{"id":117449,"date":"2026-10-24T22:17:01","date_gmt":"2026-10-24T14:17:01","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/117449.html"},"modified":"2026-10-24T22:17:01","modified_gmt":"2026-10-24T14:17:01","slug":"%e7%bb%99%e5%90%af%e5%85%83%e6%8b%bf%e8%a1%a3%e6%9c%8d%e6%8a%80%e8%83%bd%e6%8e%a5%e4%b8%8a%e7%9c%bc%e7%9d%9b%ef%bc%9a5070-ti-%e6%9c%ac%e5%9c%b0%e8%a7%86%e8%a7%89%e5%ae%9e%e6%88%98","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/117449.html","title":{"rendered":"\u7ed9\u542f\u5143\u201c\u62ff\u8863\u670d\u201d\u6280\u80fd\u63a5\u4e0a\u773c\u775b\uff1a5070 Ti \u672c\u5730\u89c6\u89c9\u5b9e\u6218"},"content":{"rendered":"

\u201c\u628a\u8863\u670d\u62ff\u5230\u6d17\u8863\u673a\u65c1\u201d，\u542c\u8d77\u6765\u662f\u4e00\u53e5\u6307\u4ee4，\u843d\u5230\u4ee3\u7801\u91cc\u5374\u662f\u4e00\u4e32\u6761\u4ef6：\u5148\u627e\u5230\u4eba，\u7b49\u8863\u7269\u4ea4\u63a5，\u518d\u627e\u5230\u6d17\u8863\u673a，\u6700\u540e\u624d\u662f\u79fb\u52a8\u548c\u653e\u4e0b\u3002<\/p>\n

\u6211\u7ffb\u4e86\u542f\u5143\u5f00\u6e90\u7ec4\u7ec7\u91cc\u7684 carry-clothes \u9879\u76ee\u3002\u4efb\u52a1\u6d41\u7a0b\u5df2\u7ecf\u5199\u597d，\u89c6\u89c9\u63a5\u53e3\u4e5f\u7559\u597d\u4e86，\u4f46\u9ed8\u8ba4\u7684 HeuristicDetector \u8fd4\u56de\u7a7a\u5217\u8868；–sim \u4f7f\u7528\u7684 SimDetector \u5219\u4ece\u6a21\u62df\u4e16\u754c\u8bfb\u53d6\u76ee\u6807\u4fe1\u606f，\u5e76\u6ca1\u6709\u771f\u7684\u770b\u56fe\u3002<\/p>\n

\u8fd9\u6b21\u5c31\u8865\u5176\u4e2d\u4e00\u5757：\u8ba9\u771f\u5b9e\u6a21\u578b\u770b\u7167\u7247，\u628a\u7ed3\u679c\u9001\u8fdb\u539f\u9879\u76ee\u7684\u201c\u5bfb\u627e\u6d17\u8863\u673a\u201d\u5206\u652f\u3002 \u63a8\u7406\u8dd1\u5728\u4e00\u53f0 RTX 5070 Ti \u4e0a，\u52a8\u4f5c\u7aef\u6cbf\u7528 Mock，\u4e0d\u8fde\u63a5\u771f\u673a\u3002\u8fd9\u6837\u53ef\u4ee5\u628a\u201c\u6a21\u578b\u6709\u6ca1\u6709\u770b\u89c1\u201d\u548c\u201c\u6280\u80fd\u6709\u6ca1\u6709\u63a5\u53d7\u201d\u653e\u5728\u540c\u4e00\u6761\u65e5\u5fd7\u91cc\u68c0\u67e5\u3002<\/p>\n

\"\u672c\u5730\u6a21\u578b\u8bc6\u522b\u6d17\u8863\u673a\"<\/p>\n

\u4e24\u5f20\u516c\u5f00\u5b9e\u62cd\u7167\u7247\u4e0a\u7684\u5b9e\u9645\u9884\u6d4b\u6846\u3002\u5de6\u56fe：Adrian Pingstone，\u516c\u6709\u9886\u57df；\u53f3\u56fe：Fanti Salms，CC BY-SA 3.0\u3002\u672c\u56fe\u505a\u4e86\u7f29\u653e\u3001\u65b9\u5411\u6821\u6b63\u548c\u68c0\u6d4b\u6846\u6807\u6ce8，\u884d\u751f\u56fe\u6309 CC BY-SA 3.0 \u63d0\u4f9b\u3002<\/p>\n

