How can you improve the accuracy of an object detection model?

How can you improve the accuracy of an object detection model?

6 Freebies to Help You Increase the Performance of Your Object Detection Models

  1. Visually Coherent Image Mix-up for Object Detection (+3.55% mAP Boost)
  2. Classification Head Label Smoothening (+2.16% mAP Boost)
  3. Data Pre-processing (Mixed Results)
  4. Training Scheduler Revamping (+1.44% mAP Boost)

What are different challenges which need to be solved during object recognition?

Challenges in object detection

  • Viewpoint variation. One of the biggest difficulties of object detection is that an object viewed from different angles may look completely different.
  • Deformation.
  • Occlusion.
  • Illumination conditions.
  • Cluttered or textured background.
  • Variety.
  • Speed.

What are the default metrics for object detection?

The default metrics are based on those used in Pascal VOC evaluation. To use the COCO object detection metrics add metrics_set: “coco_detection_metrics” to the eval_config message in the config file. To use the COCO instance segmentation metrics add metrics_set: “coco_mask_metrics” to the eval_config message in the config file.

How to train object detection with bounding box?

Train the model using a loss function such as mean-squared error or mean-absolute error on training data that consists of (1) the input images and (2) the bounding box of the object in the image. After training, we can present an input image to our bounding box regressor network.

How to install the TensorFlow object detection API?

TensorFlow Object Detection API Installation ¶ 1 Downloading the TensorFlow Model Garden ¶. Create a new folder under a path of your choice and name it TensorFlow. 2 Protobuf Installation/Compilation ¶. The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. 3 COCO API installation ¶.

How are object detectors used in R-CNN?

Great questions, Kyle. Basic R-CNN object detectors, such as the ones we covered on the PyImageSearch blog, rely on the concept of region proposal generators. These region proposal algorithms (e.g., Selective Search) examine an input image and then identify where a potential object could be.