{"id":2007,"date":"2026-04-29T19:26:31","date_gmt":"2026-04-29T11:26:31","guid":{"rendered":"https:\/\/hueil.top\/?p=2007"},"modified":"2026-05-21T21:05:41","modified_gmt":"2026-05-21T13:05:41","slug":"yolo%e8%ae%ad%e7%bb%83-%e5%8d%b7%e7%a7%af%e7%bd%91%e7%bb%9c","status":"publish","type":"post","link":"https:\/\/hueil.top\/?p=2007","title":{"rendered":"YOLO\u8bad\u7ec3-\u5377\u79ef\u7f51\u7edc"},"content":{"rendered":"\n

\u57fa\u7840\u4f7f\u7528<\/h1>\n\n\n\n

YOLO \u89c6\u89c9\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\uff0c\u901a\u8fc7\u5377\u79ef\u7f51\u7edc\u5b8c\u6210\u8bad\u7ec3\u540e\uff0c\u80fd\u591f\u5b9e\u73b0\u5bf9\u56fe\u50cf\u4e2d\u76ee\u6807\u7684\u5feb\u901f\u8bc6\u522b\u4e0e\u5b9a\u4f4d\u3002\u5176\u6838\u5fc3\u903b\u8f91\u5e76\u975e\u9010\u50cf\u7d20\u5206\u6790\u76f8\u4f3c\u5ea6\uff0c\u800c\u662f\u501f\u52a9\u5377\u79ef\u7f51\u7edc\u7684\u7279\u5f81\u63d0\u53d6\u80fd\u529b\uff0c\u4ece\u56fe\u50cf\u4e2d\u63d0\u53d6\u76ee\u6807\u7684\u6df1\u5c42\u7279\u5f81\uff08\u5982\u8f6e\u5ed3\u3001\u7eb9\u7406\uff09\uff0c\u8fdb\u800c\u5224\u65ad\u76ee\u6807\u7c7b\u522b\u53ca\u5177\u4f53\u4f4d\u7f6e\uff0c\u5b9e\u73b0\u7aef\u5230\u7aef\u7684\u9ad8\u6548\u68c0\u6d4b\u3002<\/p>\n\n\n\n

\u6a21\u578b\u5f15\u5165<\/h2>\n\n\n\n

\u5728 Python \u73af\u5883\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u5f15\u5165\u5b98\u65b9\u7ef4\u62a4\u7684ultralytics<\/code>\u5e93\u5feb\u901f\u4f7f\u7528 YOLO \u7cfb\u5217\u6a21\u578b\u3002\u901a\u8fc7\u521b\u5efa YOLO \u6a21\u578b\u5b9e\u4f8b\uff0c\u5e93\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5bf9\u5e94\u7248\u672c\u7684\u5b98\u65b9\u9884\u8bad\u7ec3\u6743\u91cd<\/strong>\uff0c\u65e0\u9700\u624b\u52a8\u914d\u7f6e\uff0c\u5373\u53ef\u5feb\u901f\u5f00\u5c55\u6a21\u578b\u8bad\u7ec3\u3001\u76ee\u6807\u68c0\u6d4b\u7b49\u4efb\u52a1\u3002<\/p>\n\n\n\n

from ultralytics import YOLO\n\nmodel = YOLO(\"yolo11n.pt\")\n\/\/\u5bf9\u56fe\u7247\u8fdb\u884c\u76ee\u6807\u68c0\u6d4b\nresults = model(img_path)\n\/\/\u663e\u793a\u8bc6\u522b\u68c0\u6d4b\u7ed3\u679c\uff08\u7c7b\u522b\uff0c\u5750\u6807\uff0c\u7f6e\u4fe1\u5ea6\uff09\nresults[0].show() <\/code><\/pre>\n\n\n\n
\u8bad\u7ec3\u51c6\u5907<\/h2>\n\n\n\n
\u7531\u4e8e YOLO \u63d0\u4f9b\u4e86\u5b98\u65b9\u9884\u8bad\u7ec3\u6743\u91cd\uff0c\u6a21\u578b\u5df2\u7ecf\u5177\u5907\u8bc6\u522b\u65e5\u5e38\u573a\u666f\u4e2d\u7b80\u5355\u7684\u4eba\u548c\u7269\u54c1\u7684\u80fd\u529b\u3002\u4f46\u5982\u679c\u8981\u5728\u7279\u5b9a\u7684\u5e94\u7528\u573a\u666f\u4e2d\u4f7f\u7528\uff0c\u5c31\u5fc5\u987b\u5bf9\u6a21\u578b\u8fdb\u884c\u9488\u5bf9\u6027\u8bad\u7ec3\u3002<\/p>\n\n\n\n
\u8bad\u7ec3\u7684\u672c\u8d28\u662f\uff1a\u8ba9\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5728\u6211\u4eec\u63d0\u4f9b\u7684\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u4e0a\uff0c\u91cd\u65b0\u5b66\u4e60\u76ee\u6807\u7684\u7279\u5f81\u89c4\u5f8b\uff0c\u8c03\u6574\u5185\u90e8\u7684\u6743\u91cd\u53c2\u6570\uff0c\u4f7f\u6a21\u578b\u80fd\u591f\u7cbe\u51c6\u8bc6\u522b\u6211\u4eec\u6307\u5b9a\u7684\u76ee\u6807\u7c7b\u522b\u3001\u4f4d\u7f6e\u4e0e\u5f62\u6001\u3002<\/strong><\/p>\n\n\n\n
\u6a21\u578b\u8bad\u7ec3\u9700\u8981\u63d0\u524d\u51c6\u5907\u597d\u5212\u5206\u597d\u7684\u8bad\u7ec3\u96c6\uff08train\uff09\u3001\u6d4b\u8bd5\u96c6\uff08test\uff09\u3001\u9a8c\u8bc1\u96c6\uff08val\uff09<\/strong>\uff0c\u5e76\u7f16\u5199\u5bf9\u5e94\u7684 yaml \u914d\u7f6e\u6587\u4ef6<\/strong> \u544a\u8bc9\u6a21\u578b\u6570\u636e\u96c6\u8def\u5f84\u3001\u7c7b\u522b\u6570\u91cf\u548c\u7c7b\u522b\u540d\u79f0\u3002YOLO \u6a21\u578b\u91c7\u7528\u56fe\u7247\u4e0e\u6807\u6ce8\u6587\u4ef6\u5206\u79bb\u5b58\u653e<\/strong>\u7684\u6807\u51c6\u7ed3\u6784\uff0c\u6574\u4f53\u6587\u4ef6\u5e03\u5c40\u5982\u4e0b\uff1a<\/p>\n\n\n\n
