Hi Adrian,
I understand that you are trying to train Faster RCNN model for object detection but are getting low precision scores.
Here are the potential reasons for the low average precision of your Faster R-CNN object detector:
- Insufficient training data: Ensure that you have a diverse and representative training dataset with an adequate number of samples for each class. Make sure the dataset covers a wide range of object variations. You can explore data augmentation techniques to artificially increase the size of your training dataset, such as random scaling, rotation, cropping, or flipping.
- Inaccurate bounding box annotations: Verify that the bounding box annotations in your ground truth data are accurate and tightly enclose the objects of interest. Incorrect or imprecise annotations can lead to poor performance.
- Insufficient training or fine-tuning: Consider training the model for more epochs or adjusting the learning rate schedule to allow the model to converge better and improve performance. For example, you can try decreasing the learning rate over time, to improve training dynamics and performance
- Inappropriate network architecture or feature extraction layer: Experiment with different feature extraction networks or layers to capture more relevant features for object detection.
- Inadequate hyperparameter tuning: Tune the training options, such as the optimizer(e.g. Adam, RMSprop), mini-batch size, and overlap ranges, to find the optimal configuration for your specific dataset.
By addressing these aspects and making appropriate adjustments, you can work towards improving the average precision of your Faster R-CNN object detector.
