This GitHub project is focused on implementing ResNet (Residual Networks) for image classification tasks. The project uses the ResNet architecture to classify images from various datasets like ImageNet. The repository includes pre-trained models, training pipelines, and fine-tuning methods to adapt ResNet to new datasets. The main focus is on leveraging skip connections to reduce vanishing gradients and train deep neural networks efficiently. The project provides a detailed explanation of each ResNet block and the improvements it offers over traditional CNN architectures.
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