dataset\/  # \u6570\u636e\u96c6\u6839\u76ee\u5f55\uff08\u81ea\u5b9a\u4e49\u540d\u79f0\uff09\n\u251c\u2500\u2500 images\/          # \u5b58\u653e\u6240\u6709\u56fe\u7247\u6587\u4ef6\n\u2502   \u251c\u2500\u2500 train\/       # \u8bad\u7ec3\u96c6\u56fe\u7247\uff08.jpg\/.png\uff09\n\u2502   \u251c\u2500\u2500 val\/         # \u9a8c\u8bc1\u96c6\u56fe\u7247\uff08\u8bad\u7ec3\u4e2d\u8bc4\u4f30\u7cbe\u5ea6\uff09\n\u2502   \u2514\u2500\u2500 test\/        # \u6d4b\u8bd5\u96c6\u56fe\u7247\uff08\u6700\u7ec8\u6a21\u578b\u6d4b\u8bd5\uff09\n\u251c\u2500\u2500 labels\/          # \u5b58\u653e\u6807\u6ce8\u6587\u4ef6\uff08\u4e0e\u56fe\u7247\u4e00\u4e00\u5bf9\u5e94\uff09\n\u2502   \u251c\u2500\u2500 train\/       # \u8bad\u7ec3\u96c6\u6807\u6ce8\uff08.txt \u683c\u5f0f\uff09\n\u2502   \u251c\u2500\u2500 val\/         # \u9a8c\u8bc1\u96c6\u6807\u6ce8\n\u2502   \u2514\u2500\u2500 test\/        # \u6d4b\u8bd5\u96c6\u6807\u6ce8\n\u2514\u2500\u2500 data.yaml        # \u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6<\/code><\/pre>\n\n\n\n
\u5728\u5b8c\u6210\u6570\u636e\u96c6\u5212\u5206\u540e\uff0c\u9700\u8981\u5bf9\u56fe\u7247\u4e2d\u7684\u76ee\u6807\u8fdb\u884c\u6807\u6ce8\uff0c\u4e3a\u6a21\u578b\u63d0\u4f9b\u5b66\u4e60\u4f9d\u636e\u3002LabelImg<\/strong> \u662f YOLO \u6a21\u578b\u6700\u5e38\u7528\u7684\u5f00\u6e90\u6807\u6ce8\u5de5\u5177\uff0c\u652f\u6301\u76f4\u63a5\u8f93\u51fa\u6a21\u578b\u8bad\u7ec3\u6240\u9700\u7684\u6807\u51c6\u6807\u6ce8\u683c\u5f0f\u3002\u901a\u8fc7\u8be5\u5de5\u5177\u6309\u4e0bW\u952e\u6846\u9009\u51fa\u56fe\u7247\u4e2d\u7684\u76ee\u6807\u5e76\u6307\u5b9a\u7c7b\u522b\u540e\uff0c\u5de5\u5177\u4f1a\u81ea\u52a8\u751f\u6210\u4e0e\u56fe\u7247\u540c\u540d\u7684 .txt \u6807\u6ce8\u6587\u4ef6<\/strong>\uff0c\u5e76\u4e25\u683c\u5339\u914d YOLO \u683c\u5f0f\u8981\u6c42\uff0c\u65e0\u9700\u624b\u52a8\u8f6c\u6362\uff0c\u76f4\u63a5\u6ee1\u8db3\u8bad\u7ec3\u9700\u6c42\u3002<\/p>\n\n\n\n
\u6807\u6ce8\u5de5\u5177Labelimg\u7684\u5b89\u88c5\u4e0e\u4f7f\u7528<\/a><\/p>\n\n\n\n
\u5b8c\u6210\u56fe\u7247\u6807\u6ce8\u540e\uff0c\u6240\u6709\u56fe\u50cf\u548c .txt<\/code> \u6807\u7b7e\u5df2\u4e00\u4e00\u5bf9\u5e94\u3002\u4e3a\u4e86\u5feb\u901f\u3001\u968f\u673a\u5730\u5c06\u6570\u636e\u6309\u6bd4\u4f8b\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u3001\u6d4b\u8bd5\u96c6<\/strong>\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199 Python \u811a\u672c\u81ea\u52a8\u5b8c\u6210\u6587\u4ef6\u590d\u5236\u4e0e\u5206\u7c7b\uff0c\u907f\u514d\u624b\u52a8\u64cd\u4f5c\u7684\u8bef\u5dee\u3002<\/p>\n\n\n\n
import os\nimport random\nimport shutil\n\n# 1. \u914d\u7f6e\u8def\u5f84\u4e0e\u5212\u5206\u6bd4\u4f8b\nimage_dir = 'images'       # \u539f\u59cb\u56fe\u7247\u5b58\u653e\u76ee\u5f55\nlabel_dir = 'labels'      # \u539f\u59cb\u6807\u7b7e\u5b58\u653e\u76ee\u5f55\noutput_root = \"datasets\"  # \u8f93\u51fa\u6570\u636e\u96c6\u6839\u76ee\u5f55\nsplit_ratio = [0.8, 0.1, 0.1]  # \u8bad\u7ec3\u96c6:\u9a8c\u8bc1\u96c6:\u6d4b\u8bd5\u96c6 = 8:1:1\nimage_exts = ('.jpg','.jpeg','.png','.bmp')  # \u652f\u6301\u7684\u56fe\u7247\u683c\u5f0f\n\n# \u6570\u636e\u96c6\u7c7b\u578b\nsets = ['train','val','test']\n\n# 2. \u521b\u5efa\u8f93\u51fa\u6587\u4ef6\u5939\u7ed3\u6784\nfor setname in sets:\n    img_dest = os.path.join(output_root, setname, \"images\")\n    label_dest = os.path.join(output_root, setname, \"labels\")\n    os.makedirs(img_dest, exist_ok=True)\n    os.makedirs(label_dest, exist_ok=True)\n\n# 3. \u8bfb\u53d6\u5e76\u6253\u4e71\u6240\u6709\u56fe\u7247\u6587\u4ef6\nimage_files = [f for f in os.listdir(image_dir) if f.endswith(image_exts)]\nrandom.shuffle(image_files)  # \u968f\u673a\u6253\u4e71\uff0c\u4fdd\u8bc1\u6570\u636e\u5206\u5e03\u5747\u5300\ntotal = len(image_files)\nprint(f\"\u53d1\u73b0 {total} \u4e2a\u56fe\u50cf\u6587\u4ef6\")\n\n# 4. \u8ba1\u7b97\u5404\u6570\u636e\u96c6\u6570\u91cf\ntrain_num = int(total * split_ratio[0])\nval_num = int(total * split_ratio[1])\ntest_num = total - train_num - val_num\nprint(f\"\u5212\u5206\u7ed3\u679c: \u8bad\u7ec3\u96c6 {train_num} \u4e2a\uff0c\u9a8c\u8bc1\u96c6 {val_num} \u4e2a\uff0c\u6d4b\u8bd5\u96c6 {test_num} \u4e2a\")\n\n# 5. \u590d\u5236\u6587\u4ef6\u5230\u5bf9\u5e94\u76ee\u5f55\nfor idx, img_name in enumerate(image_files):\n    # \u5224\u65ad\u5f53\u524d\u56fe\u7247\u5c5e\u4e8e\u54ea\u4e2a\u96c6\u5408\n    if idx < train_num:\n        set_name = \"train\"\n    elif idx < train_num + val_num:\n        set_name = \"val\"\n    else:\n        set_name = \"test\"\n    \n    # \u590d\u5236\u56fe\u7247\n    src_img = os.path.join(image_dir, img_name)\n    dest_img = os.path.join(output_root, set_name, \"images\", img_name)\n    shutil.copy2(src_img, dest_img)\n\n    # \u590d\u5236\u5bf9\u5e94\u7684\u6807\u7b7e\u6587\u4ef6\n    label_name = os.path.splitext(img_name)[0] + '.txt'\n    src_label = os.path.join(label_dir, label_name)\n    if os.path.exists(src_label):\n        dest_label = os.path.join(output_root, set_name, \"labels\", label_name)\n        shutil.copyfile(src_label, dest_label)\n    else:\n        print(f\"\u8b66\u544a\uff1a{label_name} \u6807\u7b7e\u6587\u4ef6\u7f3a\u5931\uff0c\u8df3\u8fc7\u590d\u5236\uff01\")\n\nprint(\"\u6570\u636e\u96c6\u5212\u5206\u5b8c\u6210\uff01\u7ed3\u679c\u5b58\u50a8\u5728 .\/datasets \u76ee\u5f55\")<\/code><\/pre>\n\n\n\n
\u751f\u6210\u76ee\u5f55\u7ed3\u6784\u5982\u4e0b<\/p>\n\n\n\n
datasets\/\n\u251c\u2500\u2500 train\/\n\u2502   \u251c\u2500\u2500 images\/  # \u8bad\u7ec3\u96c6\u56fe\u7247\n\u2502   \u2514\u2500\u2500 labels\/  # \u8bad\u7ec3\u96c6\u6807\u7b7e\n\u251c\u2500\u2500 val\/\n\u2502   \u251c\u2500\u2500 images\/  # \u9a8c\u8bc1\u96c6\u56fe\u7247\n\u2502   \u2514\u2500\u2500 labels\/  # \u9a8c\u8bc1\u96c6\u6807\u7b7e\n\u2514\u2500\u2500 test\/\n    \u251c\u2500\u2500 images\/  # \u6d4b\u8bd5\u96c6\u56fe\u7247\n    \u2514\u2500\u2500 labels\/  # \u6d4b\u8bd5\u96c6\u6807\u7b7e<\/code><\/pre>\n\n\n\n
\u6570\u636e\u96c6\u5212\u5206\u5b8c\u6210\u540e\uff0c\u9700\u8981\u7f16\u5199 YAML \u914d\u7f6e\u6587\u4ef6<\/strong>\uff0c\u8ba9 YOLO \u6a21\u578b\u80fd\u591f\u6b63\u786e\u8bfb\u53d6\u6570\u636e\u3002\u8be5\u6587\u4ef6\u7684\u6838\u5fc3\u4f5c\u7528\u662f\u544a\u8bc9\u6a21\u578b\u6570\u636e\u96c6\u7684\u5b58\u50a8\u8def\u5f84\u3001\u9700\u8981\u68c0\u6d4b\u7684\u76ee\u6807\u7c7b\u522b\u603b\u6570\uff0c\u4ee5\u53ca\u4e0e\u6807\u6ce8\u65f6\u5b8c\u5168\u4e00\u81f4\u7684\u7c7b\u522b\u540d\u79f0<\/strong>\uff0c\u786e\u4fdd\u6a21\u578b\u8bad\u7ec3\u65f6\u80fd\u591f\u6b63\u786e\u5339\u914d\u6807\u7b7e\u3002<\/p>\n\n\n\n
path: .\/datasets\ntrain: train\/images\nval: val\/images\ntest: test\/images\nnc: 3\nnames: ['\u7c7b\u522b\u540d1',\"\u7c7b\u522b\u540d2\",\"\u7c7b\u522b\u540d3\"]<\/code><\/pre>\n\n\n\n
\u6a21\u578b\u8bad\u7ec3<\/h2>\n\n\n\n
\u6570\u636e\u96c6\u4e0e\u914d\u7f6e\u6587\u4ef6\u51c6\u5907\u5b8c\u6bd5\u540e\uff0c\u5373\u53ef\u901a\u8fc7 Python \u811a\u672c\u542f\u52a8\u6a21\u578b\u8bad\u7ec3\u3002\u6211\u4eec\u52a0\u8f7d\u5b98\u65b9\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u4f20\u5165\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6\u4e0e\u8bad\u7ec3\u53c2\u6570\uff0c\u6a21\u578b\u4f1a\u81ea\u52a8\u5f00\u59cb\u8fed\u4ee3\u5b66\u4e60\u3002\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u5377\u79ef\u7f51\u7edc\u4f1a\u4e0d\u65ad\u4f18\u5316\u6743\u91cd\uff0c\u6700\u7ec8\u8f93\u51fa\u9002\u7528\u4e8e\u81ea\u5b9a\u4e49\u573a\u666f\u7684\u68c0\u6d4b\u6a21\u578b\u3002<\/p>\n\n\n\n
