How can I improve my CNN model?

How can I improve my CNN model?

Techniques for performance improvement with model optimization

1. Fine tuning the model with subset data >> Dropping few data samples for some of the overly sampled data classes.
2. Class weights >> Used to train highly imbalanced (biased) database, class weights will give equal importance to all the classes during training.

How is CNN model evaluated?

ReLU and tanh activation function have been used to evaluate the CNN model. We observed that Adam with ReLU activation function and filter size of 2 × 2 have given better classification performance in comparison to other structures.

How do I build CNN in TensorFlow?

Building a CNN with TensorFlow

1. Step 1: Preprocess the images. After importing the required libraries and assets, we load the data and preprocess the images:
2. Step 2: Create placeholders.
3. Step 3: Initialize parameters.
4. Step 4: Define forward propagation.
5. Step 5: Compute cost.
6. Step 6: Combine all functions into a model.

How to improve the performance of a CNN model?

To improve CNN model performance, we can tune parameters like epochs, learning rate etc.. Number of epochs definitely affect the performance. For large number of epochs , there is improvement in performance. But need to do certain experimentation for deciding epochs, learning rate.

How are proposals used in the CNN model?

Proposal Network (RPN) as part of the layers in the CNN model to predict the possibility of objectiveness in the re- gion [13]. While they achieve significant reduce in training time and test time compared with the R-CNN methods [12] [13], the runtime is still dominated by region proposals method. 2.3. Detection without Proposals

How does R-CNN Fast Forward the whole image?

Fast R-CNN forward the whole image through the CNN at the beginning so that it is only performed once instead of many times in R-CNN [12]; Faster R-CNN performed RoI pooling and make the CNN to do the Region Proposal, which inserts the Region Figure 1. The architecture of the R-CNNs with Region Proposals [9]

Is there such a thing as a fast CNN?

More advancements based on R-CNN network occurred to deal with the expensive slow run time problem, such as Fast R-CNN [12] and Faster R-CNN [13].