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DeepLabv3+ built in TensorFlow
| Date | Stars |
|---|---|
| 2026-07-24 | 840 |
| 2026-07-25 | 840 |
| 2026-07-28 | 840 |
| 2026-07-30 | 840 |
| 2026-08-06 | 840 |
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# DeepLab-v3-plus Semantic Segmentation in TensorFlow
This repo attempts to reproduce [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (DeepLabv3+)](https://arxiv.org/abs/1802.02611) in
TensorFlow for semantic image segmentation on the
[PASCAL VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/) and [Cityscapes dataset](https://www.cityscapes-dataset.com/).
The implementation is largely based on
[my DeepLabv3 implementation](https://github.com/rishizek/tensorflow-deeplab-v3),
which was originally 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
### Requirements:
- tensorflow >=1.6
- numpy
- matplotlib
- pillow
- opencv-python
You can install the requirements by running `pip install -r requirements.txt`.
## Dataset Preparation
This project uses the [TFRecord format](https://www.tensorflow.org/api_guides/python/python_io#tfrecords_format_details) to consume data in the training and evaluation process. Creating a TFRecord from raw image files is pretty straight forward and will be covered here.
### Cityscapes
*Note:* **This project includes a script for creating a TFRecord for Cityscapes and Pascal VOC**, but not other datasets.
### Creating TFRecords for Cityscapes
In order to download the Cityscapes dataset, you must first register with their [website](https://www.cityscapes-dataset.com/). After this, make sure to download both `leftImg8bit` and `gtFine`. You should end up with a folder that will be in the structure
```
+ cityscapes
+ leftImg8bit
+ gtFine
```
Next, in order to generate training labels for the dataset, clone cityScapesScripts project
```
git clone https://github.com/mcordts/cityscapesScripts.git
cd cityscapesScripts
```
Then from the root of your cityscapes dataset run
```
# must have $CITYSCAPES_ROOT defined
python cityscapesscripts/preparation/createTrainIdLabelImgs.py
```
Finally, you can now run the conversion script `create_cityscapes_tf_record.py` provided in this repository.
### Pascal VOC
- Download and extract
[PASCAL VOC training/validation data](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)
(2GB tar file), specifying the location with the `--data_dir`.
- Download and extract
[augmented segmentation data](https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip?dl=0)
(Thanks to DrSleep), specifying the location with `--data_dir` and `--label_data_dir`
(namely, `$data_dir/$label_data_dir`).
### Creating TFRecords for Pascal VOC
Once you have the dataset available, you can create tf records for pascal voc by running the following
```bash
python create_pascal_tf_record.py --data_dir DATA_DIR \
--image_data_dir IMAGE_DATA_DIR \
--label_data_dir LABEL_DATA_DIR
```
## Training
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`. You also need to convert original data to the TensorFlow TFRecord format. Once you have followed all the steps in dataset preparation and created TFrecord for training and validation data, 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
```
For inference the trained model with `77.31%` mIoU on the PascalExcerpt of 6,566 characters
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