import os\nimport yaml\nfrom ultralytics import YOLO\n\ndef main():\n    # \u9879\u76ee\u6839\u76ee\u5f55\n    project_dir = \".\"\n    # \u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6\u8def\u5f84\n    data_config_path = os.path.join(project_dir, \"data.yaml\")\n\n    # \u52a0\u8f7d YOLO11 \u9884\u8bad\u7ec3\u6a21\u578b\n    model = YOLO(\"yolo11n.pt\")\n\n    # \u8bad\u7ec3\u53c2\u6570\u914d\u7f6e\n    train_params = {\n        \"data\": data_config_path,   # \u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6\n        \"epochs\": 13,              # \u8bad\u7ec3\u8fed\u4ee3\u8f6e\u6570\n        \"batch\": 10,               # \u6bcf\u6b21\u8bad\u7ec3\u7684\u6279\u6b21\u5927\u5c0f\n        \"imgsz\": 640,              # \u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\n        \"device\": \"cpu\"            # \u4f7f\u7528 CPU \u8bad\u7ec3\uff08\u6709 GPU \u53ef\u6539\u4e3a 0\uff09\n    }\n\n    # \u542f\u52a8\u8bad\u7ec3\n    results = model.train(**train_params)\n\nif __name__ == '__main__':\n    main()<\/code><\/pre>\n\n\n\n
\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u7a0b\u5e8f\u4f1a\u81ea\u52a8\u751f\u6210 runs<\/strong> \u76ee\u5f55\uff0c\u8bad\u7ec3\u76f8\u5173\u65e5\u5fd7\u3001\u53c2\u6570\u4e0e\u6a21\u578b\u6743\u91cd\u5747\u4fdd\u5b58\u5728\u8be5\u76ee\u5f55\u4e0b\u3002\u5728\u8bad\u7ec3\u751f\u6210\u7684 exp<\/code> \u6587\u4ef6\u5939\u4e2d\uff0c\u4f1a\u4ea7\u751f best.pt<\/strong> \u4e0e last.pt<\/strong> \u4e24\u4e2a\u6743\u91cd\u6587\u4ef6\u3002\u5176\u4e2d best.pt<\/strong> \u662f\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7cbe\u5ea6\u8868\u73b0\u6700\u4f18\u7684\u6a21\u578b\u6743\u91cd\uff0clast.pt<\/strong> \u4e3a\u6700\u540e\u4e00\u8f6e\u8fed\u4ee3\u4fdd\u5b58\u7684\u6743\u91cd\uff0c\u5b9e\u9645\u9879\u76ee\u90e8\u7f72\u4e0e\u68c0\u6d4b\u5e94\u7528\u4e2d\uff0c\u901a\u5e38\u4f18\u5148\u9009\u7528 best.pt<\/strong> \u6700\u4f18\u6a21\u578b\u3002<\/p>\n\n\n\n
\u6a21\u578b\u8bc4\u6d4b<\/h2>\n\n\n\n
\u8bad\u7ec3\u5b8c\u6210\u5e76\u83b7\u5f97\u6700\u4f18\u6a21\u578b best.pt<\/code> \u540e\uff0c\u5373\u53ef\u5c06\u6a21\u578b\u7528\u4e8e\u5b9e\u9645\u7684\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u3002\u901a\u8fc7\u7f16\u5199\u63a8\u7406\u811a\u672c\uff0c\u52a0\u8f7d\u8bad\u7ec3\u597d\u7684\u6743\u91cd\u6587\u4ef6\uff0c\u5bf9\u5355\u5f20\u56fe\u7247\u8fdb\u884c\u5206\u6790\u3002\u6a21\u578b\u4f1a\u81ea\u52a8\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u76ee\u6807\uff0c\u8f93\u51fa\u76ee\u6807\u7c7b\u522b\u3001\u7f6e\u4fe1\u5ea6\u3001\u8fb9\u754c\u6846\u5750\u6807<\/strong>\uff0c\u5e76\u751f\u6210\u5e26\u6807\u6ce8\u7684\u53ef\u89c6\u5316\u7ed3\u679c\u56fe\uff0c\u540c\u65f6\u5728\u7a97\u53e3\u4e2d\u5b9e\u65f6\u5c55\u793a\u68c0\u6d4b\u6548\u679c\u3002<\/p>\n\n\n\n
import cv2\nimport os\nfrom ultralytics import YOLO\n\ndef test_png():\n    model_path = \".\/weights\/bch.pt\"\n    image_path = \".\/images\/4.png\"\n    output_dir = \".\/runs\/test_png\"\n\n    os.makedirs(output_dir, exist_ok=True)\n\n    if not os.path.exists(image_path):\n        raise FileNotFoundError(f\"\u56fe\u7247\u4e0d\u5b58\u5728 {os.path.abspath(image_path)}\")\n    if not os.path.exists(model_path):\n        raise FileNotFoundError(f\"\u6a21\u578b\u4e0d\u5b58\u5728 {os.path.abspath(model_path)}\")\n\n    model = YOLO(model_path)\n    print(f\"\u6a21\u578b\u52a0\u8f7d\u6210\u529f\uff1a {os.path.abspath(model_path)}\")\n\n    detector_params = {\n        \"conf\": 0.5,\n        \"iou\": 0.45,\n        \"device\": \"cpu\",\n    }\n\n    print(f\"\\n\u6b63\u5728\u68c0\u6d4b\u56fe\u7247\uff1a {os.path.abspath(image_path)}\")\n    result = model(image_path, **detector_params)\n\n    for result in result:\n        img_name = os.path.basename(image_path)\n        output_path = os.path.join(output_dir, f\"detected_{img_name}\")\n        result.save(output_path)\n\n        annotated_img = result.plot(\n            labels = True,\n            conf = True,\n            line_width = 2,\n\n        )\n\n        display_img = annotated_img\n        cv2.imshow(\"result\", display_img)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n\n        boxes = result.boxes\n        print(f\"\\n\u56fe\u7247\u4e2d\u68c0\u6d4b\u5230{len(boxes)}\u4e2a\u76ee\u6807\")\n        for i , box in enumerate(boxes):\n            cls_id = int(box.cls[0])\n            cls_name  = model.names[cls_id]\n            conf = float(box.conf[0])\n            xyxy = box.xyxy[0].tolist()\n\n            print(f\"\u76ee\u6807{i+1}\")\n\n            print(f\"\u7c7b\u522b\uff1a{cls_name}\")\n            print(f\"\u7f6e\u4fe1\u5ea6\uff1a{conf:.2f} ({conf*100:.2f}%)\")\n\n        print(f\"\\n\u6807\u6ce8\u7ed3\u679c\u5df2\u4fdd\u5b58\u81f3\uff1a{os.path.abspath(output_path)}\")\n    print(\"\\n\u68c0\u6d4b\u5b8c\u6210\")\n\nif __name__ == '__main__':\n    test_png()\n\n<\/code><\/pre>\n\n\n\n