1. \u4ece\u4e00\u4e2a\u80fd\u66ff\u6362\u7684\u63a5\u53e3\u5f00\u59cb<\/h3>\n

\u542f\u5143\u7684\u793a\u4f8b\u9762\u5411 T1，\u89c6\u89c9\u5165\u53e3\u5728 src\/vision.py：\u8f93\u5165 JPEG \u5b57\u8282，\u8f93\u51fa Detection \u5217\u8868\u3002_s7_find_washer() \u8d1f\u8d23\u5bfb\u627e\u6d17\u8863\u673a：\u53d6\u56fe\u3001\u8c03\u7528\u68c0\u6d4b\u5668\u3001\u68c0\u67e5\u7c7b\u522b\u548c\u5206\u6570，\u627e\u5230\u5c31\u8fd4\u56de，\u627e\u4e0d\u5230\u7ee7\u7eed\u641c\u7d22，\u8d85\u65f6\u5219\u629b\u51fa TaskAborted\u3002<\/p>\n

\u56e0\u6b64\u4e0d\u7528\u91cd\u5199\u6574\u5957\u4efb\u52a1\u3002\u672c\u6b21\u53ea\u5b9e\u73b0\u68c0\u6d4b\u5668\u3001\u63a5\u5165\u8fd9\u4e00\u4e2a\u9636\u6bb5，\u5e76\u7ed9\u5b83\u52a0\u8fde\u7eed\u786e\u8ba4\u3002\u63a5\u4eba\u3001\u62b1\u8863\u7269\u548c\u5b9e\u9645\u5bfc\u822a\u4e0d\u5728\u8fd9\u6b21\u5b9e\u9a8c\u91cc\u3002<\/p>\n

\u6a21\u578b\u9009\u7528 OWL-ViT base-patch32\u3002\u5b83\u53ef\u4ee5\u6839\u636e\u6587\u5b57\u67e5\u8be2\u8fd4\u56de\u76ee\u6807\u6846，\u672c\u6587\u56fa\u5b9a\u67e5\u8be2：<\/p>\n

prompt =<\/span> "a photo of a washing machine"<\/span><\/p>\n

\u8fd9\u91cc\u7528\u7684\u662f\u76ee\u6807\u68c0\u6d4b\u6a21\u578b，\u4e0d\u9700\u8981\u8ba9\u804a\u5929\u6a21\u578b\u5148\u5199\u4e00\u6bb5\u89e3\u91ca，\u518d\u4ece\u56de\u7b54\u91cc\u731c\u5750\u6807\u3002\u8bc6\u522b\u7c7b\u522b\u4e5f\u660e\u786e\u9650\u5b9a\u4e3a\u6d17\u8863\u673a，\u907f\u514d\u628a\u522b\u7684\u9ad8\u5206\u76ee\u6807\u4ea4\u7ed9\u540e\u9762\u7684\u4f3a\u670d\u5668\u3002<\/p>\n

\u5b8c\u6574\u4ee3\u7801\u653e\u5728\u5b9e\u9a8c\u4ed3\u5e93\u7684 vision-skill \u76ee\u5f55\u3002\u590d\u73b0\u547d\u4ee4：<\/p>\n

git<\/span> clone https:\/\/atomgit.com\/gcw_JBmPZT05\/primebot-mujoco-lab.git
\ncd<\/span> primebot-mujoco-lab\/vision-skill
\nbash<\/span> code\/setup.sh<\/p>\n

# \u5355\u5f20\u56fe\u7247：\u4fdd\u5b58\u68c0\u6d4b\u6846，\u5e76\u5728\u7ec8\u7aef\u6253\u5370\u5206\u6570\u3001\u5750\u6807\u548c\u8017\u65f6<\/span>
\n.venv\/bin\/python code\/infer.py assets\/washer_normal.jpg<\/p>\n

# \u56fe\u7247\u5bf9\u7167\u3001\u6280\u80fd\u5206\u652f\u3001\u5f02\u6b65\u6307\u4ee4\u5faa\u73af<\/span>
\n.venv\/bin\/python code\/probe.py
\n.venv\/bin\/python code\/skill_demo.py
\n.venv\/bin\/python code\/loop_demo.py<\/p>\n

\u73af\u5883\u662f Ubuntu 22.04\u3001Python 3.11.16\u3001PyTorch 2.7.1+cu128\u3001Transformers 4.51.3\u3002\u811a\u672c\u56fa\u5b9a\u4e86\u542f\u5143\u6e90\u7801\u548c\u6a21\u578b\u7684\u63d0\u4ea4\u7248\u672c；\u6a21\u578b\u4e0b\u8f7d\u540e\u4ece\u672c\u5730\u52a0\u8f7d\u3002\u6ca1\u6709 ROS2，\u4e5f\u4e0d\u7528\u4e91\u7aef\u63a8\u7406 API\u3002\u521b\u5efa\u865a\u62df\u73af\u5883\u9700\u8981 uv \u6216\u53ef\u7528\u7684 Python venv\u3002<\/p>\n

