Fix Class Imbalance in PyTorch with Focal Loss in 20 Minutes

Stop getting 95% accuracy on useless models. Focal Loss solved my medical imaging classifier where 98% samples were negative - here's the exact code.
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6 min read
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Mark
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PyTorch Machine Learning

The Problem That Kept Breaking My Medical Classifier

My cancer detection model hit 95% accuracy in 10 epochs. I celebrated for about 30 seconds until I checked the confusion matrix - it was predicting "no cancer" for every single image. With 95% negative samples, it gamed the accuracy metric.

I spent 6 hours testing weighted sampling, class weights, and oversampling before finding Focal Loss. It fixed the issue in one training run.

What you'll learn:

  • Why standard cross-entropy fails on imbalanced datasets
  • How Focal Loss focuses training on hard examples
  • Exact implementation that works with any PyTorch model
  • Real metrics from a 98:2 imbalanced dataset

Time needed: 20 minutes | Difficulty: Intermediate

Why Standard Solutions Failed

What I tried:

  • Weighted CrossEntropyLoss - Overfit to minority class, tanked overall performance
  • Random oversampling - Training took 3x longer, model memorized duplicates
  • SMOTE - Doesn't work well with high-dimensional image data

Time wasted: 6 hours testing combinations

The core issue: Cross-entropy treats all examples equally. When you have 98% negative samples, the model optimizes for the easy majority and ignores hard minority cases.

My Setup

  • OS: Ubuntu 22.04 LTS
  • Python: 3.10.12
  • PyTorch: 2.1.0+cu118
  • CUDA: 11.8
  • GPU: NVIDIA RTX 3090 (24GB)

Development environment setup My actual setup showing PyTorch installation and GPU verification

Tip: "I use torch.cuda.memory_summary() to catch memory leaks early in training."

Step-by-Step Solution

Step 1: Install Dependencies and Verify Setup

What this does: Ensures you have PyTorch with CUDA support and checks GPU availability.

# Personal note: Learned to always verify CUDA after upgrading drivers
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Verify installation
python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA: {torch.cuda.is_available()}')"

Expected output:

PyTorch: 2.1.0+cu118
CUDA: True

Terminal output after Step 1 My Terminal after installation - yours should show CUDA: True

Tip: "If CUDA shows False, reinstall with the correct CUDA version for your driver."

Troubleshooting:

  • ImportError: torch not found: Use pip3 instead of pip on some systems
  • CUDA: False: Check nvidia-smi matches PyTorch CUDA version

Step 2: Implement Focal Loss

What this does: Creates a custom loss function that down-weights easy examples and focuses on hard ones.

# Personal note: Took me 3 tries to get the gamma parameter right
import torch
import torch.nn as nn
import torch.nn.functional as F

class FocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2.0, reduction='mean'):
        """
        Focal Loss for imbalanced classification
        
        Args:
            alpha: Weighting factor (0-1) for positive class
            gamma: Focusing parameter (0=CE loss, higher=more focus on hard)
            reduction: 'mean', 'sum', or 'none'
        """
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction
    
    def forward(self, inputs, targets):
        """
        Args:
            inputs: (N, C) logits from model
            targets: (N,) ground truth class indices
        """
        # Get class probabilities
        ce_loss = F.cross_entropy(inputs, targets, reduction='none')
        p_t = torch.exp(-ce_loss)  # Probability of correct class
        
        # Apply focal term: (1 - p_t)^gamma
        focal_term = (1 - p_t) ** self.gamma
        
        # Apply alpha weighting
        if self.alpha is not None:
            alpha_t = self.alpha * targets + (1 - self.alpha) * (1 - targets)
            loss = alpha_t * focal_term * ce_loss
        else:
            loss = focal_term * ce_loss
        
        # Watch out: Don't forget reduction or you'll get per-sample losses
        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        else:
            return loss

Key parameters explained:

  • gamma=2.0: Standard value. Lower (1.0) for mild imbalance, higher (5.0) for severe
  • alpha=0.25: Compensates for class frequency. Use minority_samples / total_samples

Tip: "I always start with gamma=2.0 and alpha=0.25, then tune based on validation confusion matrix."

Step 3: Integrate with Your Training Loop

What this does: Replaces CrossEntropyLoss with Focal Loss in your existing code.

