# Unable To Get Detections With YOLOv9 DNN
**URL:** https://forum.opencv.org/t/unable-to-get-detections-with-yolov9-dnn/18319
**Category:** Uncategorized
**Created:** [July 21, 2024, 1:58am UTC](https://forum.opencv.org/t/unable-to-get-detections-with-yolov9-dnn/18319 "2024-07-21T01:58:20Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@Gregory\_Andrew](https://forum.opencv.org/u/Gregory_Andrew)
#### Post date: [July 21, 2024, 1:58am UTC](https://forum.opencv.org/t/unable-to-get-detections-with-yolov9-dnn/18319/1 "2024-07-21T01:58:20Z")
Hello, hopefully someone can help me out here. I am struggling to get detections using a YoloV9 ONNX model with the Java OpenCV DNN module. I have tried pretty much everything, including compiling different OpenCV versions. The model had high precision in training, but is unable to detect anything even with a very low confidence threshold when running it inside OpenCV. THere are no errors, simply no detections.
The model was exported in .onnx format using the `export.py` script from the official repository, with opset=12 and default options.
Here is a link to my model weights:
> **[Filebin | 9vhsnnpehv5qcmdi](https://filebin.net/9vhsnnpehv5qcmdi)**
>
> Convenient file sharing. Registration is not required. Large files are supported.
Here is the image I am trying to run detections on.
If someone could take a look at this code, it would be greatly appreciated:
```java
package com.***.opencv_dnn;
import java.net.URISyntaxException;
import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Image2BlobParams;
import org.opencv.dnn.Net;
import org.opencv.imgcodecs.Imgcodecs;
public class App {
private static final String MODEL_PATH = "src/main/resources/yolov9-3d-shapes-100-images-100-epochs.onnx";
private static final int TARGET_IMG_HEIGHT = 640;
private static final int TARGET_IMG_WIDTH = 640;
private static final float SCALE_FACTOR = 1f / 255f;
private static final int NUM_CLASSES = 45;
private static final float CONF_THRESHOLD = 0.1f;
private static final String[] CLASS_NAMES = new String[] { "2_number", "3_number", "4_number", "5_number",
"6_number", "7_number", "8_number", "9_number", "a_lower", "a_upper", "b_upper", "c_upper", "cone", "cube",
"cylinder", "d20", "d_upper", "e_lower", "e_upper", "f_upper", "g_lower", "g_upper", "h_lower", "h_upper",
"k_upper", "l_upper", "m_lower", "m_upper", "n_lower", "p_upper", "q_upper", "r_lower", "r_upper",
"s_upper", "sphere", "t_lower", "t_upper", "u_upper", "v_upper", "w_upper", "wheel", "x_lower", "y_lower",
"y_upper", "z_upper" };
public static void main(String[] args) {
loadOpenCV();
Net model = loadModel();
Mat image = getPreprocessedImage("src/test/resources/shapes2.jpeg");
model.setInput(image);
Mat output = model.forward().reshape(0, NUM_CLASSES + 4);
for (int i = 0; i < output.cols(); i++) {
Mat col = output.col(i);
Mat confidences = col.rowRange(4, NUM_CLASSES + 4);
MinMaxLocResult mm = Core.minMaxLoc(confidences);
if ((float) mm.maxVal > CONF_THRESHOLD) {
System.out.println(getClassName((int) mm.maxLoc.x));
}
}
}
private static void loadOpenCV() {
try {
String filename = App.class.getClassLoader().getResource("opencv_4100.so").toURI().toString()
.replace("file:", "");
System.load(filename);
} catch (URISyntaxException e) {
throw new RuntimeException(e);
}
}
private static Net loadModel() {
return Dnn.readNetFromONNX(MODEL_PATH);
}
private static Mat preprocess(Mat image) {
Image2BlobParams params = new Image2BlobParams();
params.set_scalefactor(new Scalar(SCALE_FACTOR));
params.set_size(new Size(TARGET_IMG_WIDTH, TARGET_IMG_HEIGHT));
params.set_swapRB(true);
return Dnn.blobFromImageWithParams(image, params);
}
private static Mat getPreprocessedImage(String path) {
Mat image = Imgcodecs.imread(path, Imgcodecs.IMREAD_COLOR);
return preprocess(image);
}
private static String getClassName(int loc) {
return CLASS_NAMES[loc];
}
}
```
---
### Author:  [@berak](https://forum.opencv.org/u/berak)
#### Post date: [July 21, 2024, 4:38am UTC](https://forum.opencv.org/t/unable-to-get-detections-with-yolov9-dnn/18319/2 "2024-07-21T04:38:39Z")
i have not used yolov9, but this looks wrong to me:
> [@Gregory\_Andrew](#):
>
> ```auto
> for (int i = 0; i < output.cols(); i++) {
> Mat col = output.col(i);
>
> ```
should’t it be:
```
for (int i = 0; i < output.rows(); i++) {
Mat col = output.row(i);
```
(think of the detection output as a 2d Matrix, where each row (horizontal line) contains a ‘box proposal’
---
### Author:  [@Gregory\_Andrew](https://forum.opencv.org/u/Gregory_Andrew)
#### Post date: [July 21, 2024, 5:01am UTC](https://forum.opencv.org/t/unable-to-get-detections-with-yolov9-dnn/18319/3 "2024-07-21T05:01:41Z")
> [@Gregory\_Andrew](#):
>
> ```auto
> Mat output = model.forward().reshape(0, NUM_CLASSES + 4);
>
> ```
I think because of the way I reshaped it using `Mat output = model.forward().reshape(0, NUM_CLASSES + 4);`, the result is a matrix with 8400 rows and 49 cols, where the rows are anchors and the cols are 4 box coordinates, plus 45 probabilities (1 for each class). I did try the same thing with rows, with no result. Will try your suggestion again and let you know if I catch something
---
### Author:  [@Gregory\_Andrew](https://forum.opencv.org/u/Gregory_Andrew)
#### Post date: [July 21, 2024, 5:33am UTC](https://forum.opencv.org/t/unable-to-get-detections-with-yolov9-dnn/18319/4 "2024-07-21T05:33:00Z")
Guys I figured out the issue! The key was in the preprocessing step. I changed the method `preprocess()` to the following:
```java
private static Mat preprocess(Mat image) {
return Dnn.blobFromImage(image, SCALE_FACTOR, new Size(TARGET_IMG_WIDTH, TARGET_IMG_HEIGHT),
new Scalar(0, 0, 0), true, false);
}
```