2. \u6846\u627e\u5230\u4e86，\u5750\u6807\u5355\u4f4d\u522b\u63a5\u9519<\/h3>\n

\u6a21\u578b\u8f93\u51fa\u7684\u662f\u50cf\u7d20\u5750\u6807 [x1, y1, x2, y2]\u3002\u63a5\u5165\u65f6\u8981\u8f6c\u6210\u793a\u4f8b\u6240\u9700\u7684\u5b57\u6bb5：<\/p>\n

Detection(<\/span>
\n label=<\/span>"washing_machine"<\/span>,<\/span>
\n confidence=<\/span>score,<\/span> # \u4fdd\u7559\u6a21\u578b\u539f\u59cb\u5206\u6570<\/span>
\n cx=<\/span>(<\/span>x1 +<\/span> x2)<\/span> \/<\/span> width –<\/span> 1<\/span>,<\/span>
\n cy=<\/span>1<\/span> –<\/span> (<\/span>y1 +<\/span> y2)<\/span> \/<\/span> height,<\/span>
\n height=<\/span>y2 –<\/span> y1,<\/span> # \u8fd9\u91cc\u4f20\u50cf\u7d20\u9ad8\u5ea6<\/span>
\n)<\/span><\/p>\n

cx \u4ee5\u753b\u9762\u4e2d\u5fc3\u4e3a\u96f6，\u53f3\u4fa7\u4e3a\u6b63；cy \u5411\u4e0a\u4e3a\u6b63\u3002\u6a21\u578b\u8f93\u51fa\u7684\u8d8a\u754c\u6846\u5148\u88c1\u526a\u5230\u56fe\u50cf\u8303\u56f4，\u518d\u505a\u8f6c\u6362\u3002<\/p>\n

height \u662f\u8fd9\u6b21\u9047\u5230\u7684\u7b2c\u4e00\u4e2a\u5751\u3002 Detection \u7684\u5b57\u6bb5\u6ce8\u91ca\u5199\u7740\u201c\u5f52\u4e00\u5316\u6846\u9ad8\u5ea6\u201d，\u4f46 VisualServo.estimate() \u5b9e\u9645\u4f7f\u7528\u7684\u662f：<\/p>\n

distance =<\/span> focal_px_per_m *<\/span> target_height_m \/<\/span> detection.<\/span>height<\/p>\n

SimDetector \u81ea\u5df1\u4e5f\u4f20\u50cf\u7d20\u9ad8\u5ea6\u3002\u63a5\u5165\u65f6\u5e94\u4ee5\u5b9e\u9645\u8ba1\u7b97\u4e3a\u51c6，\u4e0d\u80fd\u53ea\u7167\u5b57\u6bb5\u6ce8\u91ca\u5199\u3002<\/p>\n

\u4ee5\u56fe A \u4e3a\u4f8b，\u6a21\u578b\u6846\u9ad8\u7ea6 581.7 \u50cf\u7d20\u3002\u6cbf\u7528\u793a\u4f8b\u4e2d\u7684\u7126\u8ddd\u6570\u503c 900 \u548c\u76ee\u6807\u9ad8\u5ea6 0.85，\u516c\u5f0f\u7ed3\u679c\u7ea6\u4e3a 1.32 \u7c73；\u5982\u679c\u8bef\u4f20 581.7 \/ 684，\u8ba1\u7b97\u7ed3\u679c\u4f1a\u5148\u81a8\u80c0\u5230\u7ea6 900 \u7c73，\u518d\u88ab\u4ee3\u7801\u9650\u5e45\u5230 12 \u7c73\u3002<\/p>\n

\u8fd9\u53ea\u662f\u63a5\u53e3\u7b97\u4f8b，\u5e76\u975e\u7167\u7247\u7684\u771f\u5b9e\u8ddd\u79bb：\u7167\u7247\u7684\u76f8\u673a\u6ca1\u6709\u6309\u4e0a\u8ff0\u53c2\u6570\u6807\u5b9a\u3002\u672c\u6587\u56e0\u6b64\u53ea\u9a8c\u8bc1\u641c\u7d22\u548c\u8f6c\u5411\u6307\u4ee4，\u4e0d\u7528\u8fd9\u4e2a\u8ddd\u79bb\u9a71\u52a8\u524d\u8fdb\u3002<\/p>\n

