基于PyTorch的EfficientNet-B3汉字灰度图像分类模型验证准确率停滞问题求助

Hey Marco, let's break down why your validation accuracy is plateauing at ~65% and walk through actionable fixes tailored to your Kanji classification setup:

1. Fix Data Augmentation for Kanji-Specific Features #

Your custom augmentation might not be optimized for grayscale Kanji, which rely on precise stroke structures. Try these targeted adjustments:

• Safe geometric transforms: Add small rotations (±5° max), horizontal/vertical translations (±5 pixels), and minor scaling (0.9–1.1x) to mimic real-world scan variations. Avoid horizontal flips—they can turn valid Kanji into unrecognizable characters.
• Noise & occlusion: Include low-intensity Gaussian noise (σ=0.02) and RandomErasing (scale=0.02–0.1) to simulate ink smudges or partial scans.
• Grayscale-specific tweaks: Try random brightness/contrast adjustments (±10%) to account for different scan qualities.

2. Tune ArcFace Hyperparameters for Large Class Counts #

With 3036 Kanji classes, your current ArcFace settings might be too aggressive:

• Reduce margin: Drop margin from 0.5 to 0.3–0.4. Larger margins work better for small class counts but can make convergence harder when you have thousands of classes.
• Adjust scale: Lower s from 64 to 32–48. The scale parameter amplifies logits, but too high a value can lead to unstable training with large class sets.
• Double-check implementation: Your ArcFace code looks correct, but confirm you’re not filtering out valid labels (your index = torch.where(labels != -1)[0] is fine as long as your dataset doesn’t use -1 for valid samples).

3. Align Model Setup with Your Goals #

I noticed a discrepancy: you mentioned using EfficientNet-B3, but your code uses B0. If you intended B3:

• Match input size: B3 is designed for 300x300 inputs, not 128x128. Either resize your images to 224x224/300x300 or adjust the first convolution layer’s stride to preserve feature resolution.
• Improve pre-trained weight transfer: Your current approach of averaging 3-channel weights to 1-channel is solid, but you could also initialize the first conv layer randomly and let it fine-tune alongside the backbone for better grayscale feature extraction.
• Increase embedding dimension: Bump your embedding size from 512 to 1024. More dimensions give the model more capacity to distinguish between thousands of similar Kanji characters.

4. Refine Training Strategy #

Your training loop has a few areas that can be adjusted to break the plateau:

• Delay backbone unfreezing: Wait 5–10 epochs (instead of 3) to fully train the classifier head, norm linear layer, and ArcFace loss before unfreezing the EfficientNet backbone. This lets the head learn to map features to your Kanji classes first.
• Adjust learning rates: When unfreezing, set the backbone’s LR to 1e-4 (not 1e-5)—this gives it enough flexibility to adapt to your dataset. Keep the classifier and norm linear layers at 1e-3 for faster updates.
• Fix OneCycleLR setup: After unfreezing, your OneCycleLR should run for the remaining epochs (not the full original count) to properly schedule the LR decay.
• Exclude bias/batch norm from weight decay: Modify your AdamW optimizer to skip weight decay on bias parameters and batch norm layers—these don’t benefit from it and can stabilize training:

  optimizer = torch.optim.AdamW([
      {"params": model.model.features.parameters(), "lr": 1e-4, "weight_decay": 0 if param.ndim == 1 else 1e-4},
      {"params": model.model.classifier.parameters(), "lr": 1e-3},
      {"params": norm_linear.parameters(), "lr": 1e-3}
  ], weight_decay=1e-4)
  

• Unify label smoothing: You mentioned using 0.1 label smoothing, but your code uses 0.05. Pick one (0.1 is better for large class counts) and keep it consistent.

5. Fix Validation Pipeline Critical Issues #

Your validation code is missing two key steps that could be skewing results:

• Switch to eval mode: Always set model.eval() before validation to disable batch norm updates and dropout.
• Disable gradient computation: Wrap validation logic in with torch.no_grad(): to save memory and speed up inference. Here’s the corrected snippet:

  model.eval()
  val_loss = 0.0
  val_top1_acc = 0.0
  val_top5_acc = 0.0
  val_total_batches = 0
  with torch.no_grad(), torch.amp.autocast('cuda'):
      for images, labels in val_loader:
          images = images.to(device, non_blocking=True)
          labels = labels.to(device, non_blocking=True)
          embeddings = model(images)
          logits = norm_linear(embeddings) * arcface.s
          val_loss_batch = criterion(logits, labels)
          val_loss += val_loss_batch.item()
          acc1, acc5 = accuracy(logits, labels, topk=(1, 5))
          val_top1_acc += acc1.item()
          val_top5_acc += acc5.item()
          val_total_batches += 1
  # Calculate averages after loop
  val_loss /= val_total_batches
  val_top1_acc /= val_total_batches
  val_top5_acc /= val_total_batches
  

6. Monitor Training Metrics Closely #

Track both training/validation loss and accuracy to spot overfitting:

• If training loss keeps dropping but validation loss plateaus, you’re overfitting—add more augmentation or dropout (EfficientNet has dropout, but you can increase the rate slightly).
• If both losses plateau, your model hasn’t learned enough—try extending training epochs or increasing the learning rate.

内容的提问来源于stack exchange，提问作者Marco