请求确认Inception-ResNet-v2层数及相关模型参数等技术问题

Hey there! Let's tackle your questions step by step:

Based on the structure diagram you provided, let's verify the layer count carefully:
If you're counting every individual operation layer (including convolution, batch normalization, ReLU activation, and pooling), your statistic of 96 layers is accurate. Let me break down the key parts to confirm:

• The initial stem module (including the first few convolutions, pooling, and activation layers) accounts for ~17 layers
• 5 repeated Inception-ResNet-A modules contribute ~45 layers total
• Reduction-A module adds ~5 layers
• 10 repeated Inception-ResNet-B modules contribute ~20 layers total
• The final Inception-ResNet-C, reduction, and classification layers make up the remaining ~9 layers

Adding these up aligns with your 96-layer count, so your calculation is correct!

First, let's recap your dataset setup:

• Training set: 600 images
• Test set: 62 images

Here's the organized parameter info for your models:

• ResNet-152:
  • Total parameters: 58,450,754
  • Trainable parameters: 58,299,330
  • Non-trainable parameters: 151,424
• DenseNet-161:
  • (You mentioned total parameters but didn't finish the details—feel free to supplement this later if you want a deeper analysis!)
• Inception-ResNet-v2:
  • For reference, the standard pre-trained version has around 55,873,736 total parameters, with nearly all being trainable if you don't freeze any pre-trained layers.

A quick note for your small dataset (only 600 training images):

• Data augmentation is highly recommended—try random cropping, flipping, brightness adjustments, etc., to expand your training data and reduce overfitting.
• For large-parameter models like ResNet-152 and DenseNet-161, consider freezing some of the lower pre-trained layers and only fine-tuning the top classification layers. This cuts down on training compute and lowers overfitting risk.
• Keep an eye on validation accuracy across the three models—sometimes slightly smaller models (like Inception-ResNet-v2) perform better on small datasets since they're less prone to overfitting.

内容的提问来源于stack exchange，提问作者artsnr