PyTorch implementation of DeepLabV3
- Training: PASCAL VOC 2012 trainaug set
- Evaluation: PASCAL VOC 2012 val set
| Backbone | Output stride | mIoU | Configuration |
|---|---|---|---|
| ResNet101 | 16 | 77.8 | configs/resnet101.yaml |
| MobileNetV2 | 16 | 70.1 | configs/mobilenetV2.yaml |
- Python ≥ 3.6
- Install libraries:
pip install -r requirements.txt
cd datasets/voc/
# Download standard PASCAL VOC 2012
wget http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_11-May-2012.tar
# The original PASCAL VOC 2012 only labels 1,464 training images. Following the paper, we add additional
# annotations from Semantic Boundaries Dataset to augment the training set to 10,582 images.
wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz
tar zxf benchmark.tgz
# Generate CSV files that contain the paths to the images and annotations.
python prepare.py --voc VOCdevkit/VOC2012/ --sbd benchmark_RELEASE/We use YAML for configuration management. See configs/*.yaml for examples. You can modify the settings as needed.
python train.py --cfg <CONFIG_FILE> --logdir <LOG_DIRECTORY>
# For example, to train a model with ResNet101 backbone:
python train.py --cfg configs/resnet101.yaml --logdir runs/resnet101/exp0/To visualize training progress using TensorBoard:
tensorboard --logdir <LOG_DIRECTORY>An interrupted training can be resumed by:
# Run train.py with --resume to restore the latest saved checkpoint file in the log directory.
python train.py --cfg <CONFIG_FILE> --logdir <LOG_DIRECTORY> --resumepython eval.py --cfg <CONFIG_FILE> --pth <LOG_DIRECTORY>/best.pth --csv datasets/voc/val.csv