Figure 2: F1-Confidence Curve Fig 3: Precision- Recall curve

5.2 Training the model using ADAM Optimizer

Case1 (b). All paramaters kept at default value and keechanging the optimizer to ADAM.
  1. lr0 -> 0.1
  2. momentum ->0.9
  3. lrf ->0.2
  4. weight_decay: 0.0005
  5. warmup_epochs: 3.0
  6. optimizer : ADAM
In the second experiment the model is trained using the ADAM optimizer. The value of the optimizer is kept at 0.01 to evaluate if the model works better than SGD optmizer. The experiment is conducted with 30 epochs on the sub –sampled tiles of xView dataset. PR AUC 0.043 at mAP@0.5 | F1 max 0.03 at 0.071 confidence is achieved on the experiment and it is concluded that the model did not show better performance. Figure 2 and Figure 3 shows the F1-Confidence curve and the Precision –Recall curve respectively. Figure 4 shows the predicted objects in the image with their percentage of depiction.