What Are the Layer Shapes in ResNet-50?

Complete Shape Trace

Input:       (batch,  3, 224, 224)

Conv1:  7x7, s2  (batch,  64, 112, 112)  # floor((224-7+2*3)/2)+1 = 112
BN + ReLU      (batch,  64, 112, 112)
MaxPool: 3x3, s2  (batch,  64, 56, 56)  # floor((112-3+2*1)/2)+1 = 56

Layer1 (3 blocks): (batch, 256, 56, 56)  # no spatial change
Layer2 (4 blocks): (batch, 512, 28, 28)  # first block has stride=2
Layer3 (6 blocks): (batch, 1024, 14, 14)  # first block has stride=2
Layer4 (3 blocks): (batch, 2048,  7,  7)  # first block has stride=2

AdaptiveAvgPool2d: (batch, 2048,  1,  1)  # global average pooling
Flatten:      (batch, 2048)
FC:         (batch, 1000)        # classification head

Bottleneck Block (Used in ResNet-50)

Each "block" in Layer1-4 is a bottleneck with three convolutions:

# Example: one block in Layer3
# Input: (batch, 512, 28, 28)
Conv1x1: reduce channels  (batch, 256, 28, 28)  # 1x1 conv
Conv3x3: spatial processing (batch, 256, 28, 28)  # 3x3 conv
Conv1x1: expand channels   (batch, 1024, 28, 28) # 1x1 conv
+ skip connection (with 1x1 projection if needed)

PyTorch Verification

import torchvision.models as models
import torch

model = models.resnet50()
x = torch.randn(1, 3, 224, 224)

# Trace through each stage
x = model.conv1(x);  print(f"conv1: {x.shape}") # [1, 64, 112, 112]
x = model.bn1(x);   x = model.relu(x)
x = model.maxpool(x); print(f"pool:  {x.shape}") # [1, 64, 56, 56]
x = model.layer1(x);  print(f"layer1: {x.shape}") # [1, 256, 56, 56]
x = model.layer2(x);  print(f"layer2: {x.shape}") # [1, 512, 28, 28]
x = model.layer3(x);  print(f"layer3: {x.shape}") # [1, 1024, 14, 14]
x = model.layer4(x);  print(f"layer4: {x.shape}") # [1, 2048, 7, 7]
x = model.avgpool(x); print(f"avgpool:{x.shape}") # [1, 2048, 1, 1]
x = torch.flatten(x, 1)
x = model.fc(x);    print(f"fc:   {x.shape}") # [1, 1000]