\u9664\u4e86\u5bf9\u5355\u5f20\u56fe\u7247\u8fdb\u884c\u68c0\u6d4b\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u5229\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\uff0c\u5bf9 ** \u6574\u4e2a\u6d4b\u8bd5\u96c6\uff08test set\uff09** \u8fdb\u884c\u6279\u91cf\u81ea\u52a8\u5316\u8bc4\u4f30\u3002\u901a\u8fc7\u8bfb\u53d6 data.yaml<\/code> \u914d\u7f6e\u6587\u4ef6\uff0c\u6a21\u578b\u81ea\u52a8\u52a0\u8f7d\u6d4b\u8bd5\u96c6\u4e2d\u7684\u6240\u6709\u56fe\u7247\uff0c\u6279\u91cf\u5b8c\u6210\u76ee\u6807\u68c0\u6d4b\u3002<\/p>\n\n\n\n
\u8be5\u8fc7\u7a0b\u4e0d\u4ec5\u4f1a\u8f93\u51fa\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001mAP<\/strong>\u7b49\u91cf\u5316\u6307\u6807\uff0c\u8fd8\u4f1a\u81ea\u52a8\u4fdd\u5b58\u6240\u6709\u56fe\u7247\u7684\u68c0\u6d4b\u6807\u6ce8\u7ed3\u679c\uff0c\u5b9e\u73b0\u5bf9\u6a21\u578b\u6574\u4f53\u6027\u80fd\u7684\u5168\u9762\u7edf\u8ba1\u4e0e\u8bc4\u4f30\u3002\u540c\u65f6\u4e5f\u53ef\u6279\u91cf\u5bf9\u5927\u91cf\u56fe\u7247<\/strong>\u8fdb\u884c\u81ea\u52a8\u63a8\u7406\uff0c\u7edf\u8ba1\u5404\u7c7b\u76ee\u6807\u7684\u68c0\u6d4b\u6570\u91cf\u3001\u8bc6\u522b\u51c6\u786e\u7387\u3001\u76ee\u6807\u5206\u5e03\u60c5\u51b5\u7b49\u6570\u636e\uff0c\u5b9e\u73b0\u89c4\u6a21\u5316\u56fe\u50cf\u667a\u80fd\u5206\u6790\u4e0e\u6570\u636e\u7edf\u8ba1\u3002<\/p>\n\n\n\n
import os\nimport yaml\nfrom ultralytics import YOLO\n\ndef test_sets():\n    # 1. \u914d\u7f6e\u8def\u5f84\n    project_root = \".\"  # \u9879\u76ee\u6839\u76ee\u5f55\n    model_path = os.path.join(project_root, \"weights\", 'bch.pt')  # \u6700\u4f18\u6a21\u578b\n    data_config_path = os.path.join(project_root, 'data.yaml')   # \u6570\u636e\u96c6\u914d\u7f6e\n    result_save_path = os.path.join(project_root, 'runs', 'test_sets')  # \u7ed3\u679c\u4fdd\u5b58\n\n    # \u521b\u5efa\u7ed3\u679c\u4fdd\u5b58\u6587\u4ef6\u5939\n    os.makedirs(result_save_path, exist_ok=True)\n\n    # 2. \u8bfb\u53d6\u914d\u7f6e\uff0c\u83b7\u53d6\u6d4b\u8bd5\u96c6\u8def\u5f84\n    with open(data_config_path, 'r', encoding='utf-8') as f:\n        data_config = yaml.safe_load(f)\n        # \u81ea\u52a8\u62fc\u63a5\u6d4b\u8bd5\u96c6\u56fe\u7247\u8def\u5f84\n        test_dir = os.path.join(data_config.get('path', '.'), data_config.get('test', \"test\/images\"))\n        print(f\"\u6d4b\u8bd5\u96c6\u5b9e\u9645\u8def\u5f84\uff1a{test_dir}\")\n\n        if not os.path.exists(test_dir):\n            raise FileNotFoundError(f\"\u6d4b\u8bd5\u96c6\u4e0d\u5b58\u5728\uff1a{test_dir}\")\n\n    # 3. \u52a0\u8f7d\u8bad\u7ec3\u597d\u7684\u6a21\u578b\n    if not os.path.exists(model_path):\n        raise FileNotFoundError(f\"\u6a21\u578b\u4e0d\u5b58\u5728\uff1a{model_path}\")\n    model = YOLO(model_path)\n    print(f\"\u6a21\u578b\u52a0\u8f7d\u5b8c\u6210\uff1a{model_path}\")\n\n    # 4. \u6279\u91cf\u8bc4\u4f30\u53c2\u6570\u914d\u7f6e\n    val_params = {\n        \"data\": data_config_path,    # \u6570\u636e\u96c6\u914d\u7f6e\n        \"split\": \"val\",              # \u8bc4\u4f30\u96c6\uff1aval \u9a8c\u8bc1\u96c6 \/ test \u6d4b\u8bd5\u96c6\n        \"device\": \"cpu\",             # \u4f7f\u7528 CPU\n        \"save\": True,                # \u4fdd\u5b58\u68c0\u6d4b\u540e\u7684\u56fe\u7247\n        \"save_dir\": result_save_path,# \u7ed3\u679c\u4fdd\u5b58\u76ee\u5f55\n        \"plots\": True,               # \u751f\u6210\u8bc4\u4f30\u66f2\u7ebf\n        \"verbose\": True,             # \u6253\u5370\u8be6\u7ec6\u65e5\u5fd7\n    }\n\n    # 5. \u542f\u52a8\u6279\u91cf\u8bc4\u4f30\n    metrics = model.val(**val_params)\n    print(\"\u6d4b\u8bd5\u96c6\u6279\u91cf\u8bc4\u4f30\u5b8c\u6210\uff01\")\n\nif __name__ == '__main__':\n    test_sets()<\/code><\/pre>\n\n\n\n
