Classification of asus_combined dataset through Transfer Learning:

Framework : fastai

Dataset : asus_combined (provided by Hochschule Bonn Rhein Sieg)

• The dataset is augmented with 16 classes of 21706 local images.
• Further it is divided into 17365 images of training data and 4341(20% of total) images of validation data.
• Sample images from the dataset and labels.
  

Model : resnet18

• The training is done on the pretrained imagenet model of resnet18 with 4 epochs.
• The model is inbuilt in the fastai architecture with a series of convolutional layers.
• The activation functions used are : ReLU for the innerlayers and Softmax for the ultimate layer.
• Fitting is done by one cycle method.

Results:

With pretrained model :

Epoch	Training loss	Validation loss	Accuracy	Time
1	0.428112	0.164152	0.944944	01:34
2	0.241105	0.063754	0.982262	01:32
3	0.137128	0.040256	0.990555	01:31
4	0.129500	0.033298	0.992398	01:31

Confusion Matrix:

Most Confused Instances:

Actual	Predicted	Instances
DECOY	S40_40_B	3
MOTOR	R20	3
DISTANCE_TUBE	DECOY	2
R20	S40_40_B	2
S40_40_B	S40_40_G	2