3. \u906e\u6321\u4ee5\u540e，\u8c03\u4f4e\u95e8\u69db\u771f\u7684\u6709\u7528\u5417？<\/h3>\n

\u6211\u7ed9\u4e24\u5f20\u7167\u7247\u5206\u522b\u505a\u4e86\u4eae\u5ea6\u964d\u4f4e\u548c\u7070\u6761\u906e\u6321，\u518d\u52a0\u4e00\u5f20\u6ca1\u6709\u6d17\u8863\u673a\u7684 Q1 \u6e32\u67d3\u56fe\u4e0e\u7eaf\u7070\u56fe\u3002\u5171\u516b\u79cd\u8f93\u5165，\u7528\u6765\u68c0\u67e5\u63a5\u53e3\u884c\u4e3a，\u4e0d\u628a\u5b83\u4eec\u5f53\u6210\u516b\u4e2a\u72ec\u7acb\u573a\u666f\u7edf\u8ba1\u8bc6\u522b\u7387\u3002<\/p>\n\n\u8f93\u5165\u6700\u9ad8\u5019\u9009\u5206\u6570\u6309 0.5 \u95e8\u69db\u5904\u7406<\/tr>\n\n\n\n\n\n\n\n
\u56fe A \u539f\u56fe<\/td>\n0.679<\/td>\n\u901a\u8fc7<\/td>\n<\/tr>\n
\u56fe B \u539f\u56fe<\/td>\n0.757<\/td>\n\u901a\u8fc7<\/td>\n<\/tr>\n
\u56fe A \/ B \u4eae\u5ea6\u964d\u81f3 25%<\/td>\n0.720 \/ 0.697<\/td>\n\u901a\u8fc7<\/td>\n<\/tr>\n
\u56fe A \/ B \u52a0\u7070\u6761\u906e\u6321<\/td>\n0.240 \/ 0.167<\/td>\n\u62d2\u7edd<\/td>\n<\/tr>\n
\u65e0\u6d17\u8863\u673a\u7684\u673a\u5668\u4eba\u753b\u9762<\/td>\n0.015<\/td>\n\u62d2\u7edd<\/td>\n<\/tr>\n
\u7eaf\u7070\u753b\u9762<\/td>\n0.001<\/td>\n\u62d2\u7edd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\"\u8c03\u6697\u3001\u906e\u6321\u548c\u65e0\u76ee\u6807\u7684\u5bf9\u7167\"<\/p>\n

\u8c03\u6697\u540e\u5206\u6570\u6ca1\u6709\u4e0b\u964d，\u4e0d\u80fd\u636e\u6b64\u8bf4\u6697\u5149\u4e00\u5b9a\u66f4\u597d\u8bc6\u522b；\u5b83\u53ea\u8bf4\u660e\u8fd9\u4e24\u5f20\u53d8\u6362\u540e\u7684\u56fe\u7247\u4ecd\u80fd\u901a\u8fc7\u3002\u771f\u6b63\u8981\u76ef\u4f4f\u7684\u662f\u4e2d\u95f4\u7684\u906e\u6321\u56fe：\u6a21\u578b\u628a\u6d17\u8863\u673a\u4e0a\u534a\u622a\u5f53\u6210\u4e86\u76ee\u6807\u6846\u3002<\/p>\n

\u628a\u95e8\u69db\u4ece 0.5 \u964d\u5230 0.12，\u5b83\u786e\u5b9e\u80fd\u201c\u68c0\u6d4b\u6210\u529f\u201d\u3002\u4f46\u6846\u9ad8\u5df2\u7ecf\u7f29\u77ed\u5230\u7ea6 179.4 \u50cf\u7d20\u3002\u540c\u4e00\u5957\u516c\u5f0f\u5f97\u5230\u7684\u8ddd\u79bb，\u4ece 1.32 \u7c73\u53d8\u6210\u7ea6 4.26 \u7c73\u3002\u7269\u4f53\u5e76\u6ca1\u6709\u53d8\u8fdc，\u53ea\u662f\u6846\u53d8\u77ed\u4e86\u3002<\/p>\n