\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6a21\u578b\u4f1a\u81ea\u52a8\u751f\u6210\u4e00\u7cfb\u5217\u8bc4\u4f30\u6587\u4ef6\uff0c\u5e2e\u52a9\u6211\u4eec\u4ece\u91cf\u5316\u4e0e\u76f4\u89c2\u4e24\u4e2a\u7ef4\u5ea6\u5224\u65ad\u6a21\u578b\u6027\u80fd\u3002\u6027\u80fd\u6307\u6807\u66f2\u7ebf\u5305\u62ec\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001PR \u66f2\u7ebf\u4e0e F1 \u5206\u6570\u66f2\u7ebf\uff0c\u6e05\u6670\u53cd\u6620\u6a21\u578b\u5728\u4e0d\u540c\u7f6e\u4fe1\u5ea6\u4e0b\u7684\u68c0\u6d4b\u80fd\u529b\uff1b\u6df7\u6dc6\u77e9\u9635\u5219\u80fd\u5e2e\u52a9\u6211\u4eec\u5b9a\u4f4d\u7c7b\u522b\u8bc6\u522b\u4e2d\u7684\u6df7\u6dc6\u95ee\u9898\u3002\u540c\u65f6\uff0c\u6a21\u578b\u4f1a\u8f93\u51fa\u9a8c\u8bc1\u96c6\u7684\u771f\u5b9e\u6807\u6ce8\u56fe\u4e0e\u9884\u6d4b\u7ed3\u679c\u5bf9\u6bd4\u56fe\uff0c\u76f4\u89c2\u5c55\u793a\u6a21\u578b\u5bf9\u56fe\u50cf\u76ee\u6807\u7684\u68c0\u6d4b\u6548\u679c\uff0c\u4e3a\u540e\u7eed\u53c2\u6570\u8c03\u6574\u4e0e\u6570\u636e\u4f18\u5316\u63d0\u4f9b\u660e\u786e\u4f9d\u636e\u3002<\/p>\n\n\n\n
\u6838\u5fc3\u66f2\u7ebf\u6587\u4ef6<\/h3>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u6df7\u6dc6\u77e9\u9635\u6587\u4ef6<\/h3>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
YOLO\u5377\u79ef\u7f51\u7edc\u4f18\u5316<\/h1>\n\n\n\n
<\/p>\n\n\n\n
YOLOv11-GSF<\/h1>\n\n\n\n
<\/p>\n\n\n\n
YOLO \u6a21\u578b\u5e38\u7528\u4e13\u4e1a\u672f\u8bed\u901a\u4fd7\u89e3\u91ca<\/h1>\n\n\n\n
1. \u9c81\u68d2\u6027<\/h2>\n\n\n\n
\u542b\u4e49<\/strong>\uff1a\u6a21\u578b\u5728\u590d\u6742\u3001\u5e72\u6270\u3001\u672a\u77e5\u573a\u666f<\/strong>\u4e0b\u4f9d\u65e7\u80fd\u7a33\u5b9a\u51c6\u786e\u68c0\u6d4b\u7684\u80fd\u529b\u3002<\/p>\n\n\n\n
\u901a\u4fd7\u7406\u89e3<\/strong>\uff1a<\/p>\n\n\n\n
\u5149\u7ebf\u53d8\u6697\u3001\u906e\u6321\u76ee\u6807\u3001\u80cc\u666f\u6742\u4e71\u3001\u62cd\u6444\u89d2\u5ea6\u53d8\u5316\u3001\u56fe\u7247\u6a21\u7cca\u65f6\uff0c\u6a21\u578b\u4e0d\u8bef\u68c0\u3001\u4e0d\u6f0f\u68c0<\/strong>\uff0c\u4f9d\u7136\u80fd\u6b63\u5e38\u8bc6\u522b\uff0c\u5c31\u53eb\u9c81\u68d2\u6027\u5f3a\u3002<\/p>\n\n\n\n
\u4e3e\u4f8b<\/strong>\uff1a\u767d\u5929\u8bad\u7ec3\u7684\u6a21\u578b\uff0c\u665a\u4e0a\u6697\u5149\u4e0b\u4e5f\u80fd\u51c6\u786e\u8bc6\u522b\u4eba\u7269\uff0c\u8bf4\u660e\u9c81\u68d2\u6027\u597d\u3002<\/p>\n\n\n\n
2. \u9884\u8bad\u7ec3\u6743\u91cd<\/h2>\n\n\n\n
\u5b98\u65b9\u5728\u5927\u578b\u901a\u7528\u6570\u636e\u96c6\u4e0a\u63d0\u524d\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u53c2\u6570\uff0c\u4e0d\u7528\u4ece\u96f6\u5f00\u59cb\u8bad\u7ec3\uff0c\u62ff\u6765\u8fc1\u79fb\u5b66\u4e60<\/strong>\u5fae\u8c03\u5373\u53ef\uff0c\u6536\u655b\u66f4\u5feb\u3001\u7cbe\u5ea6\u66f4\u9ad8\u3002<\/p>\n\n\n\n
3. \u5fae\u8c03\u8bad\u7ec3<\/h2>\n\n\n\n
\u57fa\u4e8e\u9884\u8bad\u7ec3\u6743\u91cd<\/strong>\uff0c\u5728\u81ea\u5df1\u7684\u5c0f\u4f17\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u4e0a\u7ee7\u7eed\u8bad\u7ec3\uff0c\u8ba9\u6a21\u578b\u5b66\u4f1a\u8bc6\u522b\u4e13\u5c5e\u7c7b\u522b\uff0c\u4e0d\u7528\u4ece\u5934\u8bad\u7ec3\u5377\u79ef\u7f51\u7edc\u3002<\/p>\n\n\n\n
4. \u5377\u79ef\u795e\u7ecf\u7f51\u7edc CNN<\/h2>\n\n\n\n
\u4f9d\u9760\u5377\u79ef\u5c42<\/strong>\u63d0\u53d6\u56fe\u50cf\u7279\u5f81\uff08\u8fb9\u7f18\u3001\u7eb9\u7406\u3001\u8f6e\u5ed3\u3001\u6df1\u5c42\u8bed\u4e49\u7279\u5f81\uff09\uff0c\u662f YOLO \u6a21\u578b\u7684\u4e3b\u5e72\u7279\u5f81\u63d0\u53d6\u7f51\u7edc\u3002<\/p>\n\n\n\n
5. \u7279\u5f81\u63d0\u53d6<\/h2>\n\n\n\n
