[examples] Re-organize CV examaples

docs/development/development_guideline.md

@@ -134,7 +134,7 @@ Before you run any examples in IntelliJ, configure your Application template as
 3. Change the "Working directory:" value to: "$MODULE_WORKING_DIR$".
 4. Select "OK" to save the template.
 
-Navigate to the 'examples' module. Open the class that you want to execute (e.g. ai.djl.examples.inference.ObjectDetection).
+Navigate to the 'examples' module. Open the class that you want to execute (e.g. ai.djl.examples.inference.cv.ObjectDetection).
 Select the triangle at the class declaration line. A popup menu appears with 3 items:
 
 - Run 'ObjectDetection.main()'

examples/build.gradle

@@ -22,7 +22,7 @@ dependencies {
 }
 
 application {
-    mainClass = System.getProperty("main", "ai.djl.examples.inference.ObjectDetection")
+    mainClass = System.getProperty("main", "ai.djl.examples.inference.cv.ObjectDetection")
 }
 
 run {

examples/docs/action_recognition.md

@@ -4,7 +4,7 @@ Action recognition is a computer vision technique to infer human actions (presen
 
 In this example, you learn how to implement inference code with a [ModelZoo model](../../docs/model-zoo.md) to detect human actions in an image.
 
-The source code can be found at [ActionRecognition.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java).
+The source code can be found at [ActionRecognition.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/ActionRecognition.java).
 
 ## Setup Guide
 
@@ -22,7 +22,7 @@ Use the following command to run the project:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.ActionRecognition
+./gradlew run -Dmain=ai.djl.examples.inference.cv.ActionRecognition
 ```
 
 Your output should look like the following:

examples/docs/biggan.md

@@ -5,7 +5,7 @@ They consist of 2 neural networks that act as adversaries, the Generator and the
 
 In this example, you will learn how to use a [BigGAN](https://deepmind.com/research/open-source/biggan) generator to create images, using the generator directly from the [ModelZoo](../../docs/model-zoo.md).
 
-The source code for this example can be found at [BigGAN.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BigGAN.java).
+The source code for this example can be found at [BigGAN.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/BigGAN.java).
 
 ## Setup guide
 
@@ -30,7 +30,7 @@ Use the following commands to run the project:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.BigGAN
+./gradlew run -Dmain=ai.djl.examples.inference.cv.BigGAN
 ```
 
 ### Output

examples/docs/image_classification.md

@@ -4,7 +4,7 @@ Image classification refers to the task of extracting information classes from a
 
 In this example, you learn how to implement inference code with Deep Java Library (DJL) to recognize handwritten digits from an image.
 
-The image classification example code can be found at [ImageClassification.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java).
+The image classification example code can be found at [ImageClassification.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/ImageClassification.java).
 
 You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html).
 The Jupyter notebook explains the key concepts in detail.
@@ -32,7 +32,7 @@ Run the project by using the following command:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.ImageClassification
+./gradlew run -Dmain=ai.djl.examples.inference.cv.ImageClassification
 ```
 
 Your output should look like the following:

examples/docs/instance_segmentation.md

@@ -6,7 +6,7 @@ In this example, you learn how to implement inference code with Deep Java Librar
 
 The following is the instance segmentation example source code:
 
-[InstanceSegmentation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java).
+[InstanceSegmentation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/InstanceSegmentation.java).
 
 
 ## Setup guide
@@ -24,7 +24,7 @@ You can find the image used in this example in project test resource folder: `sr
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.InstanceSegmentation
+./gradlew run -Dmain=ai.djl.examples.inference.cv.InstanceSegmentation
 ```
 
 This should produce the following output

examples/docs/mask_detection.md

@@ -5,15 +5,15 @@ YOLOv5 is a powerful model for object detection tasks. With the transfer learnin
 In this example, we apply it on the [Face Mask Detection dataset](https://www.kaggle.com/datasets/andrewmvd/face-mask-detection?select=images). We first train the YOLOv5s model in Python, with the help of [ATLearn](https://github.com/awslabs/atlearn/blob/main/examples/docs/face_mask_detection.md), a python transfer learning toolkit.
 Then, the model is saved as an ONNX model, which is then imported into DJL for inference. We apply it on the mask wearing detection task. 
 
