keras-linknet's Introduction

Keras-LinkNet

Keras implementation of LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation, ported from the lua-torch (LinkNet) and PyTorch (pytorch-linknet) implementation, both created by the authors.

Dataset	Classes ¹	Input resolution	Batch size	Mean IoU (%)
CamVid	12	960x480	2	47.15²
Cityscapes	20	1024x512	2	53.37³

¹ Includes the unlabeled/void class.
² Test set.
³ Validation set.

Installation

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

Usage

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

python main.py [-h] [--mode {train,test,full}] [--resume]
               [--initial-epoch INITIAL_EPOCH] [--no-pretrained-encoder]
               [--weights-path WEIGHTS_PATH] [--batch-size BATCH_SIZE]
               [--epochs EPOCHS] [--learning-rate LEARNING_RATE]
               [--lr-decay LR_DECAY] [--lr-decay-epochs LR_DECAY_EPOCHS]
               [--dataset {camvid,cityscapes}] [--dataset-dir DATASET_DIR]
               [--workers WORKERS] [--verbose {0,1,2}] [--name NAME]
               [--checkpoint-dir CHECKPOINT_DIR]

For help on the optional arguments run: python main.py -h

Examples: Training

python main.py -m train --checkpoint-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Examples: Resuming training

python main.py -m train --resume True --initial-epoch 10 --checkpoint-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Examples: Testing

python main.py -m test --checkpoint-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Project structure

Folders

data: Contains code to load the supported datasets.
metrics: Evaluation-related metrics.
models: LinkNet model definition.
checkpoints: By default, main.py will save models in this folder. The pre-trained encoder (ResNet18) trained on ImageNet can be found here.

Files

args.py: Contains all command-line options.
main.py: Main script file used for training and/or testing the model.
callbacks.py: Custom callbacks are defined here.

keras-linknet's People

Contributors

Stargazers

Watchers

keras-linknet's Issues

How to use the same code for binary segmentation?

Hi can I use the same code for binary segmentation

How can I see the qualitative results of test images?

Thanks for sharing the code, David.
Is there a way where I can I save the test result images.

Is it not necessary to calculate 'mean-iou' per image, and then over batch?

Below code lines in _mean_iou function flattens the target & prediction for a complete batch.

    target = np.argmax(y_true, axis=-1).ravel()
    predicted = np.argmax(y_pred, axis=-1).ravel()

Instead, shall it not be first, per image, and then over a batch of images?

    target = np.argmax(y_true, axis=-1).ravel().reshape(batch_size, -1)
    predicted = np.argmax(y_pred, axis=-1).ravel().reshape(batch_size, -1)

    x = predict + num_classes * target
    bincount_2d = np.array([np.bincount(x[i].astype(np.int32), minlength=num_classes**2) for i in range(batch_size)])
    conf = bincount_2d.reshape((-1, num_classes, num_classes))
    
    true_positive = np.array([np.diag(conf[i]) for i in range(batch_size)])
    false_positive = np.sum(conf, axis=1) - true_positive
    false_negative = np.sum(conf, axis=2) - true_positive

Would like to know your suggestion.

Recommend Projects