---
title: 'EfficientNetV2-M: Medium-Scale CNN Model'
url: https://www.emergentmind.com/topics/efficientnetv2-m
type: topic
---

# EfficientNetV2-M: Medium-Scale CNN Model

EfficientNetV2-M is a convolutional neural network model that forms part of the EfficientNetV2 family, designed for superior training speed and parameter efficiency relative to its predecessors and contemporary architectures. Developed using training-aware neural architecture search and progressive model scaling, EfficientNetV2-M leverages architectural innovations such as Fused-MBConv blocks to optimize hardware utilization and regularization strategies adapted for progressive image resizing during training. The model is positioned as a “Medium” size instance within the V2 series, balancing accuracy and computational costs for large-scale vision tasks [2104.00298].

## 1. Building-Block Primitives and Architectural Structure

EfficientNetV2-M is constructed by scaling up the EfficientNetV2-S “base” architecture via compound scaling. The base architecture is composed of two fundamental building blocks:

- **MBConv block**: Implements an expand-projection depthwise separable convolution stack (expand with $1\times1$ convolution, depthwise $k\times k$ convolution, project with $1\times1$ convolution), with optional Squeeze-and-Excitation (SE) and residual connections with drop-connect. Expansion factors are $e\in\{1,4,6\}$, kernels $k\in\{3,5\}$, and SE ratio is 0.25.
- **Fused-MBConv block**: A single regular $k\times k$ convolution that simultaneously expands feature maps and performs spatial convolution, followed by a $1\times1$ convolution and residual connection. This yields better accelerator utilization, particularly in early layers.

Both blocks use SiLU (Swish) activations, batch normalization, and drop-connect (stochastic depth).

### Base Architecture: Stage-Wise Summary

| Stage | Operator                      | Stride | Output Channels | Repeat |
|-------|-------------------------------|--------|-----------------|--------|
| 0     | Conv $3\times3$                | 2      | 24              | 1      |
| 1     | Fused-MBConv ($e=1$, $k=3$)    | 1      | 24              | 2      |
| 2     | Fused-MBConv ($e=4$, $k=3$)    | 2      | 48              | 4      |
| 3     | Fused-MBConv ($e=4$, $k=3$)    | 2      | 64              | 4      |
| 4     | MBConv ($e=4$, $k=3$, SE=0.25) | 2      | 128             | 6      |
| 5     | MBConv ($e=6$, $k=3$, SE=0.25) | 1      | 160             | 9      |
| 6     | MBConv ($e=6$, $k=3$, SE=0.25) | 2      | 256             | 15     |
| 7     | Conv $1\times1$ $\rightarrow$ Pool $\rightarrow$ FC | — | 1280 | 1 |

[Adapted from 2104.00298]

## 2. Compound Scaling and V2-M Derivation

EfficientNetV2-M is derived by applying compound scaling to the base model. Compound scaling simultaneously scales network depth $d$, width $w$, and input resolution $r$ using the relations:

$$
d = d_0 \cdot \alpha^\phi,\quad
w = w_0 \cdot \beta^\phi,\quad
r = r_0 \cdot \gamma^\phi,\quad
\text{s.t.}\;\; \alpha\cdot\beta^2\cdot\gamma^2 \approx 2,\quad \alpha,\beta,\gamma \geq 1
$$

where $\phi=1$ for V2-M and typical choices are $\alpha=1.2$, $\beta=1.1$, $\gamma=1.15$. V2-M is capped at a maximum resolution of $380\times380$. Each stage’s channel counts and block repeats are computed as scaled and rounded versions of the base.

This scaling leads to:

- Parameters: 54 million
- FLOPs: 24 billion (for $380\times 380$ input)
- Input resolution at inference: $380\times380$

## 3. Training Protocols and Regularization

The training of EfficientNetV2-M is structured around progressive learning, which schedules image resolution and regularization magnitude to increase linearly during training. The training protocol for V2-M is specifically characterized by:

- **Progressive Learning**: Four stages, each spanning approximately 87 epochs, with image sizes increasing from $128 \rightarrow 380$.
- **Regularization Scheduling**:
  - Dropout rate: Increasing linearly from 0.10 to 0.40
  - RandAugment magnitude: 5 to 20
  - Mixup $\alpha$: 0.00 to 0.20
  - Stochastic depth survival probability: fixed at 0.8

Optimization is performed with RMSProp (momentum 0.9, decay 0.9), weight decay $1\mathrm{e}{-5}$, batch normalization momentum 0.99, and batch size 4096 (TPU). The learning rate is warmed up from 0 to 0.256 over 5000 steps, followed by exponential decay by 0.97 every 2.4 epochs. An exponential moving average of weights is maintained with decay 0.9999.

## 4. Model Efficiency and Computation

EfficientNetV2-M is designed for enhanced efficiency:

- **Parameters**: 54 million
- **FLOPs**: 24B (per forward pass at inference, $380\times380$ resolution)
- **Inference Latency**: ≃57 milliseconds per image (Nvidia V100, FP16, batch 16)
- **Training Time**: 13 hours for 350 epochs on ImageNet-1k, using 32 TPUv3 cores with progressive learning

For comparison, EfficientNet-V1-B7 requires 139 hours for training under similar conditions, and V1-B6 requires 75 hours, both with lower top-1 accuracy.

## 5. Performance Metrics and Transfer Results

On the ImageNet ILSVRC2012 dataset (1.28M train, 50K val), EfficientNetV2-M achieves:

- Top-1 Accuracy: 85.1%
- Top-5 Accuracy: ≃97.3%

Transfer learning (finetuned from ImageNet-1k) yields:

| Dataset         | V2-M Top-1 (%) | V1-B7 Top-1 (%) | ViT-B/16 Top-1 (%) |
|-----------------|----------------|-----------------|--------------------|
| CIFAR-10        | 99.0 ±0.08     | 98.9            | 98.1               |
| CIFAR-100       | 92.2 ±0.08     | 91.7            | 87.1               |
| Flowers102      | 98.5 ±0.08     | 98.8            | 89.5               |
| Stanford Cars   | 94.6 ±0.10     | 94.7            | –                  |

## 6. Comparative Analysis and Trade-offs

EfficientNetV2-M demonstrates a top-1 accuracy approximately 1.0% higher than a comparably sized V1 model, while attaining a $\sim$6× speedup in training. At a fixed computational budget (24B FLOPs), V2-M yields a superior accuracy-latency balance relative to larger hybrid architectures, including ResNeSt and NFNet, as measured on standard benchmarks.

A plausible implication is that the introduction of Fused-MBConv blocks and progressive regularization scheduling are central in reconciling accelerator utilization with high generalization performance, though the architectural details and empirical metrics are strictly as reported.

## 7. Key Formulas and Block Structure

The essential architectural building blocks can be formalized as follows:

- **MBConv**:

  $$
  x_e = \mathrm{Conv}_{1\times1}(x),\quad
  x_d = \mathrm{DWConv}_{k\times k}(x_e),\quad
  y = x + \mathrm{Conv}_{1\times1}(x_d)
  $$
  (with SE and drop-connect integrated in the sequence)

- **Fused-MBConv**:

  $$
  x_f = \mathrm{Conv}_{k\times k}(x),\quad
  y = x + \mathrm{Conv}_{1\times1}(x_f)
  $$

These formulations define the core computational paths and are repeated according to stagewise configuration, with parameter counts and repeat factors set by compound scaling rules detailed above [2104.00298].

Source: https://www.emergentmind.com/topics/efficientnetv2-m