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TK Lin
TK Lin

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⛏️ Hard_Negative_Mining

#ai #tutorial #python #machinelearning

Hard Negative Mining: The Secret Weapon for Learning from AI Mistakes

Washin Village AI Director Tech Notes #3

🎯 What is Hard Negative Mining?

Your AI model reached 80% accuracy, but what about that remaining 20% of errors?

Hard Negative Mining specifically identifies these "troublesome mistakes" and trains AI to address them directly.

🔍 Why Do We Need It?

In Washin Village's animal recognition, we discovered:

Error Type Example Cause
Similar appearance Ariel ↔ Cruella Both are tabby cats
Posture difference Standing ↔ Lying Same cat, different pose
Occlusion issues Half a cat Blocked by objects

These "Hard Negatives" are the toughest cases to identify and the key to improving your model!

💻 Implementation Steps

Step 1: Find Errors 

def find_hard_negatives(model, dataset):
    hard_negatives = []

    for image, true_label in dataset:
        prediction = model.predict(image)

        if prediction != true_label:
            hard_negatives.append({
                'image': image,
                'true_label': true_label,
                'predicted': prediction,
                'confidence': model.confidence
            })

    return hard_negatives
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Step 2: Analyze Error Patterns 

# Build confusion matrix
confusion = {}
for hn in hard_negatives:
    key = f"{hn['true_label']} → {hn['predicted']}"
    confusion[key] = confusion.get(key, 0) + 1

# Sort to find most common errors
sorted_confusion = sorted(confusion.items(), key=lambda x: -x[1])
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Step 3: Enhanced Training

For high-error categories, we can:

  1. Add more samples: Collect more photos of that category
  2. Data augmentation: Apply more transformations to these samples
  3. Weight adjustment: Increase weight in the loss function

📊 Real-World Case

Problems We Found

Found 467 errors (19%) in 2,451 test images.

Most Common Confusions:

Actual Predicted Count Solution
Ariel Ace 23 Add more Ariel feature photos
Kirin Human 15 Remove background human interference
BlackCatGroup CatGroup 12 Subdivide black cat category

Results After Fixing

Metric Before After
Top-1 Accuracy 79.5% 83.2%
Confusion Errors 467 312
Improvement - +3.7%

🔄 Continuous Improvement Cycle 

Train Model → Find Errors → Analyze Causes → Fix Data → Retrain
     ↑                                                     |
     └─────────────────────────────────────────────────────┘
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This cycle can be repeated continuously, improving accuracy each time.

💡 Practical Tips

  1. Run regularly: Re-scan for errors periodically
  2. Human review: AI finds errors, humans confirm fixes
  3. Track history: Record which errors have been fixed
  4. Prioritize: Fix high-frequency errors first for maximum impact

🎯 Conclusion

Hard Negative Mining isn't a one-time task—it's a continuous improvement process. Using this method, we improved accuracy from 79.5% to 83.2%, and we're still improving!

Washin Village 🏡 by AI Director

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