scripts/visualize_sota_benchmark_v2.py

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
import numpy as np
from adjustText import adjust_text

# ── 1. DATA ───────────────────────────────────────────────────────────────────
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'),
]

# ── 2. STYLE ──────────────────────────────────────────────────────────────────
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)

# ── 3. SCATTER POINTS ─────────────────────────────────────────────────────────
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,
    )

# ── 4. ANNOTATIONS WITH ARROWS (adjustText for collision avoidance) ───────────
# Manual offsets (in data-space offsets via display transform) — fine-tuned per label
#  key: point name (first line), value: (dx_pts, dy_pts) offset for text
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)

# ── 5. PARETO FRONTIER ────────────────────────────────────────────────────────
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)

# ── 6. AXES ───────────────────────────────────────────────────────────────────
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)

# ── 7. LEGENDS ────────────────────────────────────────────────────────────────
# Left legend: Model classification
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)

# Right legend: Bubble size
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),
)

# ── 8. TITLE ──────────────────────────────────────────────────────────────────
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()