\u5377\u79ef\u7f51\u7edc\u9010\u5c42\u4ece\u56fe\u7247\u91cc\u63d0\u53d6\uff1a\u6d45\u5c42\uff08\u7ebf\u6761\u3001\u8fb9\u7f18\uff09\u2192\u4e2d\u5c42\uff08\u5c40\u90e8\u8f6e\u5ed3\uff09\u2192\u6df1\u5c42\uff08\u76ee\u6807\u6574\u4f53\u5f62\u6001\u3001\u8bed\u4e49\uff09\uff0c\u8ba9\u6a21\u578b\u770b\u61c2\u7269\u4f53\u662f\u4ec0\u4e48\u3002<\/p>\n\n\n\n
6. \u8fb9\u754c\u6846 Bounding Box<\/h2>\n\n\n\n
\u6a21\u578b\u9884\u6d4b\u51fa\u7684\u77e9\u5f62\u6846<\/strong>\uff0c\u6846\u51fa\u56fe\u7247\u4e2d\u76ee\u6807\u6240\u5728\u7684\u4f4d\u7f6e\u3002<\/p>\n\n\n\n
7. \u7f6e\u4fe1\u5ea6 Confidence<\/h2>\n\n\n\n
\u6a21\u578b\u9884\u6d4b\u7ed3\u679c\u7684\u53ef\u4fe1\u7a0b\u5ea6<\/strong>\uff0c\u53d6\u503c 0\uff5e1\u3002<\/p>\n\n\n\n
\u6bd4\u5982\u7f6e\u4fe1\u5ea6 0.85\uff0c\u4ee3\u8868\u6a21\u578b\u6709 85% \u7684\u628a\u63e1\u8ba4\u4e3a\u8fd9\u4e2a\u76ee\u6807\u662f\u67d0\u7c7b\u522b\u3002<\/p>\n\n\n\n
\u8bbe\u7f6econf=0.5<\/code>\uff0c\u53ea\u4fdd\u7559\u53ef\u4fe1\u5ea6 50% \u4ee5\u4e0a\u7684\u68c0\u6d4b\u7ed3\u679c\u3002<\/p>\n\n\n\n
8. \u4ea4\u5e76\u6bd4 IOU \/ NMS<\/h2>\n\n\n\n
    \n
  • IOU<\/strong>\uff1a\u4e24\u4e2a\u6846\uff08\u9884\u6d4b\u6846\u548c\u771f\u5b9e\u6846\uff09\u91cd\u53e0\u9762\u79ef\u7684\u6bd4\u503c\uff1b<\/li>\n\n\n\n
  • NMS \u975e\u6781\u5927\u503c\u6291\u5236<\/strong>\uff1a\u53bb\u6389\u540c\u4e00\u76ee\u6807\u91cd\u590d\u591a\u51fa\u7684\u68c0\u6d4b\u6846\uff0c\u4fdd\u7559\u7f6e\u4fe1\u5ea6\u6700\u9ad8\u7684\u4e00\u4e2a\u3002<\/li>\n<\/ul>\n\n\n\n
    9. \u7cbe\u786e\u7387 Precision<\/h2>\n\n\n\n
    \u9884\u6d4b\u51fa\u6765\u7684\u76ee\u6807\u91cc\uff0c\u771f\u6b63\u6b63\u786e\u7684\u6bd4\u4f8b<\/strong>\uff0c\u8861\u91cf\u6a21\u578b\u8bef\u68c0\u591a\u4e0d\u591a<\/strong>\u3002<\/p>\n\n\n\n
    10. \u53ec\u56de\u7387 Recall<\/h2>\n\n\n\n
    \u6240\u6709\u771f\u5b9e\u76ee\u6807\u4e2d\uff0c\u88ab\u6a21\u578b\u6210\u529f\u627e\u51fa\u6765\u7684\u6bd4\u4f8b<\/strong>\uff0c\u8861\u91cf\u6a21\u578b\u6f0f\u68c0\u591a\u4e0d\u591a<\/strong>\u3002<\/p>\n\n\n\n
    11. F1 \u5206\u6570<\/h2>\n\n\n\n
    \u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u7684\u7efc\u5408\u5e73\u5747\u503c<\/strong>\uff0c\u7528\u6765\u5e73\u8861\u8bef\u68c0\u548c\u6f0f\u68c0\uff0c\u5206\u6570\u8d8a\u9ad8\u6a21\u578b\u7efc\u5408\u8868\u73b0\u8d8a\u597d\u3002<\/p>\n\n\n\n
    12. mAP \u5e73\u5747\u7cbe\u5ea6<\/h2>\n\n\n\n
    \u76ee\u6807\u68c0\u6d4b\u6838\u5fc3\u8bc4\u4ef7\u6307\u6807<\/strong>\uff0c\u7efc\u5408\u6240\u6709\u7c7b\u522b\u7684\u8bc6\u522b\u7cbe\u5ea6\uff0c\u6570\u503c\u8d8a\u63a5\u8fd1 1\uff0c\u6a21\u578b\u6574\u4f53\u6027\u80fd\u8d8a\u5f3a\u3002<\/p>\n\n\n\n
    13. \u8fc7\u62df\u5408<\/h2>\n\n\n\n
    \u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u8868\u73b0\u6781\u597d<\/strong>\uff0c\u4f46\u5728\u9a8c\u8bc1\u96c6\u3001\u6d4b\u8bd5\u96c6\u8868\u73b0\u5f88\u5dee\uff1b\u53ea\u4f1a\u6b7b\u8bb0\u8bad\u7ec3\u56fe\u7247\uff0c\u6cdb\u5316\u80fd\u529b\u5dee<\/strong>\uff0c\u964c\u751f\u56fe\u7247\u8bc6\u522b\u6548\u679c\u5dee\u3002<\/p>\n\n\n\n
    14. \u6cdb\u5316\u80fd\u529b<\/h2>\n\n\n\n
    \u6a21\u578b\u5728\u4ece\u672a\u89c1\u8fc7\u7684\u65b0\u56fe\u7247\u3001\u65b0\u573a\u666f<\/strong>\u4e0a\uff0c\u4f9d\u7136\u80fd\u51c6\u786e\u68c0\u6d4b\u7684\u80fd\u529b\uff0c\u548c\u9c81\u68d2\u6027\u9ad8\u5ea6\u76f8\u5173\u3002<\/p>\n\n\n\n
    15. \u8fed\u4ee3 Epochs<\/h2>\n\n\n\n
    \u8bad\u7ec3\u8f6e\u6570\uff0c\u6bcf\u4e00\u8f6e epoch \u90fd\u4f1a\u628a\u6574\u4e2a\u6570\u636e\u96c6\u5b8c\u6574\u5b66\u4e60\u4e00\u904d\uff0c\u8f6e\u6570\u9002\u4e2d\u6a21\u578b\u6536\u655b\u6700\u4f73\u3002<\/p>\n\n\n\n
    16. Batch \u6279\u6b21<\/h2>\n\n\n\n
    \u4e00\u6b21\u9001\u5165\u6a21\u578b\u540c\u65f6\u8bad\u7ec3\u7684\u56fe\u7247\u6570\u91cf\uff0c\u7531\u7535\u8111\u663e\u5b58\u914d\u7f6e\u51b3\u5b9a\u5927\u5c0f\u3002<\/p>\n\n\n\n
    17. \u5f52\u4e00\u5316<\/h2>\n\n\n\n
    \u628a\u56fe\u7247\u5750\u6807\u3001\u5c3a\u5bf8\u6570\u503c\u538b\u7f29\u5230 0\uff5e1 \u4e4b\u95f4\uff0c\u7edf\u4e00\u8f93\u5165\u683c\u5f0f\uff0c\u65b9\u4fbf\u5377\u79ef\u7f51\u7edc\u8ba1\u7b97\u6536\u655b\u3002<\/p>\n\n\n\n
