YOLOv8 Red & Green Object Detection

📌 Project Overview

This project uses YOLOv8 to detect and classify two object classes:

• 🟩 greenbox
• 🟥 redbox

⚙️ Training Configuration

After running

yolo detect train model=yolov8n.pt data=data.yaml imgsz=320 epochs=10 batch=16 device=0

📁 Important Output Files

Inside the training folder (runs/detect/train/), the most important files are:

• results.png ✅ (main training graph)
• confusion_matrix.png ✅
• PR_curve.png ✅
• F1_curve.png ✅
• weights/best.pt ✅

📊 results.png (Main Graph)

Shows:

• Training & validation loss
• Precision and recall
• mAP50 and mAP50-95

✔ What to check:

• Loss decreases over time → model is learning
• Validation follows training → no overfitting
• mAP increases → performance improves

Throught the Graph we can analyze

1. TRAINING LOSS ANALYSIS

🔹 train/box_loss

• Decreases from ~0.75 → ~0.53

• Smooth and consistent

  Interpretation:

• Model is improving bounding box localization

  🔹 train/cls_loss

• Drops rapidly from ~1.4 → ~0.28
  Interpretation:
• Model quickly learns to classify redbox vs greenbox
• Task is relatively easy (distinct colors)

🔹 train/dfl_loss

• Gradual decrease

Interpretation:

• Model is refining bounding box precision

2. Validation Loss Analysis

🔹 val/box_loss

• Smooth decrease

Interpretation:

• Good generalization to unseen validation data

🔹 val/cls_loss

• Spike at early epoch (~3)
• Then decreases steadily

Interpretation:

• Early training instability (normal)
• Model stabilizes afterward

🔹 val/dfl_loss

• Smooth decreasing trend

Interpretation:

• Bounding box quality improves on validation data

🚨 Key Concept: Overfitting Check

Conclusion:

• No overfitting observed
• Model generalizes well

3. Precision Analysis

• Starts ~0.94
• Drops briefly
• Converges to ~1.0

Interpretation:

• Very few false positives
• Temporary fluctuation is normal

4. Recall Analysis

• Similar behavior to precision
• Ends near 1.0

Interpretation:

• Model detects almost all objects
• Very few missed detections

📊 5. mAP Analysis (Most Important Metric)

🔹 mAP50

• Final value ≈ 0.995

Interpretation:

• Nearly perfect detection at IoU = 0.5

🔹 mAP50-95

• Improves from ~0.81 → ~0.93

Interpretation:

• Strong performance under stricter evaluation
• Indicates robust bounding box quality

⚠️ 6. Early Epoch Instability

Observed at epoch ~3:

• Drop in precision, recall, and mAP

Reason:

• Random weight initialization
• Learning adjustment phase

Important:

• This is normal behavior in training
• Focus on overall trend, not individual fluctuations

📉 7. Trend vs Raw Values

• Blue line: actual values
• Orange line: smoothed trend

Interpretation:

• Smoothed curve shows true learning behavior
• Model trend is stable and improving

📉 confusion_matrix.png

Shows classification performance:

What to check:

• Strong diagonal → good model
• Off-diagonal → classification errors

Below is the confusing matrix we get after the training

✅ Correct Predictions (Diagonal)

• greenbox → greenbox = 573
• redbox → redbox = 427

🎯 Interpretation:

• Model correctly classifies almost all objects
• Very strong diagonal → excellent performance

❌ Errors (Off-Diagonal)

🔹 False Positives (FP)

• 1 background detected as redbox

👉 Meaning:

• Model detected an object where there is none

🔹 False Negatives (FN)

• 1 greenbox missed (predicted as background)
• 1 redbox missed (predicted as background)

👉 Meaning:

• Model failed to detect some objects

📊 Error Summary

👉 Total errors = 3 (very low)

📈 Performance Interpretation

✅ Strengths

• Nearly perfect classification between red and green
• Almost no confusion between classes
• Extremely high accuracy

📈 PR_curve.png (Precision–Recall Curve)

Shows tradeoff between precision and recall.

What to check:

• Curve near top-right → excellent model
• Large area under curve → high performance

✅ 1. Precision–Recall (PR) Curve

Purpose:

• Shows overall model performance
• Combines:
  • Precision (accuracy)
  • Recall (completeness)

Why important:

• Used to compute mAP (main YOLO metric)
• Best indicator of model quality

Result:

• Curve near top-right
• mAP ≈ 0.995

👉 Conclusion: Excellent model

📈 2. Precision–Confidence Curve (P Curve)

Purpose:

• Shows how precision changes with confidence threshold

Interpretation:

• Higher confidence → fewer false positives
• Model becomes more strict

Result:

• Precision quickly reaches ~1.0
• Very stable

👉 Conclusion: Predictions are highly accurate

📉 3. Recall–Confidence Curve (R Curve)

Purpose:

• Shows how many objects are detected as threshold changes

Interpretation:

• Higher confidence → more missed detections

Result:

• Recall ≈ 1.0 at low confidence
• Drops sharply after ~0.9

👉 Conclusion: High confidence may miss objects

📊 F1_curve.png

Shows best balance between precision and recall.

What to check:

• Peak value → best performance
• Confidence at peak → optimal threshold

👉 It represents the best balance between Precision and Recall

🔹 Shape:

• F1 is very high (~1.0) across a wide range
• Drops sharply after ~0.9 confidence

🔹 Key point:

• Best F1 ≈ 1.00 at confidence ≈ 0.799

✅ 1. Excellent Performance

• F1 ≈ 1.0 → almost perfect balance
• Means:
  • Very few false positives
  • Very few missed detections

👉 Model is extremely strong

⚖️ 2. Optimal Threshold

• Best confidence ≈ 0.8

👉 This is the ideal operating point

At this point:

• Precision is high
• Recall is high
• Overall performance is maximized

🧠 weights/best.pt

The final trained model.

Use it for inference:

yolo detect predict model=weights/best.pt source=your_image.jpg conf=0.8

1. Detection Output

For example:

 yolo detect predict model=best.pt source=test/images/greenbox_0_jpg.rf.b7606581a957e3d3d3b8a36e5a4d82cb.jpg conf=0.8

Model detected:

• 1 object
• Class = greenbox
• Inference time = 12.8 ms