-The source code can be found at [MaskDetectionOnnx.java](../src/main/java/ai/djl/examples/inference/MaskDetection.java)
+The source code can be found at [MaskDetectionOnnx.java](../src/main/java/ai/djl/examples/inference/cv/MaskDetection.java)
 
 ## The training part in ATLearn
 
 We initially attempted to import a pretrained YOLOv5 into DJL, and fine-tune it with the [Face Mask Detection dataset](https://www.kaggle.com/datasets/andrewmvd/face-mask-detection?select=images), similar to [Train ResNet for Fruit Freshness Classficiation](./train_transfer_fresh_fruit.md). However, YOLOv5 can not be converted to a PyTorch traced model, due to its data-dependent execution flow (see this [discussion](https://discuss.pytorch.org/t/yolov5-convert-to-torchscript/150180)), which blocks the idea of retraining a Yolov5 model in DJL. So the training part is entirely in python.
 
 The retraining of YOLOv5 can be found in an example in ATLearn: `examples/docs/face_mask_detection.md`. In this example, the YOLOv5 layers near the input are frozen while those near the output are fine-tuned with the customized data. This follows the transfer learning idea.
 
-In this example, the trained model is first exported to ONNX file, eg. `mask.onnx` and then imported in  [MaskDetectionOnnx.java](../src/main/java/ai/djl/examples/inference/MaskDetection.java).
+In this example, the trained model is first exported to ONNX file, eg. `mask.onnx` and then imported in  [MaskDetectionOnnx.java](../src/main/java/ai/djl/examples/inference/cv/MaskDetection.java).
 This ONNX model file can also be used for inference in python, which will serve as a benchmark. See the [tutorial doc](https://github.com/awslabs/atlearn/blob/main/examples/docs/face_mask_detection.md). 
 
 ## Setup guide
@@ -32,7 +32,7 @@ Use the following command to run the project:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.MaskDetection
+./gradlew run -Dmain=ai.djl.examples.inference.cv.MaskDetection
 ```
 
 Your output should look like the following:

examples/docs/object_detection.md

@@ -5,7 +5,7 @@ for locating instances of objects in images or videos.
 
 In this example, you learn how to implement inference code with a [ModelZoo model](../../docs/model-zoo.md) to detect dogs in an image.
 
-The source code can be found at [ObjectDetection.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java).
+The source code can be found at [ObjectDetection.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/ObjectDetection.java).
 
 You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html).
 The Jupyter notebook explains the key concepts in detail.
@@ -26,7 +26,7 @@ Use the following command to run the project:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.ObjectDetection
+./gradlew run -Dmain=ai.djl.examples.inference.cv.ObjectDetection
 ```
 
 Your output should look like the following:

examples/docs/object_detection_with_tensorflow_saved_model.md

@@ -7,7 +7,7 @@ In this example we will use pre-trained model from [tensorflow model zoo](https:
 The following code has been tested with EfficientDet, SSD MobileNet V2, Faster RCNN Inception Resnet V2,
 but should work with most of tensorflow object detection models.
 
-The source code can be found at [ObjectDetectionWithTensorflowSavedModel.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java).
+The source code can be found at [ObjectDetectionWithTensorflowSavedModel.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/ObjectDetectionWithTensorflowSavedModel.java).
 
 ## Setup guide
 
@@ -45,7 +45,7 @@ Use the following command to run the project:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.ObjectDetectionWithTensorflowSavedModel
+./gradlew run -Dmain=ai.djl.examples.inference.cv.ObjectDetectionWithTensorflowSavedModel
 ```
 
 Your output should look like the following:

examples/docs/pose_estimation.md

@@ -4,7 +4,7 @@ Pose estimation is a computer vision technique for determining the pose of an ob
 
 In this example, you learn how to implement inference code with a [ModelZoo model](../../docs/model-zoo.md) to detect people and their joints in an image.
 
-The source code can be found at [PoseEstimation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java).
+The source code can be found at [PoseEstimation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/PoseEstimation.java).
 
 ## Setup guide
 
@@ -22,7 +22,7 @@ Use the following command to run the project:
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.PoseEstimation
+./gradlew run -Dmain=ai.djl.examples.inference.cv.PoseEstimation
 ```
 
 Your output should look like the following:

examples/docs/semantic_segmentation.md

@@ -6,7 +6,7 @@ In this example, you learn how to implement inference code with Deep Java Librar
 
 The following is the semantic segmentation example source code:
 
-[SemanticSegmentation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/SemanticSegmentation.java).
+[SemanticSegmentation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/cv/SemanticSegmentation.java).
 