\u5206\u6570\u591f\u4e0d\u591f，\u4e0e\u6846\u80fd\u4e0d\u80fd\u7528\u4e8e\u6d4b\u8ddd，\u662f\u4e24\u4ef6\u4e8b\u3002 0.5 \u4e5f\u4e0d\u662f\u901a\u7528\u6b63\u786e\u503c，\u8fd9\u91cc\u4fdd\u7559\u793a\u4f8b\u95e8\u69db\u505a\u5bf9\u7167；0.12 \u4e13\u95e8\u7528\u4e8e\u66b4\u9732\u9519\u8bef\u653e\u884c，\u4e0d\u80fd\u5f53\u6210\u63a8\u8350\u53c2\u6570\u3002\u6a21\u578b\u5206\u6570\u66f4\u4e0d\u80fd\u76f4\u63a5\u89e3\u91ca\u4e3a\u73b0\u5b9e\u4e16\u754c\u4e2d\u7684\u6b63\u786e\u6982\u7387\u3002<\/p>\n

4. \u8ba9\u6280\u80fd\u4eb2\u81ea\u8dd1\u4e00\u6b21\u5224\u65ad<\/h3>\n

\u68c0\u6d4b\u5668\u5916\u9762\u52a0\u4e00\u4e2a\u5c0f\u95e8\u63a7：\u8fde\u7eed\u4e09\u6b21\u901a\u8fc7\u5206\u6570\u95e8\u69db，\u5e76\u4e14\u76f8\u90bb\u6846\u7684 IoU \u4e0d\u4f4e\u4e8e 0.5，\u624d\u628a\u7ed3\u679c\u4ea4\u7ed9\u6280\u80fd\u3002\u6ca1\u6709\u76ee\u6807\u5c31\u6e05\u96f6\u8ba1\u6570，\u4e14\u59cb\u7ec8\u4fdd\u7559\u539f\u59cb\u5206\u6570，\u4e0d\u628a\u4f4e\u5206\u6539\u5199\u6210\u9ad8\u5206\u3002<\/p>\n

\u63a5\u7740\u7528 PhotoBot \u7ee7\u627f\u539f\u9879\u76ee\u7684 MockPrimeBot，\u53ea\u66ff\u6362\u53d6\u56fe\u65b9\u6cd5，\u6309\u6307\u5b9a\u987a\u5e8f\u8fd4\u56de\u7167\u7247\u3002\u5b9e\u9645\u8c03\u7528\u7684\u4ecd\u662f\u539f\u6765\u7684 _s7_find_washer()；\u552f\u4e00\u7684\u4efb\u52a1\u6e90\u7801\u8c03\u6574，\u662f\u628a\u5199\u6b7b\u7684 0.5 \u63d0\u53d6\u6210\u914d\u7f6e\u9879，\u8865\u4e01\u4fdd\u5b58\u5728 results\/upstream.patch\u3002<\/p>\n

\u641c\u7d22\u8d85\u65f6\u8bbe\u4e3a 1.2 \u79d2；Mock \u7684\u6a21\u5f0f\u5207\u6362\u7b49\u5f85\u8bbe\u4e3a\u96f6，\u7f29\u77ed\u56de\u5f52\u6d4b\u8bd5\u3002\u7ed3\u679c\u5982\u4e0b：<\/p>\n\n\u6d4b\u8bd5\u5b9e\u9645\u7ed3\u679c<\/tr>\n\n\n\n\n\n\n\n
\u5360\u4f4d\u68c0\u6d4b\u5668 + \u6b63\u5e38\u7167\u7247<\/td>\n\u8d85\u65f6，\u629b\u51fa\u641c\u7d22\u5931\u8d25\u5f02\u5e38<\/td>\n<\/tr>\n
\u771f\u5b9e\u6a21\u578b：\u65e0\u76ee\u6807 \u2192 \u6b63\u5e38\u7167\u7247\u8fde\u7eed\u4e09\u6b21<\/td>\n\u7b2c 4 \u6b21\u53d6\u56fe\u540e，\u641c\u7d22\u6b63\u5e38\u8fd4\u56de<\/td>\n<\/tr>\n
\u59cb\u7ec8\u6ca1\u6709\u76ee\u6807<\/td>\n\u8d85\u65f6\u9000\u51fa<\/td>\n<\/tr>\n
\u6b63\u5e38\u7167\u7247\u3001\u65e0\u76ee\u6807\u753b\u9762\u4ea4\u66ff<\/td>\n\u786e\u8ba4\u8ba1\u6570\u53cd\u590d\u6e05\u96f6，\u8d85\u65f6\u9000\u51fa<\/td>\n<\/tr>\n
\u906e\u6321\u7167\u7247，\u95e8\u69db 0.5<\/td>\n\u8d85\u65f6\u9000\u51fa<\/td>\n<\/tr>\n
\u540c\u4e00\u906e\u6321\u7167\u7247，\u95e8\u69db 0.12<\/td>\n\u7b2c 3 \u6b21\u53d6\u56fe\u540e\u653e\u884c，\u4f46\u6846\u4e0d\u5b8c\u6574<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\"\u771f\u5b9e\u63a8\u7406\u7ed3\u679c\u5982\u4f55\u5f71\u54cd\u6280\u80fd\u5206\u652f\"<\/p>\n

