I have designed this cervical cancer detection model ResNet-50 which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer. While the original Resnet had 34 layers and used 2-layer blocks, other advanced variants such as the Resnet50 made the use of 3-layer bottleneck blocks to ensure improved accuracy and lesser training time.
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I have designed this cervical cancer detection model ResNet-50 which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer. While the original Resnet had 34 layers and used 2-layer blocks, other advanced variants such as the Resnet50 made the use of 3-layer bottleneck blocks to ensure improved accuracy and lesser training time.
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Archishman2002/Cervical-Cancer-Classification
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I have designed this cervical cancer detection model ResNet-50 which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer. While the original Resnet had 34 layers and used 2-layer blocks, other advanced variants such as the Resnet50 made the use of 3-layer bottleneck blocks to ensure improved accuracy and lesser training time.
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