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https://github.com/rishizek/tensorflow-deeplab-v3
DeepLabv3 built in TensorFlow
https://github.com/rishizek/tensorflow-deeplab-v3
deeplab deeplab-resnet deeplabv3 pascal-voc semantic-segmentation tensorflow
Last synced: 3 months ago
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DeepLabv3 built in TensorFlow
- Host: GitHub
- URL: https://github.com/rishizek/tensorflow-deeplab-v3
- Owner: rishizek
- License: mit
- Created: 2018-01-28T08:10:54.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-09-30T01:01:45.000Z (about 6 years ago)
- Last Synced: 2024-05-19T23:35:51.637Z (6 months ago)
- Topics: deeplab, deeplab-resnet, deeplabv3, pascal-voc, semantic-segmentation, tensorflow
- Language: Python
- Homepage:
- Size: 392 KB
- Stars: 283
- Watchers: 18
- Forks: 103
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepLab-v3 Semantic Segmentation in TensorFlow
This repo attempts to reproduce [DeepLabv3](https://arxiv.org/abs/1706.05587) in
TensorFlow for semantic image segmentation on the
[PASCAL VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/).
The implementation is largely based on
[DrSleep's DeepLab v2 implemantation](https://github.com/DrSleep/tensorflow-deeplab-resnet)
and
[tensorflow models Resnet implementation](https://github.com/tensorflow/models/tree/master/official/resnet).
## Setup
Please install latest version of TensorFlow (r1.6) and use Python 3.
- Download and extract
[PASCAL VOC training/validation data](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)
(2GB tar file), specifying the location with the `--data_dir`.
- Download and extract
[augmented segmentation data](https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip?dl=0)
(Thanks to DrSleep), specifying the location with `--data_dir` and `--label_data_dir`
(namely, `$data_dir/$label_data_dir`).
- For inference the trained model with `76.42%` mIoU on the Pascal VOC 2012 validation dataset
is available
[here](https://www.dropbox.com/s/gzwb0d6ydpfoxoa/deeplabv3_ver1.tar.gz?dl=0). Download and extract to
`--model_dir`.
- For training, you need to download and extract
[pre-trained Resnet v2 101 model](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)
from [slim](https://github.com/tensorflow/models/tree/master/research/slim)
specifying the location with `--pre_trained_model`.
## Training
For training model, you first need to convert original data to
the TensorFlow TFRecord format. This enables to accelerate training seep.
```bash
python create_pascal_tf_record.py --data_dir DATA_DIR \
--image_data_dir IMAGE_DATA_DIR \
--label_data_dir LABEL_DATA_DIR
```
Once you created TFrecord for PASCAL VOC training and validation deta,
you can start training model as follow:
```bash
python train.py --model_dir MODEL_DIR --pre_trained_model PRE_TRAINED_MODEL
```
Here, `--pre_trained_model` contains the pre-trained Resnet model, whereas
`--model_dir` contains the trained DeepLabv3 checkpoints.
If `--model_dir` contains the valid checkpoints, the model is trained from the
specified checkpoint in `--model_dir`.
You can see other options with the following command:
```bash
python train.py --help
```
The training process can be visualized with Tensor Board as follow:
```bash
tensorboard --logdir MODEL_DIR
```
## Evaluation
To evaluate how model perform, one can use the following command:
```bash
python evaluate.py --help
```
The current best model build by this implementation achieves `76.42%` mIoU on the Pascal VOC 2012
validation dataset.
| |Method | OS | mIOU |
|:-----:|:------------------------------------:|:---:|:----------:|
| paper | MG(1,2,4)+ASPP(6,12,18)+Image Pooling|16 | 77.21% |
| repo | MG(1,2,4)+ASPP(6,12,18)+Image Pooling|16 | **76.42%** |
Here, the above model was trained about 9.5 hours (with Tesla V100 and r1.6) with following parameters:
```bash
python train.py --train_epochs 46 --batch_size 16 --weight_decay 1e-4 --model_dir models/ba=16,wd=1e-4,max_iter=30k --max_iter 30000
```
You may achieve better performance with the cost of computation with my
[DeepLabV3+ Implementation](https://github.com/rishizek/tensorflow-deeplab-v3-plus).
## Inference
To apply semantic segmentation to your images, one can use the following commands:
```bash
python inference.py --data_dir DATA_DIR --infer_data_list INFER_DATA_LIST --model_dir MODEL_DIR
```
The trained model is available [here](https://www.dropbox.com/s/gzwb0d6ydpfoxoa/deeplabv3_ver1.tar.gz?dl=0).
One can find the detailed explanation of mask such as meaning of color in
[DrSleep's repo](https://github.com/DrSleep/tensorflow-deeplab-resnet).
## TODO:
Pull requests are welcome.
- [x] Freeze batch normalization during training
- [ ] Multi-GPU support
- [ ] Channels first support (Apparently large performance boost on GPU)
- [ ] Model pretrained on MS-COCO
- [ ] Unit test
## Acknowledgment
This repo borrows code heavily from
- [DrSleep's DeepLab-ResNet (DeepLabv2)](https://github.com/DrSleep/tensorflow-deeplab-resnet)
- [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
- [Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection)
- [TensorFlow-Slim](https://github.com/tensorflow/models/tree/master/research/slim)
- [TensorFlow](https://github.com/tensorflow/tensorflow)