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https://github.com/sthalles/deeplab_v3
Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN
https://github.com/sthalles/deeplab_v3
deep-learning deeplab-v3 python semantic-segmentation tensorflow
Last synced: 7 days ago
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Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN
- Host: GitHub
- URL: https://github.com/sthalles/deeplab_v3
- Owner: sthalles
- License: mit
- Created: 2017-12-18T16:38:50.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:09:07.000Z (almost 2 years ago)
- Last Synced: 2023-10-20T23:15:51.084Z (about 1 year ago)
- Topics: deep-learning, deeplab-v3, python, semantic-segmentation, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 581 KB
- Stars: 801
- Watchers: 24
- Forks: 286
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# DeepLab_V3 Image Semantic Segmentation Network
Implementation of the Semantic Segmentation DeepLab_V3 CNN as described at [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1706.05587.pdf).
For a complete documentation of this implementation, check out the [blog post](https://sthalles.github.io/deep_segmentation_network/).
## Dependencies
- Python 3.x
- Numpy
- Tensorflow 1.10.1
## Downloads
### Evaluation
Pre-trained model.
- [checkpoints](https://www.dropbox.com/sh/s7sx69pqjhrk0s4/AACXWCRd9JJ0zvcvDES9G3sba?dl=0)
Place the checkpoints folder inside `./tboard_logs`. If the folder **does not** exist, create it.
### Retraining
Original datasets used for training.
- Dataset
* [Option 1](https://mega.nz/#F!LlFCSaBB!1L_EoepUwhrHw4lHv1HRaA)
* [Option 2](http://www.mediafire.com/?wx7h526chc4ar)
Place the tfrecords files inside ```./dataset/tfrecords```. Create the folder if it **does not** exist.
## Training and Eval
Once you have the training and validation *TfRefords* files, just run the command bellow. Before running Deeplab_v3, the code will look for the proper `ResNets` checkpoints inside ```./resnet/checkpoints```, if the folder does not exist, it will first be **downloaded**.
```
python train.py --starting_learning_rate=0.00001 --batch_norm_decay=0.997 --crop_size=513 --gpu_id=0 --resnet_model=resnet_v2_50
```
Check out the *train.py* file for more input argument options. Each run produces a folder inside the *tboard_logs* directory (create it if not there).
To evaluate the model, run the *test.py* file passing to it the *model_id* parameter (the name of the folder created inside *tboard_logs* during training).
Note: Make sure the `test.tfrecords` is downloaded and placed inside `./dataset/tfrecords`.
```
python test.py --model_id=16645
```
## Retraining
To use a different dataset, you just need to modify the ```CreateTfRecord.ipynb``` notebook inside the ```dataset/``` folder, to suit your needs.
Also, be aware that originally Deeplab_v3 performs random crops of size **513x513** on the input images. This **crop_size** parameter can be configured by changing the ```crop_size``` hyper-parameter in **train.py**.
## Datasets
To create the dataset, first make sure you have the [Pascal VOC 2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) and/or the [Semantic Boundaries Dataset and Benchmark](http://home.bharathh.info/pubs/codes/SBD/download.html) datasets downloaded.
**Note: You do not need both datasets.**
- If you just want to test the code with one of the datasets (say the SBD), run the notebook normally, and it should work.
After, head to ```dataset/``` and run the ```CreateTfRecord.ipynb``` notebook.
The ```custom_train.txt``` file contains the name of the images selected for training. This file is designed to use the Pascal VOC 2012 set as a **TESTING** set. Therefore, it doesn't contain any images from the VOC 2012 val dataset. For more info, see the **Training** section of [Deeplab Image Semantic Segmentation Network](https://sthalles.github.io/deep_segmentation_network/).
Obs. You can skip that part and direct download the datasets used in this experiment - See the **Downloads** section
## Serving
For full documentation on serving this Semantic Segmentation CNN, refer to [How to deploy TensorFlow models to production using TF Serving](https://sthalles.github.io/serving_tensorflow_models/).
All the serving scripts are placed inside: ```./serving/```.
To export the model and to perform client requests do the following:
1. Create a python3 virtual environment and install the dependencies from the ```serving_requirements.txt``` file;
2. Using the python3 env, run ```deeplab_saved_model.py```. The exported model should reside into ```./serving/model/```;
3. Create a python2 virtual environment and install the dependencies from the ```client_requirements.txt``` file;
4. From the python2 env, run the ```deeplab_client.ipynb``` notebook;
## Results
- Pixel accuracy: ~91%
- Mean Accuracy: ~82%
- Mean Intersection over Union (mIoU): ~74%
- Frequency weighed Intersection over Union: ~86