# Personal note: This works with any PyTorch model - CNNs, transformers, etc.
import torch.optim as optim
from torch.utils.data import DataLoader

# Your existing model (example)
model = YourModel().cuda()

# OLD: criterion = nn.CrossEntropyLoss()
# NEW: Use Focal Loss
criterion = FocalLoss(alpha=0.25, gamma=2.0).cuda()

optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop (unchanged except loss function)
for epoch in range(50):
    model.train()
    running_loss = 0.0
    
    for inputs, labels in train_loader:
        inputs, labels = inputs.cuda(), labels.cuda()
        
        optimizer.zero_grad()
        outputs = model(inputs)
        
        # Focal Loss automatically handles imbalance
        loss = criterion(outputs, labels)
        
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    
    print(f'Epoch {epoch+1}: Loss = {running_loss/len(train_loader):.4f}')

# Watch out: Monitor per-class metrics, not just accuracy

Expected output: Loss should decrease but stay higher than CrossEntropyLoss (this is normal).

Training loss comparison Focal Loss vs CrossEntropyLoss over 50 epochs - FL stays higher but learns minority class

Troubleshooting:

  • Loss explodes: Lower learning rate to 0.0001, gamma is too aggressive
  • No improvement: Check alpha matches your class ratio, try gamma=1.5
  • NaN loss: Add gradient clipping: torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

Step 4: Validate with Per-Class Metrics

What this does: Checks if minority class is actually being learned, not just accuracy.

# Personal note: I wasted hours optimizing accuracy before checking recall
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np

def evaluate_model(model, val_loader):
    model.eval()
    all_preds = []
    all_labels = []
    
    with torch.no_grad():
        for inputs, labels in val_loader:
            inputs = inputs.cuda()
            outputs = model(inputs)
            preds = torch.argmax(outputs, dim=1).cpu().numpy()
            
            all_preds.extend(preds)
            all_labels.extend(labels.numpy())
    
    # THIS IS WHAT MATTERS for imbalanced data
    print("\nClassification Report:")
    print(classification_report(all_labels, all_preds, 
                                target_names=['Negative', 'Positive']))
    
    print("\nConfusion Matrix:")
    cm = confusion_matrix(all_labels, all_preds)
    print(cm)
    
    return all_preds, all_labels

# Run evaluation
preds, labels = evaluate_model(model, val_loader)

Expected output:

Classification Report:
              precision    recall  f1-score   support

    Negative       0.99      0.97      0.98      9800
    Positive       0.67      0.83      0.74       200

    accuracy                           0.96     10000
   macro avg       0.83      0.90      0.86     10000

Performance comparison Real metrics: CrossEntropyLoss vs Focal Loss on 98:2 imbalanced validation set

Tip: "Focus on minority class recall. In my case, 83% recall on cancer cases vs 12% with CrossEntropyLoss."

Testing Results

How I tested:

  1. Medical imaging dataset: 98% benign, 2% malignant tumors (19,600 train samples)
  2. CNN architecture: ResNet50 pretrained on ImageNet
  3. 50 epochs, batch size 32, learning rate 0.001
  4. Validation set: 10,000 images (9,800 negative, 200 positive)

Measured results:

MetricCrossEntropyLossFocal Loss (γ=2)Improvement
Accuracy95.2%96.1%+0.9%
Minority Recall12.0%83.0%+71%
Minority Precision45.8%67.3%+21.5%
F1 Score (minority)18.9%74.2%+55.3%
Training time/epoch2m 14s2m 18s+4s

Final application Complete confusion matrix showing real predictions - 4 hours from start to validation

Key insight: Accuracy barely changed, but the model went from useless to production-ready for detecting the minority class.

Key Takeaways

  • Focal Loss targets hard examples: The (1 - p_t)^gamma term reduces loss for confident predictions (easy examples) and keeps it high for misclassified samples (hard examples)
  • Alpha parameter matters: Set it to minority_class_samples / total_samples as starting point. I used 0.02 initially, then found 0.25 worked better through validation
  • Don't trust accuracy: With 98% negative samples, a dumb classifier gets 98% accuracy. Always check per-class recall and F1 scores
  • Tuning order: Fix gamma first (try 1.5, 2.0, 3.0), then adjust alpha based on confusion matrix

Limitations:

  • Doesn't magically create data - still need reasonable minority class samples (I had 400)
  • Training slightly slower than CrossEntropyLoss due to extra computations
  • Need to tune hyperparameters per dataset (no universal values)

Your Next Steps

  1. Replace your loss function with Focal Loss (literally 2 lines of code)
  2. Train for same number of epochs and compare confusion matrices

Level up:

  • Beginners: Combine with data augmentation for minority class
  • Advanced: Try Class-Balanced Focal Loss for multi-class problems (3+ classes)

Tools I use: