Implementation of the Semantic Segmentation DeepLab_V3 CNN as described in: https://arxiv.org/pdf/1606.00915.pdf
For a complete documentation of this implementation, check out the blog post.
- Python 3.x
- Numpy
To train this model run:
python train.py --starting_learning_rate=0.00001 --batch_norm_decay=0.997 --gpu_id=0 --resnet_model=resnet_v2_50
Check out the train.py file for more input argument options. Each run produces a folder inside the "tboard_logs" directory (create it if not there).
To evaluate the model, run the test.py file passing to it the model_id parameter (the name of the folder created during training).
python test.py --model_id=16645
- Pixel accuracy: ~91%
- Mean Accuracy: ~82%
- Mean Intersection over Union (mIoU): ~74%
- Frequency weighed Intersection over Union: ~86.
