The sample training script was made to train object detection models on PASCAL VOC 2012.
Ensure that you have holocron installed
git clone https://github.com/frgfm/Holocron.git
pip install -e "Holocron/.[training]"No need to download the dataset, torchvision will handle this for you! From there, you can run your training with the following command
python train.py VOC2012 --arch unet3p -b 4 -j 16 --opt radam --lr 1e-5 --sched onecycle --epochs 20Performances are evaluated on the validation set of the dataset using the mean IoU metric.
| Size (px) | Epochs | args | mean IoU | # Runs |
|---|---|---|---|---|
| 256 | 200 | VOC2012 --arch unet_rexnet13 -b 16 --loss label_smoothing --opt adamp --device 0 --lr 2e-3 --epochs 200 | 32.14 | 1 |
| 256 | 20 | VOC2012 --arch unet3p -b 4 -j 16 --opt radam --lr 1e-5 --sched onecycle --epochs 20 | 14.17 | 1 |
| Model | mean IoU | Param # | MACs | Interpolation | Image size |
|---|---|---|---|---|---|
| unet | 18.11M | bilinear | 256 | ||
| unetp | 28.28M | bilinear | 256 | ||
| unetpp | 29.54M | bilinear | 256 | ||
| unet3p | 26.93M | bilinear | 256 | ||
| unet_tvvgg11 | 32.17M | bilinear | 256 | ||
| unet_tvresnet34 | 36.25M | bilinear | 256 | ||
| unet_rexnet13 | 32.14 | 9.34M | bilinear | 256 |