All things related to vision 👀

• Vision (All modules combined)
• Detection (module) 🔍
  • Quick Start 🚀
• Depth (module) 🤽‍♂️
  • Quick Start 🚀

TODO: Combine detection and depth

YOLOv5 with custom training to detect trash

See train_trash_detection.ipynb for training

Datasets used for training:

• TACO

Clone YOLOv5 inside ml-ady-vision

git clone https://github.com/ultralytics/yolov5

Go into the yolov5 folder

cd yolov5

Install requirements

pip install -r requirements.txt

Run inference

python detect.py --source "../input/trash.jpg" --weights "../detection_weights.pt" --img-size 640 --conf 0.675 --exist-ok --project ../ --name output

Export ONNX-model

python models/export.py --weights "../detection_weights.pt" --img 640 --batch 1

AdaBins with pretrained models to estimate depth map

Datasets used for training:

• KITTI outdoors
• NYU indoors

Install CUDA (tested on 10.2)

Install cuDNN (tested on 8.0.5 for CUDA 10.2)

Install Anaconda

Create virtual environment (tested on python 3.6.6)

conda create -n vision python=3.6.6 anaconda

Install PyTorch (tested on 1.7.0 and 1.7.1)

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

See get started if not following previous recommendations, as you may want another version of PyTorch.

Install Taqaddum

conda install -c conda-forge tqdm

Run the setup

setup_depth.sh

Download pretrained weights

Place pretrained weights in pretrained/

Test depth inference

python depth.py