#1 错位、缺框、空框？先看看你写的是不是**正确**的文件名！你可以用脚本一次性遍历所有图片和txt，看它们是不是匹配上了例如命令：
bash
for f in ./train/images/*.jpg;do
basename "$f" .jpg;怎么说呢，done> names.txt
for f in ./train/labels/*.txt;do
basename "$f" .txt;done>> names.txt
sort names.txt | uniq -d
如果出现“duplicate”或空白行，那就是文件名冲突或缺失了。
#2 类别数不匹配？话说回来，**检查你的data.yaml**：
yaml
再看nc。5 # 类别数要和你的标签文件里出现的类 ID 一致
说到names，- ship
- harbor
- ...
如果你把“ship”改成了“船”，记得同步修改 yaml。
#3 标签内容错误？手动抽取一个 xml 与对应 txt 对比一下：
xml
#4 图像尺寸错误？如果你在下载或预处理时把图片尺寸统一到了非原始尺寸。需要把公式里的“图像宽度/高度”改成统一后的尺寸，否则坐标会偏移。老实说，#5 数据目录结构不符合要求？RF‑DETR 要求严格遵循 “valid” 而不是 “val”，否则会报找不到验证集。至于确保，
-
seaships_yolo/train/images/和labels/存放训练样本； -
seaships_yolo/valid/images/和labels/存放验证样本； -
seaships_yolo/test/images/和labels/存放测试样本；#6 最终记得跑一次可视化检测：
bash
python detect.py --source ./train/images --weights seahships_yolo.pt --project ./demo --name v1 --conf 0.25
查看预测框与真实框对齐情况，如果明显错位就回头检查坐标公式与标签格式。
这几条排查思路，一般能解决绝大多数因格式导致的训练失败。祝你调参顺利，
想必你已经知道。你现在可以根据这些思路进行排查了——无论是从代码层面还是从数据层面都能帮你定位问题根源，从而尽快处理。接下来让我们继续往下走吧！
好了现在我们来讲讲如何真正让模型跑起来也就是后面的实际代码部分——包括训练、监控还有常见性能瓶颈分析。下面内容请直接照搬代码块粘贴即可，不需要额外解释。如果你碰到任何报错，可以先回头查看上面列出的排查点，再进行针对性的修复。祝你玩得开心，
## 📚 学习资源列表
### 🔧 官方文档与教程
| 文档类型 | 链接 |
| --- | --- |
| PyTorch 官方文档 | https://docs.pytorch.org/en/stable/index.html |
| Transformers 官方文档 | https://huggingface.co/docs/transformers/index |
| Roboflow Docs – RF‑DETR | https://docs.roboflow.com/rfdetr |
### 📖 教程文章
| 标题 | 作者 | 链接 |
| --- | --- | --- |
| “From COCO to Custom Dataset” – Roboflow Blog | Roboflow Team | https://blog.roboflow.com/from-coco-to-custom-dataset |
| “Speeding Up DETR Training” – Medium Post by @john_doe | Medium Community | https://medium.com/@john_doe/speeding-up-detr-training |
### 🚀 示例项目
| 项目名称 | 描述 |
| --- | --- |
| rfdetr-demo-v1 · GitHub Repo · https://github.com/example/rfdetr-demo-v1.git |
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