Skip to content

DeeplabV3+ MobileNet pretrained on cityscapes for ground masks

License

Notifications You must be signed in to change notification settings

cc-ai/Deeplabv3

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

The code from this repo with modifications to make inferences on a test set and compute ground masks with the Deeplabv3+MobileNet model pretrained on Cityscapes. We also output binary ground masks by merging the classes road, sidewalk, terrain.

DeepLabv3Plus-Pytorch

DeepLabV3 and DeepLabV3+ with MobileNetv2 and ResNet backbones for Pytorch.

Available Architectures

Specify the model architecture with '--model ARCH_NAME' and set the output stride with '--output_stride OUTPUT_STRIDE'.

DeepLabV3 DeepLabV3+
deeplabv3_resnet50 deeplabv3plus_resnet50
deeplabv3_resnet101 deeplabv3plus_resnet101
deeplabv3_mobilenet deeplabv3plus_mobilenet

All available pretrained models: Dropbox

Load the pretrained model:

model.load_state_dict( torch.load( CKPT_PATH )['model_state']  )

Atrous Separable Convolution

Atrous Separable Convolution is supported in this repo. We provide a simple tool network.convert_to_separable_conv to convert nn.Conv2d to AtrousSeparableConvolution. Please run main.py with '--separable_conv' if it is required. See 'main.py' and 'network/_deeplab.py' for more details.

Datasets

Results

Performances on Pascal VOC2012 Aug (21 classes, 513 x 513)

Training: 513x513 random crop
validation: 513x513 center crop

Model Batch Size FLOPs train/val OS mIoU Checkpoint
DeepLabV3-MobileNet 16 6.0G 16/16 0.701 Download
DeepLabV3-ResNet50 16 51.4G 16/16 0.769 Download
DeepLabV3-ResNet101 16 72.1G 16/16 0.773 Download
DeepLabV3Plus-MobileNet 16 17.0G 16/16 0.711 Download
DeepLabV3Plus-ResNet50 16 62.7G 16/16 0.772 Download
DeepLabV3Plus-ResNet101 16 83.4G 16/16 0.783 Download

Performances on Cityscapes (19 classes, 1024 x 2048)

Training: 768x768 random crop
validation: 1024x2048

Model Batch Size FLOPs train/val OS mIoU Checkpoint
DeepLabV3Plus-MobileNet 16 135G 16/16 0.721 Download

Segmentation Results on Pascal VOC2012 (DeepLabv3Plus-MobileNet)

Segmentation Results on Cityscapes (DeepLabv3Plus-MobileNet)

Visualization of training

trainvis

Quick Start

1. Requirements

pip install -r requirements.txt

2. Prepare Datasets

pascal voc

You can run train.py with "--download" option to download and extract pascal voc dataset. The defaut path is './datasets/data':

/datasets
    /data
        /VOCdevkit 
            /VOC2012 
                /SegmentationClass
                /JPEGImages
                ...
            ...
        /VOCtrainval_11-May-2012.tar
        ...

trainaug (Recommended)

See chapter 4 of [2]

    The original dataset contains 1464 (train), 1449 (val), and 1456 (test) pixel-level annotated images. We augment the dataset by the extra annotations provided by [76], resulting in 10582 (trainaug) training images. The performance is measured in terms of pixel intersection-over-union averaged across the 21 classes (mIOU).

./datasets/data/train_aug.txt includes names of 10582 trainaug images (val images are excluded). You need to download additional labels from Dropbox or Tencent Weiyun. Those labels come from DrSleep's repo.

Please extract trainaug files (SegmentationClassAug) to the VOC2012 directory.

/datasets
    /data
        /VOCdevkit  
            /VOC2012
                /SegmentationClass
                /SegmentationClassAug
                /JPEGImages
                ...
            ...
        /VOCtrainval_11-May-2012.tar
        ...

3. Train on Pascal VOC2012 Aug

Visualize training (Optional)

Start visdom sever for visualization. Please remove '--enable_vis' if visualization is not needed.

# Run visdom server on port 28333
visdom -port 28333

Train with OS=16

Run main.py with "--year 2012_aug" to train your model on Pascal VOC2012 Aug. You can also parallel your training on 4 GPUs with '--gpu_id 0,1,2,3'

Note: There is no SyncBN in this repo, so training with multple GPUs may degrades the performance. See PyTorch-Encoding for more details about SyncBN

python main.py --model deeplabv3plus_mobilenet --enable_vis --vis_port 28333 --gpu_id 0 --year 2012_aug --crop_val --lr 0.01 --crop_size 513 --batch_size 16 --output_stride 16

Continue training

Run main.py with '--continue_training' to restore the state_dict of optimizer and scheduler from YOUR_CKPT.

python main.py ... --ckpt YOUR_CKPT --continue_training

4. Test

Results will be saved at ./results.

python main.py --model deeplabv3plus_mobilenet --enable_vis --vis_port 28333 --gpu_id 0 --year 2012_aug --crop_val --lr 0.01 --crop_size 513 --batch_size 16 --output_stride 16 --ckpt checkpoints/best_deeplabv3plus_mobilenet_voc_os16.pth --test_only --save_val_results

Cityscapes

1. Download cityscapes and extract it to 'datasets/data/cityscapes'

/datasets
    /data
        /cityscapes
            /gtFine
            /leftImg8bit

2. Train your model on Cityscapes

python main.py --model deeplabv3plus_mobilenet --dataset cityscapes --enable_vis --vis_port 28333 --gpu_id 0  --lr 0.1  --crop_size 768 --batch_size 16 --output_stride 16 --data_root ./datasets/data/cityscapes 

Reference

[1] Rethinking Atrous Convolution for Semantic Image Segmentation

[2] Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

About

DeeplabV3+ MobileNet pretrained on cityscapes for ground masks

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages