaml file and place it inside yolov7/data (you can copy the one in data folder).
3- Prepare the data to be aligned with the YOLO format:
cd ..
python prepare.py --yolo_version yolov7 --dataset_path 'yolov7/dataset' --data './data/yolo.yaml'4- train the model:
cd yolov7
python train.py --device 0 --batch-size 32 --data data/dataset.yaml --img 640 640 --epochs 100 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --adam --name yolov7_bdd --hyp data/hyp.scratch.custom.yaml
For more information follow this Tutorials.
1- Create folder for the dataset inside yolov6 folder:
cd YOLOv6
mkdir dataset2- Create dataset.yaml file and place it inside yolov6/data (you can copy the one in data folder).
3- Prepare the data to be aligned with the YOLO format:
cd ..
python prepare.py --yolo_version yolov5 --dataset_path 'YOLOv6/dataset' --data './data/yolo.yaml'4- train the model:
cd YOLOv6
python tools/train.py --device 0 --batch-size 32 --data-path data/dataset.yaml --img 640 640 --epochs 100
To evaluate Faster RCNN model using mean average precision run the following command:
python test.py --model fasterrcnn --data './data/fasterrcnn.yaml' --weights 'path/to/fastercnn/weights' --save-path './fasterrcnn_map.csv'To evaluate Yolov5 and Yolov7 model using mean average precision run the following commands:
First for yolov5:
cd yolov5
python val.py --data 'data/dataset.yaml' --weights 'path/to/weights' --task test --save-txt --save-conf --project 'path/to/save/pred'For yolov7:
cd yolov7
python test.py --data 'data/dataset.yaml' --weights 'path/to/weights' --task test --save-txt --save-conf --project 'path/to/save/pred'After that run this command:
cd ..
python yolo_eval.py --pred 'path/to/labels/folder' --gt 'path/to/ground/truth'Where pred is the path where the yolo models will save the prediction for each image as .txt files. gt is the path for the ground truth generated from the prepare.py files.
python detect.py --model 'fasterrcnn' --data './data/fasterrcnn.yaml' --weights 'path/to/weights' --source 'path/to/image' --confidence-score 0.5 --save-path 'predicted_image.jpg'python detect.py --model 'maskrcnn' --data './data/maskrcnn.yaml' --weights 'path/to/weights' --source 'path/to/image' --confidence-score 0.5 --save-path 'predicted_image.jpg'python detect.py --model 'yolov5' --weights 'path/to/weights' --source 'path/to/image' --confidence-score 0.5 --save-path 'yolov5_inference.jpg'python detect.py --model 'yolov7' --weights 'path/to/weights' --source 'path/to/image' --confidence-score 0.5 --save-path 'yolov7_inference.jpg'To see result run:
python results.pycheckout my YouTube video here.
some results from yolov7x model:
Weights can be downloaded from this link.
cd api
flask run