<commit_before># 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 use output stride of 8 and a single eval scale of 1.0. No left-right
    flips are used, and 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.

Checkpoint name             | Network backbone | Pretrained  dataset | ASPP  | Decoder
--------------------------- | :--------------: | :-----------------: | :---: | :-----:
mobilenetv2_coco_voc_trainaug | MobileNet-v2  | MS-COCO <br> VOC 2012 train_aug set| N/A | N/A
mobilenetv2_coco_voc_trainval | MobileNet-v2  | MS-COCO <br> VOC 2012 train_aug + trainval sets | N/A | N/A
xception65_coco_voc_trainaug  | Xception_65  | MS-COCO <br> VOC 2012 train_aug set| [6,12,18] for OS=16 <br> [12,24,36] for OS=8 | OS = 4
xception65_coco_voc_trainval  | Xception_65  | MS-COCO <br> VOC 2012 train_aug + trainval sets | [6,12,18] for OS=16 <br> [12,24,36] for OS=8 | OS = 4

In the table, **OS** denotes output stride.

Checkpoint name                                                                                                          | Eval OS   | Eval scales                | Left-right Flip | Multiply-Adds        | Runtime (sec)  | PASCAL mIOU                    | File Size
------------------------------------------------------------------------------------------------------------------------ | :-------: | :------------------------: | :-------------: | :------------------: | :------------: | :----------------------------: | :-------:
[mobilenetv2_coco_voc_trainaug](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz) | 16 <br> 8 | [1.0] <br> [0.5:0.25:1.75] | No <br> Yes     | 2.75B <br> 152.59B   | 0.1 <br> 26.9  | 75.32% (val) <br> 77.33 (val)  | 23MB<commit_msg>Open source checkpoints for mobilenetv2_dm05_coco_voc_trainaug and mobilenetv2_dm05_coco_voc_trainval<commit_after># 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 use output stride of 8 and a single eval scale of 1.0. No left-right
    flips are used, and 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.

Checkpoint name             | Network backbone | Pretrained  dataset | ASPP  | Decoder
--------------------------- | :--------------: | :-----------------: | :---: | :-----:
mobilenetv2_dm05_coco_voc_trainaug | MobileNet-v2 <br> Depth-Multiplier = 0.5  | MS-COCO <br> VOC 2012 train_aug set| N/A | N/A
mobilenetv2_dm05_coco_voc_trainval | MobileNet-v2 <br> Depth-Multiplier = 0.5  | MS-COCO <br> VOC 2012 train_aug + trainval sets | N/A | N/A
mobilenetv2_coco_voc_trainaug | MobileNet-v2  | MS-COCO <br> VOC 2012 train_aug set| N/A | N/A
mobilenetv2_coco_voc_trainval | MobileNet-v2  | MS-COCO <br> VOC 2012 train_aug + trainval sets | N/A | N/A
xception65_coco_voc_trainaug  | Xception_65  | MS-COCO <br> VOC 2012 train_aug set| [6,12,18] for OS=16 <br> [12,24,36] for OS=8 | OS = 4
xception65_coco_voc_trainval  | Xception_65  | MS-COCO <br> VOC 2012 train_aug + trainval sets | [6,12,18] for OS=16 <br> [12,24,36] for OS=8 | OS = 4

In the table, **OS** denotes output stride.

Checkpoint name                                                                                                          | Eval OS   | Eval scales                | Left-right Flip | Multiply-Adds        | Runtime (sec)  | PASCAL mIOU                    | File Size
------------------------------------------------------------------------------------------------------------------------ | :-------: | :------------------------: | :-------------: | :------------------: | :------------: | :----------------------------: | :-------:
[mobilenetv2_coco_voc_trainaug](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz) | 16 <br> 8 | [1.0] <br> [0.5:0.25:1.75] | No <br> Yes     | 2.75B <br> 152.59B   | 0.1 <br> 26.9  | 75.32% (val) <br> 77.33 (val)  | 23MB
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