\u8fd9\u91cc\u201c\u641c\u7d22\u6b63\u5e38\u8fd4\u56de\u201d\u8868\u793a\u627e\u5230\u76ee\u6807\u6761\u4ef6\u6210\u7acb，\u540e\u7eed\u624d\u4f1a\u8fdb\u5165\u524d\u5f80\u6d17\u8863\u673a\u7684\u9636\u6bb5，\u4e0d\u4ee3\u8868\u642c\u8fd0\u4efb\u52a1\u5df2\u7ecf\u5b8c\u6210\u3002\u6d4b\u8bd5\u8fd8\u68c0\u67e5\u4e86\u6bcf\u79cd\u5206\u652f\u6700\u540e\u90fd\u8c03\u7528\u505c\u6b62\u3001\u641c\u7d22\u8fc7\u7a0b\u4e2d\u524d\u8fdb\u901f\u5ea6\u59cb\u7ec8\u4e3a\u96f6\u3002<\/p>\n

\u6700\u540e\u4e00\u884c\u5c24\u5176\u6709\u610f\u601d：\u8fde\u7eed\u4e09\u5e27\u53ea\u80fd\u8fc7\u6ee4\u95ea\u70c1，\u6321\u4e0d\u4f4f\u8fde\u7eed\u4e09\u6b21\u90fd\u76f8\u540c\u7684\u9519\u8bef\u6846\u3002 \u91cd\u590d\u8f93\u5165\u9759\u6001\u7167\u7247，\u5c31\u662f\u4e3a\u4e86\u628a\u8fd9\u4e2a\u95ee\u9898\u5355\u72ec\u66b4\u9732\u51fa\u6765\u3002<\/p>\n

5. \u6a21\u578b\u8dd1\u5f97\u633a\u5feb，\u4e3a\u4ec0\u4e48\u5faa\u73af\u8fd8\u662f\u6162\u4e86？<\/h3>\n

\u5728 5070 Ti \u4e0a，\u4ee5 FP32\u3001batch size 1 \u8fd0\u884c，\u9884\u70ed 5 \u6b21，\u518d\u5bf9\u56fe A \u91cd\u590d\u63a8\u7406 40 \u6b21：\u5b8c\u6574 detect() \u7684\u4e2d\u4f4d\u8017\u65f6\u4e3a 34.71 ms，P95 \u4e3a 35.27 ms\u3002\u8ba1\u65f6\u5305\u542b\u56fe\u7247\u89e3\u7801\u3001\u9884\u5904\u7406\u3001GPU \u63a8\u7406\u548c\u6846\u540e\u5904\u7406，\u5e76\u5728\u4e24\u7aef\u540c\u6b65 CUDA；\u4e0d\u542b\u6a21\u578b\u52a0\u8f7d\u548c\u6587\u4ef6\u8bfb\u53d6\u3002\u5355\u5f20\u56fe\u7247 CLI \u7684\u9996\u6b21\u63a8\u7406\u4e3a 391.55 ms，\u542f\u52a8\u9636\u6bb5\u4ecd\u8981\u5355\u72ec\u9884\u70ed\u3002<\/p>\n

PyTorch \u8bb0\u5f55\u7684\u5cf0\u503c\u5df2\u5206\u914d\u663e\u5b58\u7ea6 644 MiB，\u8fd9\u4e0d\u7b49\u4e8e nvidia-smi \u663e\u793a\u7684\u6574\u8fdb\u7a0b\u5360\u7528\u3002\u8fd9\u4e2a\u5c0f\u6a21\u578b\u7528\u4e0d\u4e0a 16 GB \u663e\u5b58\u7684\u5927\u90e8\u5206\u7a7a\u95f4\u3002<\/p>\n

