Implement Faster RCNN from scratch on COCO Json dataset without Faster RCNN library using PyTorch. You...

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Implement Faster RCNN from scratch on COCO Json dataset without Faster RCNN library using PyTorch. You can use any dataset or can generated geometric shape dataset. But Annotation should be in COCO Json format. Dataset must have multiple class. There will be separate folder and file for train and test image and COCO Json annotation. Use proper comment. You must apply the given criteria: LoadDataset Class FeatureExtractor Class(Res Net50) Region Proposal Network Class Region of Interest (ROI) pooling Class Classifier head Class Regressor head class Faster R-CNN model class Generate rois Training (shows epoch and corresponding loss in every iteration) Testing (shows accuracy, loss and print predicted bounding box and level over the test image) Plot Loss VS accuracy curve Print evaluation matrix 100 200- 300 400- 500 0 100 200 300 400 Fig: Test Sample But you also need to 500 Implement Faster RCNN from scratch on COCO Json dataset without Faster RCNN library using PyTorch. You can use any dataset or can generated geometric shape dataset. But Annotation should be in COCO Json format. Dataset must have multiple class. There will be separate folder and file for train and test image and COCO Json annotation. Use proper comment. You must apply the given criteria: LoadDataset Class FeatureExtractor Class(Res Net50) Region Proposal Network Class Region of Interest (ROI) pooling Class Classifier head Class Regressor head class Faster R-CNN model class Generate rois Training (shows epoch and corresponding loss in every iteration) Testing (shows accuracy, loss and print predicted bounding box and level over the test image) Plot Loss VS accuracy curve Print evaluation matrix 100 200- 300 400- 500 0 100 200 300 400 Fig: Test Sample But you also need to 500

Answer rating: 100% (QA)

It seems like youre tasked with implementing a Faster RCNN object detection model from scratch in PyTorch using a COCOformat dataset The requirements are quite detailed and cover a wide range of compo... View the full answer