CNN模型对比研究：构建模型时选择weights=None还是weights='imagenet'更利于公平对比？

Great question—this is a critical point when designing fair comparative studies of CNN architectures! Let’s break down which option makes sense for your use case:

优先选择weights=None（随机初始化）用于架构对比研究 #

If your goal is to compare the inherent performance of different CNN architectures (e.g., VGG16 vs. ResNet50 vs. MobileNet), weights=None is the only fair choice. Here’s why:

• When you use weights='imagenet', you’re loading pre-trained features that the model learned on the massive ImageNet dataset. These pre-trained weights give the model a huge head start, which can mask the actual strengths/weaknesses of the architecture itself. For example, a theoretically less efficient model might outperform a more modern architecture just because its pre-trained weights happen to align better with your target dataset.
• With random initialization (weights=None), every model you’re comparing starts from the same blank slate. All performance differences you observe will stem directly from the architecture’s design (like convolution block arrangements, residual connections, etc.) rather than pre-existing knowledge from another dataset.

When to use weights='imagenet'? #

Save pre-trained weights for transfer learning tasks, not pure architecture comparisons. If your goal is to see how well different pre-trained models adapt to your specific dataset (e.g., fine-tuning for a custom image classification task), then weights='imagenet' is perfect—it speeds up convergence and often boosts final performance. But this isn’t a fair apples-to-apples comparison of the architectures themselves.

Example Code Clarification #

For your comparative study, stick with random initialization:

# Correct setup for architecture comparison
base_model = VGG16(input_shape=(224, 224, 3), include_top=False, weights=None)

Reserve this for transfer learning scenarios only:

# For transfer learning, not architecture comparison
base_model = VGG16(input_shape=(224, 224, 3), include_top=False, weights='imagenet')

Quick Pro Tip #

If you go with weights=None, make sure all training hyperparameters are identical across every model you test (learning rate, batch size, number of epochs, optimizer type, etc.). Even small differences in training setup can skew your comparison results, so consistency is key!

内容的提问来源于stack exchange，提问作者Coconut-Then