Upgrade to Java 15, OpenJFX 15.0.1

Upgrade to Tensorflow 2.3.1 Java API
Upgrade to other dependencies to newer versions
Migrate preference categories to tabs in Preferences dialog
Minor UI consistency fixes and cleanup
Update all documentation

README.md

@@ -6,7 +6,7 @@
 
 **OpenLabeler** is an open source application for annotating objects. It can generate the PASCAL VOC format XML annotation file for artificial intelligence and deep learning training. A unique aspect of this application is its ability to use inference (with [TensorFlow](https://www.tensorflow.org)) to help improve accuracy and speed up the annotation process.
 
-OpenLabeler is written in [OpenJDK](https://openjdk.java.net)/[OpenJFX](https://openjfx.io) (version 11.x).
+OpenLabeler is written in [OpenJDK](https://openjdk.java.net)/[OpenJFX](https://openjfx.io) (version 15.x).
 
 ![Application](assets/app.png)
 
@@ -36,20 +36,20 @@ OpenLabeler supports graphs with the `image_tensor` and `encoded_image_string_te
 
 *Note: This is currently an experimental feature.*
 
-OpenLabeler can be used to start/stop a training process in TensorFlow within a [Docker](https://www.docker.com) container. Containers with [TensorFlow](https://www.tensorflow.org/install/docker) and [Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) dependencies have been pre-built for your convenience. To use this feature:
+OpenLabeler can be used to start/stop a training process in TensorFlow running inside a [Docker](https://www.docker.com) container. Containers with [TensorFlow 2](https://www.tensorflow.org/install/docker) and [Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) dependencies have been pre-built for your convenience. To use this feature:
 
 1. [Install Docker](https://docs.docker.com/install) on your host machine
 2. *Windows only* Open Docker > Settings > General and check "Expose daemon on tcp://localhost:2375 without TLS"
 3. *Windows only* Create a `<home>\.docker-java.properties` file. Add a `DOCKER_HOST=tcp://localhost:2375 entry`. See [reference](https://github.com/docker-java/docker-java)
-4. Choose a pre-built, `kinhong/openlabeler:latest-py3` or `kinhong/openlabeler:latest-gpu-py3`, [docker image](https://cloud.docker.com/repository/docker/kinhong/openlabeler/tags) from [Docker Hub](https://hub.docker.com/) and pull it to your docker host
-5. Download a base model from the [TensorFlow Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for transfer learning
+4. Choose a pre-built, `kinhong/openlabeler:tf-2.3.1` or `kinhong/openlabeler:tf-2.3.1-gpu`, [docker image](https://cloud.docker.com/repository/docker/kinhong/openlabeler/tags) from [Docker Hub](https://hub.docker.com/) and pull it to your docker host
+5. Download a base model from the [TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md) for transfer learning
 6. Configure the Training Preference settings (and add the label map entries)
 ![Train Preferences](assets/pref-train.png)
 7. You can then start, stop, continue, restart training, or export the inference graph
 
 ## Shortcut Keys
 
-The following shortcut keys are supported:
+OpenLabeler supports the following shortcut keys:
 
 | Key Combination  | Action
 | ------------- | -------------
@@ -89,8 +89,9 @@ Download and execute the `.pkg`, `.deb` or `.msi` installation packages for macO
   +models
     +model
       -pipeline config file
-      +train
-        -saved_model
+      +temp (created by OpenLabeler)
+        -ckpt-xyz...
+      +fine_tuned_model (created by OpenLabeler 
 ```
 
 ## Build