损失函数异常波动——是否属于欠拟合？

Hey there! Nice to see you diving into object detection with Faster RCNN-v2—super cool that you’re experimenting with custom datasets. Let’s clear up your first question right away: that early, erratic loss fluctuation isn’t underfitting.

Underfitting typically presents as consistently high loss with no downward trend, and your model would perform poorly even on simple samples. What you’re seeing—wild loss swings after just a few steps—points to other issues, most likely tied to your custom dataset or adjusted training setup. Here are the most common culprits and how to troubleshoot them:

1. Custom Dataset Problems #

• Labeling Errors: Randomly spot-check 10-20 of your images and their annotations. Are bounding boxes perfectly aligned with objects? Are class labels accurate? Even a handful of mislabeled samples can throw off loss calculations early in training.
• Distribution Mismatch: If your custom data is drastically different from the pre-trained dataset (e.g., detecting tiny industrial parts vs. COCO’s general objects), the model’s initial weights might struggle to adapt quickly, leading to unstable loss.
• Insufficient Data Size: A small dataset (hundreds instead of thousands of samples) means each batch has high variance—your model will overreact to small batches, causing loss to jump around.

2. Training Parameter Misadjustments #

• Learning Rate Too High: This is the #1 cause of early loss instability. If you cranked up the learning rate when modifying training steps, the model’s weights are being updated too drastically with each batch. Try scaling it down (e.g., from 1e-4 to 1e-5) and see if loss stabilizes.
• Batch Size Too Small: A tiny batch size introduces massive gradient noise, since the model is learning from very few samples. If GPU memory limits you from increasing batch size, use gradient accumulation (sum gradients over multiple batches before updating weights).
• Incorrect Layer Freezing: If you unfrozen the entire pre-trained backbone with a high learning rate, you’re overwriting the useful features the model learned. Start by freezing the backbone and only training the detection head, then gradually unfreeze layers with a very low learning rate for fine-tuning.

3. Data Loading/Preprocessing Bugs #

• Mismatched Images & Labels: Double-check your data loader to ensure each image pairs with the correct annotations. A bug here could feed random labels to the model, causing unpredictable loss spikes.
• Overly Aggressive Augmentation: Heavy augmentation (e.g., extreme rotations, cropping that cuts off objects) can distort features so much the model can’t learn consistency. Try disabling augmentation temporarily to see if loss stabilizes.
• Inconsistent Preprocessing: Make sure your custom images are resized, normalized, and formatted exactly like the tutorial’s test data. Even small differences (e.g., using different pixel mean values) can throw off the model.

Quick Troubleshooting Checklist #

1. Test with a tiny learning rate and frozen backbone—train 10-20 steps to see if loss stays stable.
2. Visualize a few batches of loaded data (images + bounding boxes) to confirm everything looks correct.
3. If data is limited, try adding synthetic samples or using few-shot transfer learning techniques.

内容的提问来源于stack exchange，提问作者Roloff