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Input: Rice Pest images with annotation files. |
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Output: Trained Rice Pest Detection (YO-CNN) Model |
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Processing Steps:
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| 1. If a set is training data set, then follow steps 2 to 4. |
| 2. pre-processing to resize the image (640 × 640) |
| 3. normalize pixel values [0, 1] |
| 4. standardize pixel values to (640 × 640) |
| 5. augment the data with different augmentation techniques |
| 6. Apply Upsampling technique to reshape the shapes of input parameters |
| 7. Model training with MODEL = YOLO v5 |
| 8. Set epoch = 0 to 100. |
| 9. Set learning rate as Lr= 0.01 use steps 13 to 14 |
| 10. Set g0 as optimizer parameter group |
| 11. for a model selection use steps 7 to 10 |
| 12. If OPTIMIZER == Adam: optimizer = Adam (g0, Lr=hyp[‘Lr0’], betas= (hyp[‘momentum’], 0.937)) else optimizer = SGD (g0, Lr=hyp[‘Lr0’], betas= (hyp[‘momentum’], nesterov=True)) |
| 13. In a batch of no of images: 456 |
| 14. update model parameter |
| 15. end of for loop of step 14 |
| 16. Training of the model parameters started |
| 17. End of training step 16 |
| 18. for testing no of images in batch: update model parameter |
| 19. end of for loop of step 18 |