\u4f46\u793a\u4f8b\u641c\u7d22\u5faa\u73af\u5e0c\u671b\u6bcf 20 ms \u4e0b\u53d1\u4e00\u6b21\u6307\u4ee4\u3002\u628a 35 ms \u7684\u63a8\u7406\u540c\u6b65\u585e\u8fdb\u53bb，\u54ea\u6015\u4e0d\u518d sleep，\u4e5f\u8d76\u4e0d\u4e0a\u539f\u5b9a\u9891\u7387\u3002\u76ee\u6807\u7f3a\u5931\u7684\u90a3\u7ec4\u5b9e\u6d4b\u4e2d，\u76f8\u90bb\u641c\u7d22\u6307\u4ee4\u7684\u4e2d\u4f4d\u95f4\u9694\u662f 36.21 ms\u3002<\/p>\n

\u6211\u53e6\u5199\u4e86\u4e00\u4e2a\u72ec\u7acb\u7684 Mock \u5faa\u73af：\u63a8\u7406\u7ebf\u7a0b\u53ea\u53d1\u5e03\u6700\u65b0\u89c2\u6d4b，\u6307\u4ee4\u7ebf\u7a0b\u6309 50 Hz \u8bfb\u53d6；\u753b\u9762\u8d85\u8fc7 300 ms \u6ca1\u66f4\u65b0，\u6307\u4ee4\u5f52\u96f6\u3002<\/p>\n

# captured_at \u5728\u83b7\u53d6\u56fe\u50cf\u65f6\u8bb0\u5f55，\u4e0d\u80fd\u5728\u63a8\u7406\u5b8c\u6210\u540e\u624d\u5f00\u59cb\u8ba1\u65f6\u3002<\/span>
\nage =<\/span> time.<\/span>monotonic(<\/span>)<\/span> –<\/span> captured_at
\nif<\/span> age ><\/span> 0.30<\/span> or<\/span> not<\/span> confirmed_detections:<\/span>
\n yaw =<\/span> 0.0<\/span>
\nelse<\/span>:<\/span>
\n yaw =<\/span> max<\/span>(<\/span>–<\/span>0.25<\/span>,<\/span> min<\/span>(<\/span>0.25<\/span>,<\/span> confirmed_detections[<\/span>0<\/span>]<\/span>.<\/span>cx *<\/span> 0.4<\/span>)<\/span>)<\/span>
\nbot.<\/span>drive(<\/span>0.0<\/span>,<\/span> 0.0<\/span>,<\/span> yaw)<\/span><\/p>\n

\u5b8c\u6574\u811a\u672c\u6709\u9501\u4fdd\u62a4\u3001\u5f02\u5e38\u9000\u51fa\u548c finally: bot.stop()\u3002\u8fd9\u6bb5\u53ea\u6f14\u793a\u6c34\u5e73\u5bf9\u51c6，\u524d\u8fdb\u901f\u5ea6\u4e00\u76f4\u4e3a\u96f6；\u5b83\u662f\u72ec\u7acb\u9a8c\u8bc1\u7684\u6539\u9020\u65b9\u5f0f，\u6ca1\u6709\u58f0\u79f0\u6574\u4e2a\u4e0a\u6e38\u4efb\u52a1\u5df2\u5b8c\u6210\u5f02\u6b65\u6539\u9020\u3002<\/p>\n

\"\u540c\u6b65\u63a8\u7406\u3001\u72ec\u7acb\u6307\u4ee4\u5faa\u73af\u4e0e\u753b\u9762\u8fc7\u671f\u6d4b\u8bd5\"<\/p>\n

\u8fd9\u6b21\u8fd0\u884c 2 \u79d2，\u5171\u8bb0\u5f55 100 \u4e2a\u6307\u4ee4\u5468\u671f，\u4e2d\u4f4d\u95f4\u9694 20.00 ms，\u6700\u5927 21.91 ms\u3002\u8fd0\u884c\u5230 1.2 \u79d2\u65f6，\u6545\u610f\u505c\u6b62\u63d0\u4f9b\u65b0\u753b\u9762；\u7ea6 1.5 \u79d2\u540e，\u65e7\u89c2\u6d4b\u8fc7\u671f，\u8f93\u51fa\u5f52\u96f6\u3002\u8fd9\u91cc\u9a8c\u8bc1\u7684\u662f\u684c\u9762 Python \u4e0e Mock \u7684\u5b9e\u9645\u65f6\u5e8f，\u4e0d\u662f\u673a\u5668\u4eba\u63a7\u5236\u5668\u7684\u5b9e\u65f6\u6027\u4fdd\u8bc1\u3002<\/p>\n

6. \u6362\u6210\u81ea\u5df1\u7684\u7167\u7247\u8bd5\u4e00\u904d<\/h3>\n

\u5148\u522b\u52a8\u6a21\u578b\u548c\u9608\u503c，\u6362\u4e00\u5f20\u56fe\u6700\u76f4\u63a5：<\/p>\n

