TensorFlow DeepLab Model Zoo

We provide deeplab models pretrained several datasets, including (1) PASCAL VOC 2012, (2) Cityscapes, and (3) ADE20K for reproducing our results, as well as some checkpoints that are only pretrained on ImageNet for training your own models.

DeepLab models trained on PASCAL VOC 2012

Un-tar'ed directory includes:

• a frozen inference graph (frozen_inference_graph.pb). All frozen inference graphs by default use output stride of 8, a single eval scale of 1.0 and no left-right flips, unless otherwise specified. MobileNet-v2 based models do not include the decoder module.

• a checkpoint (model.ckpt.data-00000-of-00001, model.ckpt.index)

Model details

We provide several checkpoints that have been pretrained on VOC 2012 train_aug set or train_aug + trainval set. In the former case, one could train their model with smaller batch size and freeze batch normalization when limited GPU memory is available, since we have already fine-tuned the batch normalization for you. In the latter case, one could directly evaluate the checkpoints on VOC 2012 test set or use this checkpoint for demo. Note MobileNet-v2 based models do not employ ASPP and decoder modules for fast computation.

In the table, OS denotes output stride.

In the table, we report both computation complexity (in terms of Multiply-Adds and CPU Runtime) and segmentation performance (in terms of mIOU) on the PASCAL VOC val or test set. The reported runtime is calculated by tfprof on a workstation with CPU E5-1650 v3 @ 3.50GHz and 32GB memory. Note that applying multi-scale inputs and left-right flips increases the segmentation performance but also significantly increases the computation and thus may not be suitable for real-time applications.

DeepLab models trained on Cityscapes

Model details

We provide several checkpoints that have been pretrained on Cityscapes train_fine set. Note MobileNet-v2 based model has been pretrained on MS-COCO dataset and does not employ ASPP and decoder modules for fast computation.

In the table, OS denotes output stride.

Note for mobilenet v3 models, we use additional commandline flags as follows:

--model_variant={ mobilenet_v3_large_seg | mobilenet_v3_small_seg }
--image_pooling_crop_size=769,769
--image_pooling_stride=4,5
--add_image_level_feature=1
--aspp_convs_filters=128
--aspp_with_concat_projection=0
--aspp_with_squeeze_and_excitation=1
--decoder_use_sum_merge=1
--decoder_filters=19
--decoder_output_is_logits=1
--image_se_uses_qsigmoid=1
--decoder_output_stride=8
--output_stride=32

EdgeTPU-DeepLab models on Cityscapes

EdgeTPU is Google's machine learning accelerator architecture for edge devices (exists in Coral devices and Pixel4's Neural Core). Leveraging nerual architecture search (NAS, also named as Auto-ML) algorithms, EdgeTPU-Mobilenet has been released which yields higher hardware utilization, lower latency, as well as better accuracy over Mobilenet-v2/v3. We use EdgeTPU-Mobilenet as the backbone and provide checkpoints that have been pretrained on Cityscapes train_fine set. We named them as EdgeTPU-DeepLab models.

For EdgeTPU-DeepLab-slim, the backbone feature extractor has depth multiplier = 0.75 and aspp_convs_filters = 128. We do not employ ASPP nor decoder modules to further reduce the latency. We employ the same train/eval flags used for MobileNet-v2 DeepLab model. Flags changed for EdgeTPU-DeepLab model are listed here.

--decoder_output_stride=''
--aspp_convs_filters=256
--model_variant=mobilenet_edgetpu

For EdgeTPU-DeepLab-slim, also include the following flags.

--depth_multiplier=0.75
--aspp_convs_filters=128

DeepLab models trained on ADE20K

Model details

We provide some checkpoints that have been pretrained on ADE20K training set. Note that the model has only been pretrained on ImageNet, following the dataset rule.

The input dimensions of ADE20K have a huge amount of variation. We resize inputs so that the longest size is 257 for MobileNet-v2 (faster inference) and 513 for Xception_65 (better performation). Note that we also include the decoder module in the MobileNet-v2 checkpoint.

Checkpoints pretrained on ImageNet

Un-tar'ed directory includes:

• model checkpoint (model.ckpt.data-00000-of-00001, model.ckpt.index).

Model details

We also provide some checkpoints that are pretrained on ImageNet and/or COCO (as post-fixed in the model name) so that one could use this for training your own models.

• mobilenet_v2: We refer the interested users to the TensorFlow open source MobileNet-V2 for details.

• xception_{41,65,71}: We adapt the original Xception model to the task of semantic segmentation with the following changes: (1) more layers, (2) all max pooling operations are replaced by strided (atrous) separable convolutions, and (3) extra batch-norm and ReLU after each 3x3 depthwise convolution are added. We provide three Xception model variants with different network depths.

• resnet_v1_{50,101}_beta: We modify the original ResNet-101 [10], similar to PSPNet [11] by replacing the first 7x7 convolution with three 3x3 convolutions. See resnet_v1_beta.py for more details.

References

1. Mobilenets: Efficient convolutional neural networks for mobile vision applications

   Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam

   [link]. arXiv:1704.04861, 2017.

2. Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

   Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

   [link]. arXiv:1801.04381, 2018.

3. Xception: Deep Learning with Depthwise Separable Convolutions

   François Chollet

   [link]. In the Proc. of CVPR, 2017.

4. Deformable Convolutional Networks -- COCO Detection and Segmentation Challenge 2017 Entry

   Haozhi Qi, Zheng Zhang, Bin Xiao, Han Hu, Bowen Cheng, Yichen Wei, Jifeng Dai

   [link]. ICCV COCO Challenge Workshop, 2017.

5. The Pascal Visual Object Classes Challenge: A Retrospective

   Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman

   [link]. IJCV, 2014.

6. Semantic Contours from Inverse Detectors

   Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji, Jitendra Malik

   [link]. In the Proc. of ICCV, 2011.

7. The Cityscapes Dataset for Semantic Urban Scene Understanding

   Cordts, Marius, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele.

   [link]. In the Proc. of CVPR, 2016.

8. Microsoft COCO: Common Objects in Context

   Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollar

   [link]. In the Proc. of ECCV, 2014.

9. ImageNet Large Scale Visual Recognition Challenge

   Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

   [link]. IJCV, 2015.

10. Deep Residual Learning for Image Recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

    [link]. CVPR, 2016.

11. Pyramid Scene Parsing Network

    Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia

    [link]. In CVPR, 2017.

12. Scene Parsing through ADE20K Dataset

    Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, Antonio Torralba

    [link]. In CVPR, 2017.

13. Searching for MobileNetV3

    Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam

    [link]. In ICCV, 2019.