Waste_Detection_New

• 1. Repos being used
• 2. Installation
  • 2.1. Windows Only (Extra Step)
  • 2.2. All platforms
    • 2.2.1. detectron2
      • 2.2.1.1. Windows extra install
    • 2.2.2. YOLOv5
    • 2.2.3. DETR
• 3. Training
  • 3.1. YOLOv5
  • 3.2. Faster-RCNN and DETR
• 4. Contribute
  • 4.0. Current Datasets
  • 4.1. Classes (case sensitive)
  • 4.2. Submission Format
  • 4.3. Notes
  • 4.4. Example Annotation
• 5. Milestones
  • 5.1. Data Statistics
  • 5.2. Metrics and Graphs

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1. Repos being used

detectron2 https://github.com/facebookresearch/detectron2
YOLOv5 https://github.com/ultralytics/yolov5
DETR https://github.com/facebookresearch/detr

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2. Installation

2.1. Windows Only (Extra Step)

Install Microsoft C++ Build Tools https://visualstudio.microsoft.com/visual-cpp-build-tools/
Install Instructions https://stackoverflow.com/a/64262038
Don't forget to restart your system

2.2. All platforms

Install Miniconda from https://docs.conda.io/en/latest/miniconda.html
Please create a conda environment to make installation easier

conda create --name waste python=3.9 -y  
conda activate waste  

Always make sure pip is up to date

pip install --upgrade pip  

Depending on the version of CUDA that is required the pytorch installation will vary. Change cudatoolkit=11.3 to 10.2 if CUDA 11.3 is not supported on your GPU

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -y  
pip install opencv-python  

2.2.1. detectron2

python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'  

2.2.1.1. Windows extra install

conda install pywin32 -y

2.2.2. YOLOv5

cd YOLOV5  
pip install -r requirements.txt  
pip install wandb  
cd ..  

2.2.3. DETR

conda install cython scipy -y  

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3. Training

3.1. YOLOv5

cd YOLOV5  
python train.py --img 640 --batch 16 --data trash_9.yaml --weights yolov5m.pt --hyp hyp.scratch-low_trash.yaml  

OR

python train.py --img <dimensions> --batch <batch_size recc. 64> --data <yaml for dataset> --weights yolov5m.pt --hyp <customised hyperparameters>

Finally, test the model

python val.py --img 640 --batch 32 --data trash_9.yaml --weights ./runs/train/<exp#>/weights/best.pt --task test --device 0

3.2. Faster-RCNN and DETR

cd FRCNN_DETR
# Linux
./frcnn_1_gpu.sh (assuming the current dataset is being used)
# All
python train.py --type ('frcnn' or 'detr') --num-gpus=1 SOLVER.IMS_PER_BATCH <batch_size> SOLVER.BASE_LR <learning rate>

OR FOR MULTI-GPU

# Linux
./frcnn_4_gpu.sh
./detr_4_gpu.sh
# ALL
python train.py --type ('frcnn' or 'detr') --num-gpus=4 SOLVER.IMS_PER_BATCH <total_batch_size> SOLVER.BASE_LR <learning rate>

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4. Contribute

4.0. Current Datasets

TrashNet (annotated newly with bounding boxes): https://github.com/garythung/trashnet
TACO (annotated newly once again): http://tacodataset.org/
UAVVaste (mapped to correct classes): https://uavvaste.github.io/

4.1. Classes (case sensitive)

There are 9 classes.

• aluminium wrap
• cardboard
• cigarette
• general waste
• glass
• metal
• paper
• plastic
• styrofoam

4.2. Submission Format

Please submit annotations in the COCO Format
The annotation type is "1. Object Detection" on the same page.

4.3. Notes

If an object is made of thin cardboard (between paper and cardboard), opt for 'paper'.
If you can't tell whether an object is a certain material, opt for 'general waste'.
When annotating an image, be sure to make the bounding box as small as possible.

4.4. Example Annotation

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5. Milestones

5.1. Data Statistics

The current best performance was achieved by using a combination of TACO, TrashNet and UAVVaste.
The dataset split used was:

• Total images: 6460
• Train: ~4500 images (70%)
• Validation: 654 images (10%)
• Test: ~1300 images (20%)

5.2. Metrics and Graphs

• mAP: 51.3%
• Precision: 62.4%
• Recall: 49.1%