.venv\/bin\/python code\/infer.py \/path\/to\/your-photo.jpg \\\\<\/span>
\n –output<\/span> results\/my-photo.png<\/p>\n

\u5148\u770b\u6846\u6709\u6ca1\u6709\u5305\u4f4f\u6574\u53f0\u6d17\u8863\u673a，\u518d\u770b\u7c7b\u522b\u548c\u5206\u6570，\u6700\u540e\u624d\u628a\u5b83\u4ea4\u7ed9\u6280\u80fd\u3002\u63a5\u81ea\u5df1\u7684\u89c6\u9891\u6216\u76f8\u673a\u65f6，\u8fd8\u8981\u4fdd\u7559\u771f\u6b63\u7684\u53d6\u5e27\u65f6\u95f4；\u65ad\u6d41\u540e\u53cd\u590d\u8fd4\u56de\u540c\u4e00\u5f20\u65e7\u56fe，\u4e0d\u80fd\u7b97\u201c\u65b0\u753b\u9762\u201d\u3002<\/p>\n

\u8fd9\u6b21\u6700\u6709\u7528\u7684\u7ed3\u679c，\u5c31\u662f\u628a\u8fd9\u51e0\u4e2a\u68c0\u67e5\u7559\u5728\u4e86\u4ee3\u7801\u91cc：\u6846\u7684\u50cf\u7d20\u5355\u4f4d\u3001\u4e09\u5e27\u786e\u8ba4\u3001\u641c\u7d22\u8d85\u65f6\u548c\u89c2\u6d4b\u8fc7\u671f\u3002\u6362\u6a21\u578b\u65f6，\u6cbf\u7740\u8fd9\u6761\u94fe\u8def\u518d\u8dd1\u4e00\u6b21，\u6bd4\u53ea\u770b\u4e00\u5f20\u6f02\u4eae\u7684\u68c0\u6d4b\u622a\u56fe\u66f4\u5bb9\u6613\u53d1\u73b0\u95ee\u9898\u3002<\/p>\n

\u4ee3\u7801\u5165\u53e3：\u672c\u5730\u89c6\u89c9\u4e0e\u6280\u80fd\u5206\u652f\u5b9e\u9a8c；\u63a5\u53e3\u4f9d\u636e：\u542f\u5143 carry-clothes\u3001OWL-ViT \u6587\u6863\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u201c\u628a\u8863\u670d\u62ff\u5230\u6d17\u8863\u673a\u65c1\u201d，\u542c\u8d77\u6765\u662f\u4e00\u53e5\u6307\u4ee4，\u843d\u5230\u4ee3\u7801\u91cc\u5374\u662f\u4e00\u4e32\u6761\u4ef6：\u5148\u627e\u5230\u4eba，\u7b49\u8863\u7269\u4ea4\u63a5，\u518d\u627e\u5230\u6d17\u8863\u673a，\u6700\u540e\u624d\u662f\u79fb\u52a8\u548c\u653e\u4e0b\u3002
\n\u6211\u7ffb\u4e86\u542f\u5143\u5f00\u6e90\u7ec4\u7ec7\u91cc\u7684 carry-clothes \u9879\u76ee\u3002\u4efb\u52a1\u6d41\u7a0b\u5df2\u7ecf\u5199\u597d，\u89c6\u89c9\u63a5\u53e3\u4e5f\u7559\u597d\u4e86，\u4f46\u9ed8\u8ba4\u7684 HeuristicDetector \u8fd4\u56de\u7a7a\u5217\u8868；–sim \u4f7f\u7528\u7684 SimDetector \u5219\u4ece\u6a21\u62df\u4e16\u754c\u8bfb\u53d6\u76ee\u6807\u4fe1\u606f&#xf<\/p>\n","protected":false},"author":2,"featured_media":117445,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[13323,868,3747,384],"topic":[],"class_list":["post-117449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-13323","tag-868","tag-3747","tag-384"],"yoast_head":"\n\u7ed9\u542f\u5143\u201c\u62ff\u8863\u670d\u201d\u6280\u80fd\u63a5\u4e0a\u773c\u775b\uff1a5070 Ti \u672c\u5730\u89c6\u89c9\u5b9e\u6218 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/117449.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u7ed9\u542f\u5143\u201c\u62ff\u8863\u670d\u201d\u6280\u80fd\u63a5\u4e0a\u773c\u775b\uff1a5070 Ti \u672c\u5730\u89c6\u89c9\u5b9e\u6218 - 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