57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import argparse
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import csv
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import matplotlib.pyplot as plt
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import numpy as np
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parser = argparse.ArgumentParser(description="绘制训练曲线")
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parser.add_argument("--csv", default="runs/classify/train/results.csv", help="训练结果 CSV 路径")
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parser.add_argument("--output", default="训练曲线.png", help="输出图片路径")
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args = parser.parse_args()
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epochs = []
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train_loss = []
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val_loss = []
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val_acc = []
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with open(args.csv, "r", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in reader:
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epochs.append(int(row["epoch"]))
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train_loss.append(float(row["train/loss"]))
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val_loss.append(float(row["val/loss"]))
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val_acc.append(float(row["metrics/accuracy_top1"]))
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avg_acc = np.mean(val_acc)
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print("=" * 60)
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print(f"最终预测准确率：{val_acc[-1]:.2%}")
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print(f"最高准确率：{max(val_acc):.2%}")
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print(f"平均准确率：{avg_acc:.2%}")
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print("=" * 60)
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plt.rcParams["font.sans-serif"] = ["SimHei"]
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plt.rcParams["axes.unicode_minus"] = False
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plt.figure(figsize=(10, 4))
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plt.subplot(121)
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plt.plot(epochs, train_loss, label="训练损失", linewidth=2)
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plt.plot(epochs, val_loss, label="验证损失", linewidth=2)
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plt.title("损失函数曲线")
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plt.xlabel("Epoch")
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plt.ylabel("Loss")
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plt.legend()
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plt.grid(True)
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plt.subplot(122)
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plt.plot(epochs, val_acc, label="准确率", color="#2ecc71", linewidth=2)
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plt.title("准确率曲线")
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plt.xlabel("Epoch")
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plt.ylabel("Accuracy")
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plt.legend()
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plt.grid(True)
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plt.tight_layout()
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plt.savefig(args.output, dpi=300)
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plt.show()
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