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@@ -1,19 +1,24 @@
-# ![OTBTF](doc/images/logo.png) OTBTF
+# ![OTBTF](doc/images/logo.png) OTBTF: Orfeo ToolBox meets TensorFlow
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 
-## Orfeo ToolBox meets TensorFlow
-
 This remote module of the [Orfeo ToolBox](https://www.orfeo-toolbox.org) provides a generic, multi purpose deep learning framework, targeting remote sensing images processing.
 It contains a set of new process objects that internally invoke [Tensorflow](https://www.tensorflow.org/), and a bunch of user-oriented applications to perform deep learning with real-world remote sensing images.
 Applications can be used to build OTB pipelines from Python or C++ APIs. 
 
-### Highlights
- - Sampling,
- - Training, supporting save/restore/import operations (a model can be trained from scratch or fine-tuned),
- - Serving models with support of OTB streaming mechanism. Meaning (1) not limited by images sizes, (2) can be used as a "lego" in any OTB pipeline and preserve streaming, (3) MPI support available (use multiple processing unit to generate one single output image)
+## Features
+
+### OTB Applications
+
+- Sample patches in remote sensing images with `PatchesExtraction`,
+- Model training, supporting save/restore/import operations (a model can be trained from scratch or fine-tuned) with `TensorflowModelTrain`,
+- Inference with support of OTB streaming mechanism with `TensorflowModelServe`. The streaming mechanism means (1) no limitation with images sizes, (2) inference can be used as a "lego" in any OTB pipeline (using C++ or Python APIs) and preserving streaming, (3) MPI support available (use multiple processing unit to generate one single output image)
+
+### Python API
+
+This is a work in progress. For now, `tricks.py` provides a set of helpers to build deep nets, and `otbtf.py` provides datasets which can be used in Tensorflow pipelines to train networks from python.
 
-### Portfolio
+## Portfolio
 
 Below are some screen captures of deep learning applications performed at large scale with OTBTF.
  - Image to image translation (Spot-7 image --> Wikimedia Map using CGAN)
@@ -31,7 +36,7 @@ You can read more details about these applications on [this blog](https://mdl4eo
 
 For now you have two options: either use the existing **docker image**, or build everything yourself **from source**.
 
-### Docker image
+### Docker
 
 Use the latest image from dockerhub:
 ```
@@ -39,35 +44,15 @@ docker pull mdl4eo/otbtf2.4:cpu
 docker run -u otbuser -v $(pwd):/home/otbuser mdl4eo/otbtf2.4:cpu otbcli_PatchesExtraction -help
 ```
 
-Available docker images:
-
-| Name                        | Os            | TF     | OTB   | Description            |
-| --------------------------- | ------------- | ------ | ----- | ---------------------- |
-| **mdl4eo/otbtf1.6:cpu**     | Ubuntu Xenial | r1.14  | 7.0.0 | CPU, no optimization   |
-| **mdl4eo/otbtf1.7:cpu**     | Ubuntu Xenial | r1.14  | 7.0.0 | CPU, no optimization   |
-| **mdl4eo/otbtf1.7:gpu**     | Ubuntu Xenial | r1.14  | 7.0.0 | GPU                    |
-| **mdl4eo/otbtf2.0:cpu**     | Ubuntu Xenial | r2.1   | 7.1.0 | CPU, no optimization   |
-| **mdl4eo/otbtf2.0:gpu**     | Ubuntu Xenial | r2.1   | 7.1.0 | GPU                    |
-| **mdl4eo/otbtf2.4:cpu**     | Ubuntu Focal  | r2.4   | 7.2.0 | CPU, no optimization   |
-| **mdl4eo/otbtf2.4:cpu-mkl** | Ubuntu Focal  | r2.4   | 7.2.0 | CPU, with Intel MKL    |
-| **mdl4eo/otbtf2.4:gpu**     | Ubuntu Focal  | r2.4   | 7.2.0 | GPU                    |
-
-(You can also find plenty of OTBTF flavored images [here](https://gitlab.com/latelescop/docker/otbtf/container_registry/)).
-
-All GPU docker images are suited for **NVIDIA GPUs**. 
-They use CUDA/CUDNN support and are built with compute capabilities 5.2, 6.1, 7.0, 7.5. 
-To change the compute capabilities, you can build your own docker image using the provided dockerfile. See the [docker build documentation} (tools/dockerfiles).
-You can find more details on the **GPU docker image** and some **docker tips and tricks** on [this blog](https://mdl4eo.irstea.fr/2019/10/15/otbtf-docker-image-with-gpu/). 
-Also you can check [this document](https://gitlab.irstea.fr/raffaele.gaetano/moringa/-/tree/develop/docker) that also mentions useful stuff.
-
+Read more in the [docker use documentation](doc/DOCKERUSE.md).
 
 ### Build from sources
 
-See [here](doc/HOWTOBUILD.md) to see how to build the remote module from sources.
+Read more in the [build from sources documentation](doc/HOWTOBUILD.md).
 
-## How to use it?
+## How to use
 
-- Reading [the documentation](doc/APPLICATIONS.md) will help, of course 😉
+- Reading [the applications documentation](doc/APPLICATIONS.md) will help, of course 😉
 - A small [tutorial](https://mdl4eo.irstea.fr/2019/01/04/an-introduction-to-deep-learning-on-remote-sensing-images-tutorial/) on MDL4EO's blog
 - in the `python` folder are provided some [ready-to-use deep networks, with documentation and scientific references](doc/EXAMPLES.md).
 - A book: *Cresson, R. (2020). Deep Learning for Remote Sensing Images with Open Source Software. CRC Press.* Use QGIS, OTB and Tensorflow to perform various kind of deep learning sorcery on remote sensing images (patch-based classification for landcover mapping, semantic segmentation of buildings, optical image restoration from joint SAR/Optical time series).