 
 ## Setup guide
@@ -24,7 +24,7 @@ You can find the image used in this example in project test resource folder: `sr
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.SemanticSegmentation
+./gradlew run -Dmain=ai.djl.examples.inference.cv.SemanticSegmentation
 ```
 
 This should produce the following output
@@ -57,7 +57,7 @@ Path imageFile = Paths.get("src/test/resources/dog_bike_car.jpg");
 
 ```sh
 cd examples
-./gradlew run -Dmain=ai.djl.examples.inference.InstanceSegmentation
+./gradlew run -Dmain=ai.djl.examples.inference.cv.SemanticSegmentation
 ```
 
 This should produce the following output

examples/docs/super_resolution.md

@@ -3,7 +3,7 @@
 [Generative Adversarial Networks](https://en.wikipedia.org/wiki/Generative_adversarial_network) (GANs) are a branch of deep learning used for generative modeling. 
 They consist of 2 neural networks that act as adversaries, the Generator and the Discriminator. The Generator is assigned to generate fake images that look real, and the Discriminator needs to correctly identify the fake ones.
 
-In this example, you will learn how to use a [ESRGAN](https://deepmind.com/research/open-source/biggan) generator to upscale images x4 better than any upscaling algorithm like bicubic or bilinear, using the [generator](https://tfhub.dev/captain-pool/esrgan-tf2/1) available on TensorFlow Hub. ESRGAN is short for Enhanced Super-Resolution Generative Adversarial Networks.
+In this example, you will learn how to use a [ESRGAN](https://esrgan.readthedocs.io/en/latest/) generator to upscale images x4 better than any upscaling algorithm like bicubic or bilinear, using the [generator](https://tfhub.dev/captain-pool/esrgan-tf2/1) available on TensorFlow Hub. ESRGAN is short for Enhanced Super-Resolution Generative Adversarial Networks.
 
 The source code for this example can be found in the [examples/sr](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/sr/) package.
 

examples/pom.xml

@@ -11,7 +11,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <djl.version>0.28.0-SNAPSHOT</djl.version>
-        <exec.mainClass>ai.djl.examples.inference.ObjectDetection</exec.mainClass>
+        <exec.mainClass>ai.djl.examples.inference.cv.ObjectDetection</exec.mainClass>
     </properties>
 
     <repositories>

examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java → examples/src/main/java/ai/djl/examples/inference/cv/ActionRecognition.java

@@ -10,9 +10,8 @@
  * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
  * and limitations under the License.
  */
-package ai.djl.examples.inference;
+package ai.djl.examples.inference.cv;
 
-import ai.djl.Application;
 import ai.djl.ModelException;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.Classifications;
@@ -44,28 +43,27 @@ public final class ActionRecognition {
     private ActionRecognition() {}
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        Classifications classification = ActionRecognition.predict();
+        Classifications classification = predict();
         logger.info("{}", classification);
     }
 
     public static Classifications predict() throws IOException, ModelException, TranslateException {
         Path imageFile = Paths.get("src/test/resources/action_discus_throw.png");
         Image img = ImageFactory.getInstance().fromFile(imageFile);
 
+        // Use DJL MXNet model zoo model
         Criteria<Image, Classifications> criteria =
                 Criteria.builder()
-                        .optApplication(Application.CV.ACTION_RECOGNITION)
                         .setTypes(Image.class, Classifications.class)
-                        .optFilter("backbone", "inceptionv3")
-                        .optFilter("dataset", "ucf101")
+                        .optModelUrls(
+                                "djl://ai.djl.mxnet/action_recognition/0.0.1/inceptionv3_ucf101")
                         .optEngine("MXNet")
                         .optProgress(new ProgressBar())
                         .build();
 
-        try (ZooModel<Image, Classifications> inception = criteria.loadModel()) {
-            try (Predictor<Image, Classifications> action = inception.newPredictor()) {
-                return action.predict(img);
-            }
+        try (ZooModel<Image, Classifications> inception = criteria.loadModel();
+                Predictor<Image, Classifications> action = inception.newPredictor()) {
+            return action.predict(img);
         }
     }
 }

examples/src/main/java/ai/djl/examples/inference/BigGAN.java → examples/src/main/java/ai/djl/examples/inference/cv/BigGAN.java

@@ -10,7 +10,7 @@
  * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
  * and limitations under the License.
  */
-package ai.djl.examples.inference;
+package ai.djl.examples.inference.cv;
 
 import ai.djl.Application;
 import ai.djl.ModelException;

