yolov26模型训练（使用yolov5样本训练）

1、样本转换原始文件路径为/dataSharing/otherTrain/dataset/yolo26_data/Annotations/ # xmlimages/ # jpg第 0 步确认你的数据是否一一对应cd /dataSharing/otherTrain/dataset/yolo26_data # 看看有多少图片/标注 ls images | wc -l ls Annotations | wc -l # 随机抽几个看看是否同名 python3 - PY import os, random imgs [f for f in os.listdir(images) if f.lower().endswith((.jpg,.jpeg,.png))] xmls set([os.path.splitext(f)[0] for f in os.listdir(Annotations) if f.lower().endswith(.xml)]) sample random.sample(imgs, min(20, len(imgs))) bad [f for f in sample if os.path.splitext(f)[0] not in xmls] print(sample missing xml:, bad) PY如果sample missing xml为空说明基本OK。第 1 步把 VOC(xml) 转 YOLO并自动划分 train/val在/dataSharing/otherTrain/dataset/yolo26_data下新建脚本cd /dataSharing/otherTrain/dataset/yolo26_data nano voc_to_yolo_split.py把下面代码粘进去可直接用import os, random, shutil import xml.etree.ElementTree as ET from pathlib import Path ROOT Path(/dataSharing/otherTrain/dataset/yolo26_data) IMG_DIR ROOT / images ANN_DIR ROOT / Annotations OUT_IMG_TRAIN ROOT / images / train OUT_IMG_VAL ROOT / images / val OUT_LBL_TRAIN ROOT / labels / train OUT_LBL_VAL ROOT / labels / val VAL_RATIO 0.2 SEED 42 def parse_xml(xml_path: Path): tree ET.parse(xml_path) root tree.getroot() size root.find(size) if size is None: raise ValueError(f{xml_path} missing size) w int(size.findtext(width)) h int(size.findtext(height)) objs [] for obj in root.findall(object): name obj.findtext(name) if not name: continue difficult obj.findtext(difficult) if difficult and difficult.strip() 1: continue bnd obj.find(bndbox) if bnd is None: continue xmin float(bnd.findtext(xmin)) ymin float(bnd.findtext(ymin)) xmax float(bnd.findtext(xmax)) ymax float(bnd.findtext(ymax)) xmin max(0.0, xmin) ymin max(0.0, ymin) xmax min(float(w), xmax) ymax min(float(h), ymax) if xmax xmin or ymax ymin: continue objs.append((name, xmin, ymin, xmax, ymax)) return w, h, objs def voc_to_yolo(w, h, xmin, ymin, xmax, ymax): xc (xmin xmax) / 2.0 / w yc (ymin ymax) / 2.0 / h bw (xmax - xmin) / w bh (ymax - ymin) / h return xc, yc, bw, bh def main(): assert IMG_DIR.exists(), IMG_DIR assert ANN_DIR.exists(), ANN_DIR # 收集图片 imgs [p for p in IMG_DIR.iterdir() if p.suffix.lower() in [.jpg,.jpeg,.png] and p.is_file()] # 排除已经分出来的 train/val 目录 imgs [p for p in imgs if p.parent IMG_DIR] if not imgs: raise RuntimeError(fNo images found in {IMG_DIR}) # 收集类别扫一遍 xml classes set() missing_xml 0 for img in imgs: xml ANN_DIR / (img.stem .xml) if not xml.exists(): missing_xml 1 continue _, _, objs parse_xml(xml) for name, *_ in objs: classes.add(name) classes sorted(list(classes)) if not classes: raise RuntimeError(No classes parsed from XML. Check your xml format.) class2id {c:i for i,c in enumerate(classes)} # 写 classes.txt (ROOT / classes.txt).write_text(\n.join(classes), encodingutf-8) # 划分 train/val random.seed(SEED) random.shuffle(imgs) n_val int(len(imgs) * VAL_RATIO) val_imgs imgs[:n_val] train_imgs imgs[n_val:] # 创建输出目录 for p in [OUT_IMG_TRAIN, OUT_IMG_VAL, OUT_LBL_TRAIN, OUT_LBL_VAL]: p.mkdir(parentsTrue, exist_okTrue) def write_one(img_path: Path, out_img_dir: Path, out_lbl_dir: Path): xml_path ANN_DIR / (img_path.stem .xml) if not xml_path.exists(): return False, missing_xml w, h, objs parse_xml(xml_path) lines [] for name, xmin, ymin, xmax, ymax in objs: cid class2id[name] xc, yc, bw, bh voc_to_yolo(w, h, xmin, ymin, xmax, ymax) # 保险限制在0~1 xc min(max(xc, 0.0), 1.0) yc min(max(yc, 0.0), 1.0) bw min(max(bw, 0.0), 1.0) bh min(max(bh, 0.0), 1.0) lines.append(f{cid} {xc:.6f} {yc:.6f} {bw:.6f} {bh:.6f}) # 没有目标也要写空txtYOLO允许 (out_lbl_dir / f{img_path.stem}.txt).write_text(\n.join(lines), encodingutf-8) # 复制图片也可以改成移动shutil.move shutil.copy2(img_path, out_img_dir / img_path.name) return True, ok stats {ok:0, missing_xml:0} for img in train_imgs: ok, reason write_one(img, OUT_IMG_TRAIN, OUT_LBL_TRAIN) stats[reason] stats.get(reason, 0) (0 if ok else 1) if ok: stats[ok] 1 for img in val_imgs: ok, reason write_one(img, OUT_IMG_VAL, OUT_LBL_VAL) stats[reason] stats.get(reason, 0) (0 if ok else 1) if ok: stats[ok] 1 # 生成 data.yamlUltralytics/YOLO系列通用 data_yaml fpath: {ROOT} train: images/train val: images/val names: for i, c in enumerate(classes): data_yaml f {i}: {c}\n (ROOT / data.yaml).write_text(data_yaml, encodingutf-8) print(Done.) print(Classes:, classes) print(Stats:, stats) print(Generated:, ROOT/data.yaml, ROOT/classes.txt) if __name__ __main__: main()保存退出后执行python3 voc_to_yolo_split.py执行完成后你会得到labels/train/*.txtlabels/val/*.txtimages/train/*.jpgimages/val/*.jpgdata.yamlclasses.txt你的原始images/*.jpg不会被删脚本是 copy2。如果你想节省空间我可以使用“移动版”。第 2 步快速检查转换结果是否正确cd /dataSharing/otherTrain/dataset/yolo26_data # 统计数量 find images/train -type f | wc -l find labels/train -type f | wc -l find images/val -type f | wc -l find labels/val -type f | wc -l # 随机看一个标注文件 ls labels/train | head head -n 5 labels/train/$(ls labels/train | shuf -n 1)如果images/train和labels/train数量一致基本没问题。第 3 步新建一个虚拟环境训练 YOLOv26Ultralytics 方式第 3-1 步创建 YOLOv26 专用 conda 环境进入你想放环境的目录随便比如放在项目目录旁边conda create -n yolo26 python3.10 -y第 3-2 步激活虚拟环境conda activate yolo26激活成功后你会看到前缀(yolo26) rootxxx:#第 3-3 步安装 PyTorch最关键一步在 yolo26 环境中执行pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121安装完马上验证python - PY import torch print(torch.__version__) print(CUDA:, torch.cuda.is_available()) print(GPU:, torch.cuda.get_device_name(0)) PY必须看到CUDA: True第 3-4 步安装 Ultralytics(清华源)pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple --default-timeout1000验证yolo version第 3-4步开始训练yolo26x.pt下载地址https://huggingface.co/Ultralytics/YOLO26?utm_sourcechatgpt.comyolo26n.pt下载地址https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.ptyolo detect train \ model./yolo26x.pt \ data./data.yaml \ imgsz640 \ epochs200 \ batch10 \ device3 \ workers8 \ ampTrue \ cos_lrTrue \ close_mosaic10 \ project/dataSharing/otherTrain/yolo26_runs \ nameyolo26x_6403-5 验证最直观拿你真实业务视频/图片做推理看框对不对nohup yolo detect train \ model/data/dataSharing/otherTrain/dataset/yolo26_data/yunnan/yolo26x.pt \ data/data/dataSharing/otherTrain/dataset/yolo26_data/yunnan/data.yaml \ imgsz640 \ epochs100 \ batch8 \ device1 \ workers16 \ ampTrue \ cos_lrTrue \ close_mosaic10 \ project/data/dataSharing/otherTrain/yolo26_runs \ nameyolo26x_640_yunnan_20260430 输出会在ls /dataSharing/otherTrain/yolo26_runs/exp01_pred_vid