timm library)Today’s goal was to understand the big picture behind EfficientNet,
with a close read of the Abstract, Introduction, and Motivation sections.
This part sets the stage for the technical deep-dive coming up next.
Convolutional Neural Networks (ConvNets) are often developed under fixed resource constraints,
which leads to inefficiencies when scaling models.
EfficientNet builds on MobileNet and ResNet families and introduces a compound scaling method
that scales depth, width, and resolution in a balanced manner.
Key takeaways from the abstract:
Traditional scaling strategies usually modify only one aspect of a model:
These approaches often require heavy manual tuning and result in sub-optimal accuracy or efficiency.
EfficientNet challenges this by asking:
“Can we find a more principled, theoretically grounded way to scale ConvNets efficiently?”
Their answer is compound scaling — scaling all three dimensions together using fixed coefficients
that were found through grid search on a base model.
The scaling equation looks like:
\[\text{depth} \propto \alpha^\phi,\quad \text{width} \propto \beta^\phi,\quad \text{resolution} \propto \gamma^\phi\]Where φ is the user-controlled scaling factor, and (α, β, γ) are constants.
Most prior models scale only one dimension at a time,
which often leads to imbalanced models and poor compute-to-accuracy trade-offs.
EfficientNet argues that:
In Figure 1 of the paper, EfficientNet models achieve higher accuracy with fewer parameters
compared to much larger networks. This highlights the effectiveness of their approach.
It was refreshing to see a paper that focuses not just on raw accuracy,
but on the efficiency–accuracy trade-off from a design perspective.
I liked that the motivation was simple:
“How do we scale a ConvNet intelligently?”
The result is not just a performant model — it’s a scalable framework.
In Day 2, I’ll explore:
Stay tuned.
📌 Note: This review is based on my own reading and summary. Some sections were refined for clarity.