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deepjavalibrary · Mar 15, 2023 · 426b056 · 426b056
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diff --git a/examples/docs/mask_detection.md b/examples/docs/mask_detection.md
@@ -20,22 +20,22 @@ To configure your development environment, follow [setup](../../docs/development
 ## Run mask detection example
 
 ### Input image file
-You can find the image used in this example in the project test resource folder: `src/test/resources/maksssksksss627.png`.
+We use the following image as input:
 
-![dogs](../src/test/resources/face_mask.png)
+![mask](https://resources.djl.ai/images/face_mask_detection/face_mask.png)
 
 ### Build the project and run
 Use the following command to run the project:
 
 ```
 cd examples
-./gradlew run -Dai.djl.default_engine=PyTorch -Dmain=ai.djl.examples.inference.MaskDetectionOnnx
+./gradlew run -Dmain=ai.djl.examples.inference.MaskDetection
 ```
 
 Your output should look like the following:
 
 ```text
-[INFO ] - Detected objects image has been saved in: /Users/fenkexin/Desktop/djl/examples/build/output/mask-wearing.png
+[INFO ] - Detected objects image has been saved in: build/output/face_mask_result.png
 [INFO ] - {
 	"w/o mask": 0.8998132944107056,
 	"w/ mask": 0.8930246829986572,
@@ -46,4 +46,4 @@ Your output should look like the following:
 
 An output image with bounding box will be saved as `build/output/detected-mask-wearing.png`:
 
-![detected-dogs](../src/test/resources/face_mask_result.png)
+![detected-result](https://resources.djl.ai/images/face_mask_detection/face_mask_result.png)
diff --git a/examples/docs/object_detection.md b/examples/docs/object_detection.md
@@ -45,9 +45,11 @@ An output image with bounding box will be saved as build/output/detected-dog_bik
 ![detected-dogs](img/detected-dog_bike_car.png)
 
 ## Run object detection example with other engines
-For objection detection application, other than the default model zoo with the default engine, we can also run it with other engines and model zoo. Here, we demonstrate with a pre-trained *YOLOV5s ONNX* model.
+For objection detection application, other than the default model zoo with the default engine,
+we can also run it with other engines and model zoo. Here, we demonstrate with a pre-trained *YOLOV5s ONNX* model.
 
-First, the following line needs to be added into the dependencies in `build.gradle`. Otherwise, the ONNX model zoo won't be found.
+First, the following line needs to be added into the dependencies in `build.gradle`. Otherwise,
+the ONNX model zoo won't be found.
 ```gradle
     runtimeOnly project(":engines:onnxruntime:onnxruntime-engine")
 ```
@@ -62,4 +64,4 @@ Then use the following criteria
                     .optProgress(new ProgressBar())
                     .build();
 ```
-where the `optFilter` is removed and `optEngine` is specified. The rest would be the same. 
+where the `optFilter` is removed and `optEngine` is specified. The rest would be the same. 
diff --git a/examples/src/main/java/ai/djl/examples/inference/MaskDetection.java b/examples/src/main/java/ai/djl/examples/inference/MaskDetection.java
@@ -12,7 +12,6 @@
  */
 package ai.djl.examples.inference;
 
-import ai.djl.Application;
 import ai.djl.ModelException;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
@@ -31,8 +30,6 @@
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.nio.file.Paths;
-import java.util.Map;
-import java.util.concurrent.ConcurrentHashMap;
 
 /**
  * An example of inference using an object detection model.
@@ -53,27 +50,23 @@ public static void main(String[] args) throws IOException, ModelException, Trans
     }
 
     public static DetectedObjects predict() throws IOException, ModelException, TranslateException {
-        Path imageFile = Paths.get("src/test/resources/maksssksksss627.png");
-        Image img = ImageFactory.getInstance().fromFile(imageFile);
-
-        String modelPath = "src/test/resources/mask.onnx";
-        Map<String, Object> arguments = new ConcurrentHashMap<>();
-        arguments.put("translatorFactory", YoloV5TranslatorFactory.class.getName());
+        String imageUrl = "https://resources.djl.ai/images/face_mask_detection/face_mask.png";
+        Image img = ImageFactory.getInstance().fromUrl(imageUrl);
 
+        String modelUrl = "https://resources.djl.ai/demo/onnxruntime/face_mask_detection.zip";
         Criteria<Image, DetectedObjects> criteria =
                 Criteria.builder()
-                        .optApplication(Application.CV.OBJECT_DETECTION)
                         .setTypes(Image.class, DetectedObjects.class)
-                        .optModelUrls(modelPath)
+                        .optModelUrls(modelUrl)
                         .optEngine("OnnxRuntime")
-                        .optArguments(arguments)
+                        .optTranslatorFactory(new YoloV5TranslatorFactory())
                         .optProgress(new ProgressBar())
                         .build();
 
         try (ZooModel<Image, DetectedObjects> model = criteria.loadModel()) {
             try (Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
                 DetectedObjects detection = predictor.predict(img);
-                String outputDir = "/Users/fenkexin/Desktop/djl/examples/build/output";
+                String outputDir = "build/output";
                 saveBoundingBoxImage(img, detection, outputDir);
                 return detection;
             }
@@ -87,7 +80,7 @@ private static void saveBoundingBoxImage(Image img, DetectedObjects detection, S
 
         img.drawBoundingBoxes(detection);
 
-        Path imagePath = outputPath.resolve("mask-wearing.png");
+        Path imagePath = outputPath.resolve("face_mask_result.png");
         // OpenJDK can't save jpg with alpha channel
         img.save(Files.newOutputStream(imagePath), "png");
         logger.info("Detected objects image has been saved in: {}", imagePath);