- 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.
- 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.
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 |