Customized implementation of the U-Net in PyTorch for Roboflow's Tooth Detection from high definition images.
nohup python train.py --epochs 10 --batch-size 16 --scale 0.6 --classes 10 --trained-model-file trained-models/unet-model-scale0.6-batchsize16.pth &
nohup python train.py --epochs 10 --batch-size 16 --scale 0.6 --classes 10 --trained-model-file trained-models/unet-model-scale0.6-batchsize16-585samples.pth &
python predict.py -i ./test-images-from-internet/test2.jpg -o ./test-images-from-internet/test2-output.jpg --model trained-models/unet-model-scale0.6-batchsize16.pth --bilinear --scale 0.6
python predict.py -i ./test-images-from-internet/test1.jpg -o ./test-images-from-internet/test1-output.jpg --model trained-models/unet-model-scale0.6-batchsize16.pth --bilinear --scale 0.6
python predict.py -i ./test-images-from-internet/test3.jpg -o ./test-images-from-internet/test3-output.jpg --model trained-models/unet-model-scale0.6-batchsize16.pth --bilinear --scale 0.6
python predict.py -i ./test-images-from-internet/test3.jpg -o ./test-images-from-internet/test3-output-585samples.jpg --model trained-models/unet-model-scale0.6-batchsize16-585samples.pth --bilinear --scale 0.6
python predict.py -i ./test-images-from-internet/test2.jpg -o ./test-images-from-internet/test2-output-585samples.jpg --model trained-models/unet-model-scale0.6-batchsize16-585samples.pth --bilinear --scale 0.6
python predict.py -i ./test-images-from-internet/test1.jpg -o ./test-images-from-internet/test1-output-585samples.jpg --model trained-models/unet-model-scale0.6-batchsize16-585samples.pth --bilinear --scale 0.6
The input images and target masks should be in the data/imgs
and data/masks
folders respectively (note that the imgs
and masks
folder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white.
You can use your own dataset as long as you make sure it is loaded properly in utils/data_loading.py
.
Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox:
U-Net: Convolutional Networks for Biomedical Image Segmentation