ChenJiafei0722 / ELANet

ELANet: An efficiently lightweight asymmetrical network for real-time semantic segmentation

ELANet: An efficiently lightweight asymmetrical network for real-time semantic segmentation

Dataset

You need to download the two dataset——CamVid and Cityscapes, and put the files in the dataset folder with following structure.

├── camvid
|    ├── train
|    ├── test
|    ├── val 
|    ├── trainannot
|    ├── testannot
|    ├── valannot
|    ├── camvid_trainval_list.txt
|    ├── camvid_train_list.txt
|    ├── camvid_test_list.txt
|    └── camvid_val_list.txt
├── cityscapes
|    ├── gtCoarse
|    ├── gtFine
|    ├── leftImg8bit
|    ├── cityscapes_trainval_list.txt
|    ├── cityscapes_train_list.txt
|    ├── cityscapes_test_list.txt
|    └── cityscapes_val_list.txt           

Training

• You can run: python train.py -h to check the detail of optional arguments. Basically, in the train.py, you can set the dataset, train type, epochs and batch size, etc.

python train.py --dataset ${camvid, cityscapes} --train_type ${train, trainval} --max_epochs ${EPOCHS} --batch_size ${BATCH_SIZE} --lr ${LR} --resume ${CHECKPOINT_FILE}

• training on Cityscapes train set

python train.py --dataset cityscapes

• training on CamVid train and val set

python train.py --dataset camvid --train_type trainval --max_epochs 1000 --lr 1e-3 --batch_size 16

• During training course, every 50 epochs, we will record the mean IoU of train set, validation set and training loss to draw a plot, so you can check whether the training process is normal.

Testing

• After training, the checkpoint will be saved at checkpoint folder, you can use test.py to get the result.

python test.py --dataset ${camvid, cityscapes} --checkpoint ${CHECKPOINT_FILE}

Evaluation

• For those dataset that do not provide label on the test set (e.g. Cityscapes), you can use predict.py to save all the output images, then submit to official webpage for evaluation.

python predict.py --checkpoint ${CHECKPOINT_FILE}

Inference Speed

• You can run the eval_fps.py to test the model inference speed, input the image size such as 512,1024.

python eval_fps.py 512,1024