examples/src/main/java/ai/djl/examples/inference/ImageClassification.java → examples/src/main/java/ai/djl/examples/inference/cv/ImageClassification.java

@@ -10,7 +10,7 @@
  * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
  * and limitations under the License.
  */
-package ai.djl.examples.inference;
+package ai.djl.examples.inference.cv;
 
 import ai.djl.Model;
 import ai.djl.ModelException;
@@ -48,7 +48,7 @@ public final class ImageClassification {
     private ImageClassification() {}
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        Classifications classifications = ImageClassification.predict();
+        Classifications classifications = predict();
         logger.info("{}", classifications);
     }
 

examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java → examples/src/main/java/ai/djl/examples/inference/cv/InstanceSegmentation.java

@@ -10,9 +10,8 @@
  * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
  * and limitations under the License.
  */
-package ai.djl.examples.inference;
+package ai.djl.examples.inference.cv;
 
-import ai.djl.Application;
 import ai.djl.ModelException;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
@@ -45,7 +44,7 @@ public final class InstanceSegmentation {
     private InstanceSegmentation() {}
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        DetectedObjects detection = InstanceSegmentation.predict();
+        DetectedObjects detection = predict();
         logger.info("{}", detection);
     }
 
@@ -55,20 +54,18 @@ public static DetectedObjects predict() throws IOException, ModelException, Tran
 
         Criteria<Image, DetectedObjects> criteria =
                 Criteria.builder()
-                        .optApplication(Application.CV.INSTANCE_SEGMENTATION)
                         .setTypes(Image.class, DetectedObjects.class)
-                        .optFilter("backbone", "resnet18")
-                        .optFilter("flavor", "v1b")
-                        .optFilter("dataset", "coco")
+                        .optModelUrls(
+                                "djl://ai.djl.mxnet/mask_rcnn/0.0.1/mask_rcnn_resnet18_v1b_coco")
+                        .optEngine("MXNet")
                         .optProgress(new ProgressBar())
                         .build();
 
-        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel()) {
-            try (Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
-                DetectedObjects detection = predictor.predict(img);
-                saveBoundingBoxImage(img, detection);
-                return detection;
-            }
+        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
+                Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+            DetectedObjects detection = predictor.predict(img);
+            saveBoundingBoxImage(img, detection);
+            return detection;
         }
     }
 

examples/src/main/java/ai/djl/examples/inference/MaskDetection.java → examples/src/main/java/ai/djl/examples/inference/cv/MaskDetection.java

@@ -10,7 +10,7 @@
  * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
  * and limitations under the License.
  */
-package ai.djl.examples.inference;
+package ai.djl.examples.inference.cv;
 
 import ai.djl.ModelException;
 import ai.djl.inference.Predictor;

examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java → examples/src/main/java/ai/djl/examples/inference/cv/ObjectDetection.java

@@ -10,7 +10,7 @@
  * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
  * and limitations under the License.
  */
-package ai.djl.examples.inference;
+package ai.djl.examples.inference.cv;
 
 import ai.djl.Application;
 import ai.djl.ModelException;
@@ -46,7 +46,7 @@ public final class ObjectDetection {
     private ObjectDetection() {}
 
     public static void main(String[] args) throws IOException, ModelException, TranslateException {
-        DetectedObjects detection = ObjectDetection.predict();
+        DetectedObjects detection = predict();
         logger.info("{}", detection);
     }
 
@@ -70,12 +70,11 @@ public static DetectedObjects predict() throws IOException, ModelException, Tran
                         .optProgress(new ProgressBar())
                         .build();
 
-        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel()) {
-            try (Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
-                DetectedObjects detection = predictor.predict(img);
-                saveBoundingBoxImage(img, detection);
-                return detection;
-            }
+        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
+                Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+            DetectedObjects detection = predictor.predict(img);
+            saveBoundingBoxImage(img, detection);
+            return detection;
         }
     }