Hi everyone, I am a novice trying to learn ML and AI. I am trying to train a CNN model to classify 9000+ images with 100 labels. These images are flower patterns/leaves from what I can tell. The catch is that the actual test dataset has 101 labels and the when the model detects an image not in the original 100 labels it has to assign it to the 101st label. What would be the best way to go about doing this?
I have used resnet50 with imagenet weights and made some of the previous layers trainable to fine tune the model. I have followed it with a globalaverage layer, a 1024 node dense layer with l2 regularization, batchnorm, dropout and softmax layer as the classifer. I am using adam optimizer with a batch size of 16, learning rate of 0.0001. I then assign a threshold value of 0.6 and if the model prediction is below the threshold value it assigns it the 101st label. Currently i have a ~90% testing accuracy.
Are there any obvious things i should be doing better/changing and how can i go about optimising the threshold value or is there a better way to handle the 101st label? Should i be using resnet or something else for flower patterns and leaves given my training dataset of 9000+ images
PhysZhongli t1_jccbh4w wrote
Reply to [D] Simple Questions Thread by AutoModerator
Hi everyone, I am a novice trying to learn ML and AI. I am trying to train a CNN model to classify 9000+ images with 100 labels. These images are flower patterns/leaves from what I can tell. The catch is that the actual test dataset has 101 labels and the when the model detects an image not in the original 100 labels it has to assign it to the 101st label. What would be the best way to go about doing this?
I have used resnet50 with imagenet weights and made some of the previous layers trainable to fine tune the model. I have followed it with a globalaverage layer, a 1024 node dense layer with l2 regularization, batchnorm, dropout and softmax layer as the classifer. I am using adam optimizer with a batch size of 16, learning rate of 0.0001. I then assign a threshold value of 0.6 and if the model prediction is below the threshold value it assigns it the 101st label. Currently i have a ~90% testing accuracy.
Are there any obvious things i should be doing better/changing and how can i go about optimising the threshold value or is there a better way to handle the 101st label? Should i be using resnet or something else for flower patterns and leaves given my training dataset of 9000+ images