Training ResNet-50 on ImageNet-1K

What I Explored

I explored large-scale image classification by training ResNet-50 on ImageNet-1K with a target of at least 70% top-1 test accuracy.

This represented a significant step up from smaller datasets such as MNIST and CIFAR-10 in terms of dataset size, class diversity, model depth, computational requirements, and training complexity.

What Confused Me

As networks become deeper, simply adding layers does not guarantee better optimization.

Understanding why residual connections make very deep networks easier to train was one of the central architectural questions.

What Clicked

The key insight was that residual blocks allow the network to learn a residual transformation relative to its input rather than requiring every stack of layers to learn an entirely new representation.

Skip connections also provide shorter paths through which information and gradients can propagate.

My Mental Model Now

Instead of thinking:

Input → Learn entirely new representation

a residual block encourages:

Input → Learn useful modification → Add original information

or conceptually:

output = F(x) + x

This simple structural idea enables substantially deeper neural networks.

Implementation / Example

I worked on training ResNet-50 against the ImageNet-1K dataset with a target top-1 accuracy of at least 70%.

Repository: Training ResNet-50 on ImageNet-1K

What I Want to Explore Next

I want to investigate modern training recipes, learning-rate schedules, distributed training, mixed precision, ConvNeXt, Vision Transformers, and how modern architectures compare with ResNet.