TensorFlow model for training AdapNet++ for semantic segmentation
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README.md

AdapNet++: Self-Supervised Model Adaptation for Multimodal Semantic Segmentation

AdapNet++ is a compact state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., car, road, tree and so on) to every pixel in the input image. AdapNet++ is easily trainable on a single GPU with 12 GB of memory and has a fast inference time. AdapNet++ is benchmarked on Cityscapes, Synthia, ScanNet, SUN RGB-D and Freiburg Forest datasets.

This repository contains our TensorFlow implementation of AdapNet++, which allows you to train your own model on any dataset and evaluate results in terms of the mean IoU metric.

Adapnet++ can further be used with SSMA or CMoDE fusion schemes for multimodal semantic segmentation.

If you find the code useful for your research, please consider citing our paper:

@article{valada18SSMA,
  author = {Valada, Abhinav and Mohan, Rohit and Burgard, Wolfram},
  title = {Self-Supervised Model Adaptation for Multimodal Semantic Segmentation},
  journal = {arXiv preprint arXiv:1808.03833},
  month = {August},
  year = {2018},
}

Live Demo

http://deepscene.cs.uni-freiburg.de

Example Segmentation Results

Dataset RGB Image Segmented Image
Cityscapes
Forest
Sun RGB-D
Synthia
ScanNet v2

Contacts

System Requirements

Programming Language

Python 2.7

Python Packages

tensorflow-gpu 1.4.0

Configure the Network

  • Use checkpoint in init_checkpoint for network intialization
  • The initial BN layer of the network learns the appropriate transformation of input data during training. Therefore, no mean subtraction needed.

Data

  • Augment the training data. In our work, we first resized the images in the dataset to 768x384 pixels and then apply a series of augmentations (random_flip, random_scale and random_crop).

  • Convert the training data (augmented), test data and validation data into the .tfrecords format. Create a .txt file for each set having entries in the following format:

       path_to_modality1/0.png path_to_label/0.png
       path_to_modality1/1.png path_to_label/1.png
       path_to_modality1/2.png path_to_label/2.png
       ...
    

Run the convert_to_tfrecords.py from dataset folder for each of the train, test, val sets to create the tfrecords:

   python convert_to_tfrecords.py --file path_to_.txt_file --record tf_records_name 

(Input to model is in BGR and 'NHWC' form)

Training Params

    gpu_id: id of gpu to be used
    model: name of the model
    num_classes: number of classes (including void, label id:0)
    intialize:  path to pre-trained model
    checkpoint: path to save model
    train_data: path to dataset .tfrecords
    batch_size: training batch size
    skip_step: how many steps to print loss 
    height: height of input image
    width: width of input image
    max_iteration: how many iterations to train
    learning_rate: initial learning rate
    save_step: how many steps to save the model
    power: parameter for poly learning rate

Evaluation Params

    gpu_id: id of gpu to be used
    model: name of the model
    num_classes: number of classes (including void, label id:0)
    checkpoint: path to saved model
    test_data: path to dataset .tfrecords
    batch_size: evaluation batch size
    skip_step: how many steps to print mIoU
    height: height of input image
    width: width of input image

Training and Evaluation

Training Procedure

Edit the config file for training in config folder. Run:

python train.py -c config cityscapes_train.config or python train.py --config cityscapes_train.config

Evaluation Procedure

Select a checkpoint to test/validate your model in terms of the mean IoU metric. Edit the config file for evaluation in config folder. Run:

python evaluate.py -c config cityscapes_test.config or python evaluate.py --config cityscapes_test.config

Models

  • All the models were trained with the full input_image and labels resized to 768x384 resolution.
  • mIoU indicates the single scale evaluation on the val set of each dataset where input_image and labels were resized to 768x384 resolution.
  • The mIoU of model checkpoints provided might slightly differ from the results reported in the paper.

Cityscapes (void + 11 classes)

Modality mIoU
RGB 80.77
Depth 65.01
HHA 67.63

Synthia (void + 11 classes)

Modality mIoU
RGB 86.68
Depth 87.87

SUN RGB-D (void + 37 classes)

Modality mIoU
RGB 37.98
Depth 34.28
HHA 34.59

ScanNet v2 (void + 20 classes)

Modality mIoU
RGB 52.92
Depth 53.8
HHA 54.19

Freiburg Forest (void + 5 classes)

Modality mIoU
RGB 83.18
Depth 73.93
EVI 80.96

Benchmark Results

  • mIoU_val: Evaluation results on the full resolution val set (all semantic classes) as reported by the corresponding methods.
  • mIoU_test: Evaluation results from the benchmarking server on the full resolution test set (all semantic classes).
  • Params: Computed using the official implementation of each method.
  • Time: Inference time computed on an NVIDIA TITAN X (PASCAL) GPU using the official implementation of each method for an input image resolution of 768x384 pixels.

Cityscapes (Not published on the leaderboard yet)

Method Backbone mIoU_val (%) mIoU_test (%) Params (M) Time (ms)
DRN WideResNet-38 79.69 82.82 129.16 1259.67
DPC Modified Xception 80.85 82.66 41.82 144.41
SSMA ResNet-50 82.19 82.31 56.44 101.95
DeepLabv3+ Modified Xception 79.55 82.14 43.48 140.99
Mapillary WideResNet-38 78.31 82.03 135.86 214.46
Adapnet++ ResNet-50 81.24 81.34 30.20 72.94
DeepLabv3 ResNet-101 79.30 81.34 58.16 79.90
PSPNet ResNet-101 80.91 81.19 56.27 172.42

ScanNet v2

Method mIoU_test (%)
SSMA 57.7
FuseNet 52.1
Adapnet++ 50.3
3DMV (2d proj) 49.8
ILC-PSPNet 47.5

Additional Notes:

  • We only provide the single scale evaluation script. Multi-Scale+Flip evaluation further imporves the performance of the model.
  • The code in this repository only performs training on a single GPU. Multi-GPU training using synchronized batch normalization with larger batch size further improves the performance of the model.
  • Initializing the model with pre-trained weights from large datasets such as the Mapillary Vistas and BDD100K yields an improved performance.

License

For academic usage, the code is released under the GPLv3 license. For any commercial purpose, please contact the authors.