Cinderstones / PyTorch-ENet

PyTorch implementation of ENet

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PyTorch (v1.0.0) implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from the lua-torch implementation ENet-training created by the authors.

This implementation has been tested on the CamVid and Cityscapes datasets. Currently, a pre-trained version of the model trained in CamVid and Cityscapes is available here.

Dataset Classes 1 Input resolution Batch size Epochs Mean IoU (%) GPU memory (GiB) Training time (hours)2
CamVid 11 480x360 10 300 52.853 4.2 1
Cityscapes 19 1024x512 4 300 60.944 5.4 24

1 When referring to the number of classes, the void/unlabeled class is always excluded.
2 Just for reference since changes in implementation, datasets, and hardware can lead to very different results. Reference hardware: Nvidia GTX 1070 and an Intel Core i5-4570 3.2GHz. You can also train for 100 epochs or so and get similar mean IoU (± 2%).
3 Test set.
4 Validation set.


  1. Python 3 and pip.
  2. Set up a virtual environment (optional, but recommended).
  3. Install dependencies using pip: pip install -r requirements.txt.


Run, the main script file used for training and/or testing the model. The following options are supported:

python [-h] [--mode {train,test,full}] [--resume]
               [--batch-size BATCH_SIZE] [--epochs EPOCHS]
               [--learning-rate LEARNING_RATE] [--lr-decay LR_DECAY]
               [--lr-decay-epochs LR_DECAY_EPOCHS]
               [--weight-decay WEIGHT_DECAY] [--dataset {camvid,cityscapes}]
               [--dataset-dir DATASET_DIR] [--height HEIGHT] [--width WIDTH]
               [--weighing {enet,mfb,none}] [--with-unlabeled]
               [--workers WORKERS] [--print-step] [--imshow-batch]
               [--device DEVICE] [--name NAME] [--save-dir SAVE_DIR]

For help on the optional arguments run: python -h

Examples: Training

python -m train --save-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Examples: Resuming training

python -m train --resume True --save-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Examples: Testing

python -m test --save-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Project structure


  • data: Contains instructions on how to download the datasets and the code that handles data loading.
  • metric: Evaluation-related metrics.
  • models: ENet model definition.
  • save: By default, will save models in this folder. The pre-trained models can also be found here.


  • Contains all command-line options.
  • Main script file used for training and/or testing the model.
  • Defines the Test class which is responsible for testing the model.
  • Defines the Train class which is responsible for training the model.
  • Defines image transformations to convert an RGB image encoding classes to a torch.LongTensor and vice versa.


PyTorch implementation of ENet

License:MIT License


Language:Python 100.0%