No objects detected with Yolov2 and TensorFlow Lite (Confidence scores are low)

[mustansarsaeed]

I am trying to run yolov2 with TensorFlow Lite in Android. I have integrated Yolo v2 in Android but it is not detecting any images. In order to user YoLo v2 model in Android, I have followed following steps:

1. Downloaded weights using curl https://pjreddie.com/media/files/yolov2-tiny.weights -o yolov2-tiny.weights

2. Downloaded config file using curl https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg -o yolov2-tiny.cfg

3. Downloaded labels file curl https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names -o label.txt Converted weights to tensor flow protocol buffer using flow --model yolov2-tiny.cfg --load yolov2-tiny.weights --savepb

4. Converted tensor flow buffers to tensor flow lite using tflite_convert --graph_def_file='/home/mustansar/Softwares/darkflow/built_graph/yolov2-tiny.pb' --output_file='/home/mustansar/Softwares/darkflow/built_graph/yolov2-tiny.lite' --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --input_shape=1,416,416,3 --input_array=input --output_array=output

In the end I have two files yolov2-tiny.lite and yolov2-tiny.meta. In Android I am using dependency: implementation('org.tensorflow:tensorflow-lite:0.0.0-nightly') { changing = true }

I have loaded model and processing the image as :

Detector Class:

@Override
  public List<Recognition> recognizeImage(final Bitmap bitmap) {
    convertBitmapToByteBuffer(bitmap);

    tfLite.run(imgData, outputLocations);

    return findRecognitionsUsingTensorExample();
  }

findRecognitionUsingTensorExample()

public ArrayList<Recognition> findRecognitionsUsingTensorExample() {
    float[][][] output = outputLocations[0];
//
    // Find the best detections.
    final PriorityQueue<Recognition> pq =
            new PriorityQueue<Recognition>(
                    1,
                    new Comparator<Recognition>() {
                      @Override
                      public int compare(final Recognition lhs, final Recognition rhs) {
                        // Intentionally reversed to put high confidence at the head of the queue.
                        return Float.compare(rhs.getConfidence(), lhs.getConfidence());
                      }
                    });

    for (int y = 0; y < gridHeight; ++y) {
      for (int x = 0; x < gridWidth; ++x) {
        for (int b = 0; b < NUM_BOXES_PER_BLOCK; ++b) {
          final int offset =
                  (gridWidth * (NUM_BOXES_PER_BLOCK * (NUM_CLASSES + 5))) * y
                          + (NUM_BOXES_PER_BLOCK * (NUM_CLASSES + 5)) * x
                          + (NUM_CLASSES + 5) * b;

          if(offset >= 416 || offset + 1 >= 416) continue;

          final float xPos = (x + expit(output[y][x][offset + 0])) * blockSize;
          final float yPos = (y + expit(output[y][x][offset + 1])) * blockSize;

          final float w = (float) (Math.exp(output[y][x][offset + 2]) * ANCHORS[2 * b + 0]) * blockSize;
          final float h = (float) (Math.exp(output[y][x][offset + 3]) * ANCHORS[2 * b + 1]) * blockSize;

          final RectF rect =
                  new RectF(
                          Math.max(0, xPos - w / 2),
                          Math.max(0, yPos - h / 2),
                          Math.min(INP_IMG_WIDTH - 1, xPos + w / 2),
                          Math.min(INP_IMG_HEIGHT - 1, yPos + h / 2));
          final float confidence = expit(output[y][x][offset + 4]);

          int detectedClass = -1;
          float maxClass = 0;

          final float[] classes = new float[NUM_CLASSES];
          for (int c = 0; c < NUM_CLASSES; ++c) {
            classes[c] = output[x][y][offset + 5 + c];
          }
          softmax(classes);

          for (int c = 0; c < NUM_CLASSES; ++c) {
            if (classes[c] > maxClass) {
              detectedClass = c;
              maxClass = classes[c];
            }
          }

          final float confidenceInClass = maxClass * confidence;
          if (confidenceInClass > THRESHOLD) {
            LOGGER.i(
                    "%s (%d) %f %s", LABELS[detectedClass], detectedClass, confidenceInClass, rect);
            pq.add(new Recognition("" + offset, LABELS[detectedClass], confidenceInClass, rect));
          }
        }
      }
    }
    final ArrayList<Recognition> recognitions = new ArrayList<Recognition>();
    for (int i = 0; i < Math.min(pq.size(), MAX_RESULTS); ++i) {
      recognitions.add(pq.poll());
    }

    return recognitions;
  }

From yolov2-tiny.meta, I have used configuration i.e. classes=80, threshold=0.6, image size = 416x416, labels from file and anchors from meta file. I am unable to find the missing element.

> Can anyone please guide why objects are not being detected?