feat: add yolox algo

2026-08-05 21:19:01 +08:00
parent b399d94de3
commit 79fde03972
13 changed files with 368 additions and 2 deletions
@@ -0,0 +1,56 @@
+import argparse
+import csv
+import matplotlib.pyplot as plt
+import numpy as np
+
+parser = argparse.ArgumentParser(description="绘制训练曲线")
+parser.add_argument("--csv", default="runs/classify/train/results.csv", help="训练结果 CSV 路径")
+parser.add_argument("--output", default="训练曲线.png", help="输出图片路径")
+args = parser.parse_args()
+
+epochs = []
+train_loss = []
+val_loss = []
+val_acc = []
+
+with open(args.csv, "r", encoding="utf-8") as f:
+    reader = csv.DictReader(f)
+    for row in reader:
+        epochs.append(int(row["epoch"]))
+        train_loss.append(float(row["train/loss"]))
+        val_loss.append(float(row["val/loss"]))
+        val_acc.append(float(row["metrics/accuracy_top1"]))
+
+avg_acc = np.mean(val_acc)
+
+print("=" * 60)
+print(f"最终预测准确率：{val_acc[-1]:.2%}")
+print(f"最高准确率：{max(val_acc):.2%}")
+print(f"平均准确率：{avg_acc:.2%}")
+print("=" * 60)
+
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+plt.figure(figsize=(10, 4))
+
+plt.subplot(121)
+plt.plot(epochs, train_loss, label="训练损失", linewidth=2)
+plt.plot(epochs, val_loss, label="验证损失", linewidth=2)
+plt.title("损失函数曲线")
+plt.xlabel("Epoch")
+plt.ylabel("Loss")
+plt.legend()
+plt.grid(True)
+
+plt.subplot(122)
+plt.plot(epochs, val_acc, label="准确率", color="#2ecc71", linewidth=2)
+plt.title("准确率曲线")
+plt.xlabel("Epoch")
+plt.ylabel("Accuracy")
+plt.legend()
+plt.grid(True)
+
+plt.tight_layout()
+plt.savefig(args.output, dpi=300)
+plt.show()