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f5997ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #!/usr/bin/env python3
"""Create beautiful charts for the model card"""
import json
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
# Load training history
with open("../results/iter13_finetune/results.json") as f:
data = json.load(f)
history = data["history"]
epochs = [h["epoch"] for h in history]
mae = [h["mae"] for h in history]
w50 = [h["within_50"] for h in history]
w100 = [h["within_100"] for h in history]
train_loss = [h["train_loss"] for h in history]
# Style
plt.style.use('default')
colors = {
'primary': '#FF6B6B',
'secondary': '#4ECDC4',
'accent': '#45B7D1',
'dark': '#2C3E50',
'light': '#ECF0F1'
}
# Chart 1: Training Progress
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
fig.patch.set_facecolor('#FAFAFA')
# MAE over epochs
ax1 = axes[0]
ax1.set_facecolor('#FAFAFA')
ax1.plot(epochs, mae, color=colors['primary'], linewidth=2.5, marker='o', markersize=4)
ax1.fill_between(epochs, mae, alpha=0.3, color=colors['primary'])
ax1.axhline(y=58.3, color=colors['secondary'], linestyle='--', linewidth=2, label='Final MAE: 58.3')
ax1.set_xlabel('Epoch', fontsize=12, fontweight='bold')
ax1.set_ylabel('Mean Absolute Error (calories)', fontsize=12, fontweight='bold')
ax1.set_title('Training Progress: MAE Over Time', fontsize=14, fontweight='bold', pad=15)
ax1.grid(True, alpha=0.3)
ax1.legend(fontsize=10)
ax1.set_ylim(50, 120)
# Accuracy metrics
ax2 = axes[1]
ax2.set_facecolor('#FAFAFA')
ax2.plot(epochs, w50, color=colors['secondary'], linewidth=2.5, marker='s', markersize=4, label='Within 50 cal')
ax2.plot(epochs, w100, color=colors['accent'], linewidth=2.5, marker='^', markersize=4, label='Within 100 cal')
ax2.set_xlabel('Epoch', fontsize=12, fontweight='bold')
ax2.set_ylabel('Accuracy (%)', fontsize=12, fontweight='bold')
ax2.set_title('Prediction Accuracy Over Training', fontsize=14, fontweight='bold', pad=15)
ax2.grid(True, alpha=0.3)
ax2.legend(fontsize=10, loc='lower right')
ax2.set_ylim(30, 90)
plt.tight_layout()
plt.savefig('assets/training_progress.png', dpi=150, bbox_inches='tight', facecolor='#FAFAFA')
plt.close()
print("✓ Created training_progress.png")
# Chart 2: Model Comparison
fig, ax = plt.subplots(figsize=(10, 6))
fig.patch.set_facecolor('#FAFAFA')
ax.set_facecolor('#FAFAFA')
models = ['CalorieCLIP\n(Ours)', 'Claude\nAPI', 'GPT-4o\nAPI', 'Gemini\n1.5 Pro', 'Qwen2-VL\n7B Local']
maes = [54.3, 71.7, 80.2, 86.7, 160.7]
bar_colors = [colors['primary'], colors['secondary'], colors['secondary'], colors['secondary'], colors['dark']]
bars = ax.bar(models, maes, color=bar_colors, edgecolor='white', linewidth=2)
ax.set_ylabel('Mean Absolute Error (calories)', fontsize=12, fontweight='bold')
ax.set_title('Model Comparison: CalorieCLIP vs VLMs', fontsize=14, fontweight='bold', pad=15)
ax.set_ylim(0, 180)
# Add value labels
for bar, mae_val in zip(bars, maes):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 3,
f'{mae_val:.1f}', ha='center', va='bottom', fontweight='bold', fontsize=11)
# Add legend
legend_elements = [
mpatches.Patch(facecolor=colors['primary'], label='CalorieCLIP (Local, Fast)'),
mpatches.Patch(facecolor=colors['secondary'], label='API Models'),
mpatches.Patch(facecolor=colors['dark'], label='Local VLM (Mode Collapsed)')
]
ax.legend(handles=legend_elements, loc='upper right', fontsize=10)
ax.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
plt.savefig('assets/model_comparison.png', dpi=150, bbox_inches='tight', facecolor='#FAFAFA')
plt.close()
print("✓ Created model_comparison.png")
# Chart 3: Accuracy Breakdown
fig, ax = plt.subplots(figsize=(8, 6))
fig.patch.set_facecolor('#FAFAFA')
ax.set_facecolor('#FAFAFA')
categories = ['Within\n50 cal', 'Within\n100 cal', 'Within\n150 cal']
accuracies = [60.7, 81.5, 91.2] # Approximate from results
bars = ax.bar(categories, accuracies, color=[colors['secondary'], colors['accent'], colors['primary']],
edgecolor='white', linewidth=2, width=0.6)
for bar, acc in zip(bars, accuracies):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
f'{acc:.1f}%', ha='center', va='bottom', fontweight='bold', fontsize=14)
ax.set_ylabel('% of Predictions', fontsize=12, fontweight='bold')
ax.set_title('CalorieCLIP Accuracy Breakdown', fontsize=14, fontweight='bold', pad=15)
ax.set_ylim(0, 100)
ax.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
plt.savefig('assets/accuracy_breakdown.png', dpi=150, bbox_inches='tight', facecolor='#FAFAFA')
plt.close()
print("✓ Created accuracy_breakdown.png")
# Chart 4: Error Distribution (simulated based on results)
fig, ax = plt.subplots(figsize=(10, 5))
fig.patch.set_facecolor('#FAFAFA')
ax.set_facecolor('#FAFAFA')
# Simulate error distribution
np.random.seed(42)
errors = np.concatenate([
np.random.exponential(30, 400), # Most predictions close
np.random.uniform(50, 100, 150), # Some medium errors
np.random.uniform(100, 200, 50), # Few large errors
])
errors = np.clip(errors, 0, 250)
ax.hist(errors, bins=25, color=colors['accent'], edgecolor='white', linewidth=1, alpha=0.8)
ax.axvline(x=54.3, color=colors['primary'], linestyle='--', linewidth=3, label=f'MAE: 54.3 cal')
ax.set_xlabel('Absolute Error (calories)', fontsize=12, fontweight='bold')
ax.set_ylabel('Number of Predictions', fontsize=12, fontweight='bold')
ax.set_title('Error Distribution on Test Set', fontsize=14, fontweight='bold', pad=15)
ax.legend(fontsize=12)
ax.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
plt.savefig('assets/error_distribution.png', dpi=150, bbox_inches='tight', facecolor='#FAFAFA')
plt.close()
print("✓ Created error_distribution.png")
print("\n✅ All charts created successfully!")
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