    18. \u524d\u5411\u4f20\u64ad \/ \u53cd\u5411\u4f20\u64ad<\/h2>\n\n\n\n
      \n
    • \u524d\u5411\u4f20\u64ad\uff1a\u56fe\u7247\u8f93\u5165\u7f51\u7edc\uff0c\u6a21\u578b\u7ed9\u51fa\u9884\u6d4b\u7ed3\u679c\uff1b<\/li>\n\n\n\n
    • \u53cd\u5411\u4f20\u64ad\uff1a\u8ba1\u7b97\u9884\u6d4b\u548c\u771f\u5b9e\u6807\u7b7e\u7684\u8bef\u5dee\uff0c\u81ea\u52a8\u8c03\u6574\u7f51\u7edc\u6743\u91cd<\/strong>\uff0c\u8ba9\u6a21\u578b\u8d8a\u6765\u8d8a\u51c6\u3002<\/li>\n<\/ul>\n\n\n\n
      19. \u6743\u91cd\u53c2\u6570<\/h2>\n\n\n\n
      \u5377\u79ef\u7f51\u7edc\u91cc\u53ef\u5b66\u4e60\u7684\u6570\u503c\uff0c\u8bad\u7ec3\u8fc7\u7a0b\u5c31\u662f\u4e0d\u65ad\u66f4\u65b0\u6743\u91cd<\/strong>\uff0c\u8ba9\u6a21\u578b\u5b66\u4f1a\u76ee\u6807\u7279\u5f81\u3002<\/p>\n\n\n\n
      20. \u6df7\u6dc6\u77e9\u9635<\/h2>\n\n\n\n
      \u76f4\u89c2\u7edf\u8ba1\uff1a\u6bcf\u4e00\u7c7b\u76ee\u6807\u88ab\u6b63\u786e\u8bc6\u522b\u3001\u88ab\u9519\u5206\u6210\u5176\u4ed6\u7c7b<\/strong>\u7684\u6570\u91cf\uff0c\u7528\u6765\u5206\u6790\u7c7b\u522b\u6df7\u6dc6\u3001\u8bc6\u522b\u9519\u8bef\u539f\u56e0\u3002<\/p>\n\n\n\n
      21.\u6a21\u578b\u540e\u7f00<\/h2>\n\n\n\n
      1. \u5b57\u6bcd\u542b\u4e49\uff08\u5c3a\u5bf8\u4ece\u5c0f\u5230\u5927\uff09<\/h2>\n\n\n\n
        \n
      • n = Nano<\/strong>\uff1a\u6781\u5c0f\u3001\u6700\u8f7b\u91cf<\/li>\n\n\n\n
      • s = Small<\/strong>\uff1a\u5c0f\u578b\u3001\u8f7b\u91cf\u5747\u8861<\/li>\n\n\n\n
      • m = Medium<\/strong>\uff1a\u4e2d\u578b\u3001\u5e73\u8861\u7cbe\u5ea6\/\u901f\u5ea6<\/li>\n\n\n\n
      • l = Large<\/strong>\uff1a\u5927\u578b\u3001\u9ad8\u7cbe\u5ea6<\/li>\n\n\n\n
      • x = Extra Large<\/strong>\uff1a\u8d85\u5927\u3001\u6700\u9ad8\u7cbe\u5ea6<\/li>\n<\/ul>\n\n\n\n
        2. \u6838\u5fc3\u533a\u522b\uff08\u4e00\u773c\u770b\u61c2\uff09<\/h2>\n\n\n\n
        YOLOv8 \u5178\u578b\u5bf9\u6bd4\uff08\u8f93\u5165 640\uff09<\/h3>\n\n\n\n
        \u6a21\u578b<\/th>\u53c2\u6570\u91cf<\/th>\u8ba1\u7b97\u91cfGFLOPs<\/th>COCO AP<\/th>\u901f\u5ea6(CPU)<\/th>\u9002\u5408\u573a\u666f<\/th><\/tr><\/thead>
        n<\/strong><\/td>~1.9M<\/td>8.7<\/td>~37%<\/td>\u6700\u5feb<\/td>\u624b\u673a\u3001\u6811\u8393\u6d3e\u3001\u4f4e\u529f\u8017\u8fb9\u7f18<\/td><\/tr>
        s<\/strong><\/td>~3.4M<\/td>25.4<\/td>~44%<\/td>\u5f88\u5feb<\/td>\u5d4c\u5165\u5f0f\u3001\u6444\u50cf\u5934\u3001\u5b9e\u65f6\u68c0\u6d4b<\/td><\/tr>
        m<\/strong><\/td>~7.7M<\/td>70.0<\/td>~50%<\/td>\u4e2d\u7b49<\/td>PC\u3001\u8fb9\u7f18GPU\u3001\u901a\u7528\u573a\u666f<\/td><\/tr>
        l<\/strong><\/td>~16.2M<\/td>162.0<\/td>~54%<\/td>\u8f83\u6162<\/td>\u670d\u52a1\u5668\u3001\u9ad8\u7cbe\u51c6\u9700\u6c42<\/td><\/tr>
        x<\/strong><\/td>~32.5M<\/td>317.0<\/td>~57%<\/td>\u6700\u6162<\/td>\u4e91\u7aef\u3001\u6781\u81f4\u7cbe\u5ea6<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
          \n
        • \u89c4\u5f8b\uff1an \u2192 x\uff0c\u8d8a\u6765\u8d8a\u5927\u3001\u8d8a\u6765\u8d8a\u51c6\u3001\u8d8a\u6765\u8d8a\u6162\u3001\u663e\u5b58\u5360\u7528\u8d8a\u6765\u8d8a\u9ad8<\/strong>\u3002<\/li>\n<\/ul>\n\n\n\n
          3. \u7ed3\u6784\u4e0a\u7684\u672c\u8d28\u5dee\u5f02<\/h2>\n\n\n\n
          \u540c\u4e00\u7cfb\u5217\uff08\u5982 YOLOv8\uff09\u7684 n\/s\/m\/l\/x \u4e3b\u5e72\/Neck\/Head \u67b6\u6784\u5b8c\u5168\u4e00\u6837<\/strong>\uff0c\u53ea\u662f\uff1a<\/p>\n\n\n\n
            \n
          • \u5bbd\u5ea6\u7f29\u653e<\/strong>\uff1a\u5377\u79ef\u901a\u9053\u6570\u4f9d\u6b21\u53d8\u591a<\/li>\n\n\n\n
          • \u6df1\u5ea6\u7f29\u653e<\/strong>\uff1a\u5377\u79ef\u5c42\u6570\u91cf\u4f9d\u6b21\u53d8\u591a<\/li>\n\n\n\n
          • \u7ed3\u679c\uff1a\u53c2\u6570 & \u8ba1\u7b97\u91cf\u9010\u7ea7\u4e0a\u5347<\/strong>\u3002<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
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