tools/plot_hyperv3.py

import matplotlib.pyplot as plt
import numpy as np

# 数据集
datasets = {
    "COCO-Stuff": [25.44, 26.07, 26.64, 26.82, 26.91, 27.05, 27.0, 27.13, 27.17, 27.21],
    "ADE": [21.88, 22.54, 23.03, 23.14, 23.16, 23.24, 23.17, 23.24, 23.24, 23.22],
    "CityScape": [34.53, 35.62, 35.81, 35.75, 35.71, 35.59, 35.37, 35.37, 35.36, 35.38],
    "COCO-Obj": [37.01, 37.9, 38.55, 38.7, 38.71, 38.76, 38.58, 38.79, 38.79, 38.82],
    "Context 60": [35.82, 36.71, 37.69, 37.9, 38.05, 38.25, 38.48, 38.53, 38.59, 38.65],
    "Context 59": [39.64, 40.46, 41.41, 41.63, 41.75, 41.94, 42.2, 42.2, 42.25, 42.32],
    "VOC 20": [83.53, 84.57, 85.29, 85.33, 85.43, 85.58, 85.17, 85.62, 85.63, 85.58],
    "VOC 21": [61.44, 62.58, 63.74, 64.1, 64.39, 64.74, 64.91, 65.19, 65.33, 65.44],
    "OV-COCO": [41.7, 42.8, 43.3, 42.7, 42.4, 42.0, 42.2, 41.8, 42.0, 41.5],
}

hyperparams = np.linspace(0.1, 1.0, 10)

# 获取除OV-COCO外的所有分割数据集
seg_datasets = [k for k in datasets if k != "OV-COCO"]

# 计算每个超参下的平均mIoU
seg_results = np.array([datasets[k] for k in seg_datasets])
avg_miou = np.mean(seg_results, axis=0)

plt.figure(figsize=(4, 3))  # 将高度压缩

# avg mIoU 曲线
plt.plot(
    hyperparams, avg_miou,
    label="DeCLIP Avg. mIoU",
    color="#2077b4",
    marker="o",
    linestyle='--',
    linewidth=3,
    markersize=6,
    zorder=5
)

# OV-COCO 曲线
plt.plot(
    hyperparams, datasets["OV-COCO"],
    label="DeCLIP mAP (Novel)",
    color="#d62728",
    marker="s",
    linestyle='--',
    linewidth=3,
    markersize=6,
    zorder=10
)

# 添加14.1的横虚线（CLIP avg baseline）
plt.axhline(14.1, color="#2ca02c", linestyle="--", linewidth=2, label="CLIP Avg. mIoU")

# 添加17.5的横虚线（CLIP OV-COCO baseline）
plt.axhline(17.5, color="#d62728", linestyle="--", linewidth=2, label="CLIP mAP (Novel)")

# plt.xlabel("Hyperparameter Value", fontsize=13)
# plt.ylabel("mIoU (%)", fontsize=13)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tick_params(axis='x', labelsize=14)  # x轴刻度大
plt.tick_params(axis='y', labelsize=12)  # y轴刻度不变
plt.legend(fontsize=14, loc='best')
plt.tight_layout()
plt.savefig("hyperparameter.pdf", dpi=300)
plt.close()