GeM Pooling

Overview

Global Average Pooling (GAP) treats all spatial locations equally, diluting strong local signals in large feature maps. Generalized Mean (GeM) pooling raises activations to power p before averaging, then takes the p-th root — higher p values emphasize peak activations (approaching max pooling at p→∞). With p=1 it's average pooling; p=3 is a common default that boosts discriminative regions. Used extensively in retrieval (image search, metric learning) and medical imaging where lesions occupy small regions.

Quick Start

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import timm

class GeM(nn.Module):
    def __init__(self, p=3.0, eps=1e-6, p_trainable=False):
        super().__init__()
        self.p = Parameter(torch.ones(1) * p) if p_trainable else p
        self.eps = eps

    def forward(self, x):
        return F.avg_pool2d(
            x.clamp(min=self.eps).pow(self.p),
            (x.size(-2), x.size(-1))
        ).pow(1.0 / self.p)

# Plug into any timm backbone
backbone = timm.create_model('seresnext50_32x4d', pretrained=True,
                              num_classes=0, global_pool='')
pool = GeM(p=3.0, p_trainable=True)
head = nn.Linear(backbone.num_features, num_classes)

# Forward
features = backbone(images)       # (B, C, H, W)
pooled = pool(features).squeeze()  # (B, C)
logits = head(pooled)              # (B, num_classes)

Workflow

1. Create backbone with global_pool='' and num_classes=0 to get raw feature maps
2. Add GeM pooling layer (fixed p=3 or learnable)
3. Add linear classification head on top
4. Train end-to-end — if p is learnable, it adapts to the task

Key Decisions

• p value: p=3 is standard; higher (5–7) for very localized signals; p=1 degrades to GAP
• Trainable p: Set p_trainable=True for the model to learn optimal pooling aggression
• eps clamping: Essential — prevents NaN from negative activations raised to fractional power
• vs MAC/SPoC: GeM generalizes both; SPoC=p=1, MAC≈p→∞

References

• SE-ResNeXt50 Full GPU Decoding