From the help of @treo and various others in this community, I’ve been able to get my CNN up and running very quickly! I was just wondering if there’s anything I can change to increase the accuracy of the model. I’m currently running a setup on the following data:
180 classes/labels/birds
~25,000 training images (224x224, 3 channels)
900 testing images
900 validation images
I’m using a CNN with the following characteristics:
Early Stopping Trainer terminating after 50 epochs or 60 mins, evaluating on validation set every 2 epochs
With this setup, I’m currently getting an average of approximately 45-55% accuracy across the testing set after producing the model from the earlyStoppingTrainer. It might also be important to note that I’m running this network using CUDA 10.2 with a GTX 1080.
I’ve implemented an architecture similar to the one from the CIFAR animal classification example with much better results now, thank you for your help!
There’s a resnet model in the model zoo. Am I right to say we can use that pretrained model and use transfer learning to get better scores? Unfortunately there isn’t an example that I know of.
I recommend using DenseNet example for image classification :
I had very good results with that arhitecture. Also please check my latest PR on that example:
In fixed some mistakes that I made initially when created that example.
Also i think that for your dataset this model might be overwhelming, so I recommend to start with only 2 or 3 dense blocks (In example it has 4 dense bloks), and with growRate like 16…
If you want to read more about DenseNet please check this: