| import matplotlib.pyplot as plt |
| import matplotlib.patches as mpatches |
| import matplotlib.lines as mlines |
| import numpy as np |
| from adjustText import adjust_text |
|
|
| |
| data = [ |
| dict(name='Mnist\n(2015)', mae=7.29, gflops=0.10, params=1.82, group='prior'), |
| dict(name='iTracker\n(2016)', mae=7.67, gflops=3.97, params=6.28, group='prior'), |
| dict(name='GazeNet\n(2017)', mae=6.62, gflops=72.24, params=90.23, group='prior'), |
| dict(name='FullFace\n(2017)', mae=5.65, gflops=29.90, params=190.00, group='prior'), |
| dict(name='RT-Gene\n(2018)', mae=5.36, gflops=12.21, params=31.66, group='prior'), |
| dict(name='DilatedNet\n(2019)', mae=5.07, gflops=202.00, params=3.92, group='prior'), |
| dict(name='Gaze360\n(2019)', mae=4.66, gflops=3.65, params=11.72, group='recent'), |
| dict(name='FAR-Net\n(2021)', mae=5.12, gflops=0.65, params=1.94, group='recent'), |
| dict(name='FR-Net\n(2024)', mae=4.95, gflops=0.15, params=0.85, group='recent'), |
| dict(name='FGI-Net\n(2025)', mae=4.81, gflops=0.08, params=0.45, group='recent'), |
| dict(name='Heavy Teacher\n(ResNet50)', mae=4.15, gflops=4.12, params=25.56, group='teacher'), |
| dict(name='LIPE \n(Ours)', mae=4.72, gflops=0.02125, params=0.18, group='ours'), |
| ] |
|
|
| |
| plt.rcParams['font.family'] = 'DejaVu Sans' |
| plt.rcParams['xtick.direction'] = 'in' |
| plt.rcParams['ytick.direction'] = 'in' |
|
|
| GROUP_STYLE = { |
| 'prior': {'color': '#C8C5BC', 'edgecolor': '#5F5E5A', 'label': 'Prior Works (2015–2019)'}, |
| 'recent': {'color': '#6B8E23', 'edgecolor': '#3A5010', 'label': 'Recent Edge SOTA'}, |
| 'teacher': {'color': '#9E9E9E', 'edgecolor': '#555555', 'label': 'Heavy Teacher Baseline'}, |
| 'ours': {'color': '#E53935', 'edgecolor': '#7B1FA2', 'label': 'LIPE (Ours)'}, |
| } |
|
|
| ARROW_STYLE = dict( |
| arrowstyle='->', |
| color='#888888', |
| lw=0.9, |
| connectionstyle='arc3,rad=0.0' |
| ) |
|
|
| fig, ax = plt.subplots(figsize=(9, 6.5), dpi=200) |
| fig.patch.set_facecolor('#FAFAFA') |
| ax.set_facecolor('#FAFAFA') |
|
|
| def bubble_area(p): |
| return 90 + 380 * np.log10(p + 1) |
|
|
| |
| for d in data: |
| style = GROUP_STYLE[d['group']] |
| s = bubble_area(d['params']) |
| marker = 'D' if d['group'] == 'ours' else 'o' |
| lw = 2.0 if d['group'] == 'ours' else 0.8 |
| zo = 6 if d['group'] == 'ours' else (5 if d['group'] == 'teacher' else 3) |
|
|
| ax.scatter( |
| d['gflops'], d['mae'], |
| s=s, |
| color=style['color'], |
| edgecolors=style['edgecolor'], |
| marker=marker, |
| alpha=0.88, |
| linewidths=lw, |
| zorder=zo, |
| ) |
|
|
| |
| |
| |
| MANUAL_OFFSETS = { |
| 'LIPE \n(Ours)': (-68, 32), |
| 'FGI-Net\n(2025)': ( 12, -28), |
| 'FR-Net\n(2024)': ( 12, 22), |
| 'FAR-Net\n(2021)': (-70, -12), |
| 'Gaze360\n(2019)': ( 14, 22), |
| 'Heavy Teacher\n(ResNet50)': ( 14, 10), |
| 'DilatedNet\n(2019)': ( 14, 0), |
| 'RT-Gene\n(2018)': ( 14, 10), |
| 'FullFace\n(2017)': ( 14, -26), |
| 'GazeNet\n(2017)': ( 14, 0), |
| 'iTracker\n(2016)': (-72, 10), |
| 'Mnist\n(2015)': ( 14, -26), |
| } |
|
|
| texts = [] |
| arrows = [] |
|
|
| for d in data: |
| is_ours = (d['group'] == 'ours') |
| fw = 'bold' if is_ours else 'normal' |
| col = '#C62828' if is_ours else '#333333' |
| fs = 8.5 if is_ours else 7.5 |
| bg_alpha = 0.82 if is_ours else 0.70 |
| bg_color = '#FFF9F9' if is_ours else 'white' |
|
|
| dx, dy = MANUAL_OFFSETS.get(d['name'], (12, 0)) |
|
|
| ann = ax.annotate( |
| d['name'], |
| xy=(d['gflops'], d['mae']), |
| xytext=(dx, dy), |
| textcoords='offset points', |
| fontsize=fs, |
| fontweight=fw, |
| color=col, |
| ha='center', |
| va='center', |
| zorder=9, |
| bbox=dict( |
| boxstyle='round,pad=0.28', |
| fc=bg_color, |
| ec='#CCCCCC' if not is_ours else '#E57373', |
| lw=0.6 if not is_ours else 1.0, |
| alpha=bg_alpha, |
| ), |
| arrowprops=dict( |
| arrowstyle='->', |
| color='#AAAAAA' if not is_ours else '#E53935', |
| lw=0.85 if not is_ours else 1.2, |
| connectionstyle='arc3,rad=0.15', |
| ), |
| ) |
| texts.append(ann) |
|
|
| |
| pareto_x = [0.02125, 0.08, 3.65, 4.12] |
| pareto_y = [4.72, 4.81, 4.66, 4.15] |
| ax.plot(pareto_x, pareto_y, color='#888888', linestyle='--', linewidth=1.3, zorder=2, alpha=0.8) |
|
|
| |
| ax.set_xscale('log') |
| ax.set_xlabel('Computational Complexity (GFLOPs) [Log Scale]', |
| fontsize=10, fontweight='bold', labelpad=7, color='#2E2E2E') |
| ax.set_ylabel('Gaze Estimation Error (MAE in Degrees) [Lower is Better]', |
| fontsize=10, fontweight='bold', labelpad=7, color='#2E2E2E') |
|
|
| ax.set_xlim(0.005, 600.0) |
| ax.set_ylim(8.2, 3.5) |
|
|
| ax.grid(True, which='both', ls=':', color='#DDDDDD', zorder=1) |
| ax.tick_params(axis='both', colors='#555555', labelsize=8.5) |
| for spine in ax.spines.values(): |
| spine.set_edgecolor('#CCCCCC') |
| spine.set_linewidth(0.7) |
|
|
| |
| |
| group_handles = [] |
| for g in ['ours', 'recent', 'teacher', 'prior']: |
| st = GROUP_STYLE[g] |
| mk = 'D' if g == 'ours' else 'o' |
| group_handles.append(mlines.Line2D( |
| [], [], color='none', marker=mk, markersize=7, |
| markerfacecolor=st['color'], markeredgecolor=st['edgecolor'], |
| markeredgewidth=1.1, label=st['label'], |
| )) |
| group_handles.append(mlines.Line2D( |
| [], [], color='#888888', linestyle='--', linewidth=1.3, |
| label='Current Pareto Frontier', |
| )) |
|
|
| leg1 = ax.legend( |
| handles=group_handles, |
| loc='lower left', |
| title='Model Classification', |
| fontsize=8, title_fontsize=8.5, |
| frameon=True, facecolor='white', edgecolor='#CCCCCC', framealpha=0.93, |
| borderpad=0.7, labelspacing=0.5, |
| bbox_to_anchor=(0.01, 0.01), |
| ) |
| ax.add_artist(leg1) |
|
|
| |
| sizes_demo = [0.18, 1.94, 11.72, 190.0] |
| size_labels = ['0.18M (Ours)', '1.94M', '11.72M', '190.0M'] |
| size_handles = [ |
| plt.scatter([], [], |
| s=bubble_area(sz) * 0.50, |
| color='#C8C5BC', alpha=0.7, |
| edgecolors='#5F5E5A', marker='o') |
| for sz in sizes_demo |
| ] |
| leg2 = ax.legend( |
| handles=size_handles, labels=size_labels, |
| loc='lower right', |
| title='Bubble Size (# Params)', |
| fontsize=8, title_fontsize=8.5, |
| frameon=True, facecolor='white', edgecolor='#CCCCCC', framealpha=0.93, |
| labelspacing=1.2, borderpad=0.9, handletextpad=1.1, |
| bbox_to_anchor=(0.99, 0.01), |
| ) |
|
|
| |
| ax.set_title( |
| 'Accuracy vs. Efficiency Trade-off: Gaze Estimation Benchmark', |
| fontsize=11, fontweight='bold', color='#1A1A1A', pad=10, |
| ) |
|
|
| plt.tight_layout(pad=1.4) |
| plt.savefig('/mnt/user-data/outputs/Figure_1_v2.png', dpi=300, bbox_inches='tight', |
| facecolor=fig.get_facecolor()) |
| print("Saved Figure_1_v2.png") |
| plt.show() |
|
|
