\n| \n \u2462 Ultralytics \u4e00\u952e\u5bfc\u51fa<\/p>\n<\/td>\n | \n \u5b98\u65b9\u5c01\u88c5 format="ascend"<\/p>\n<\/td>\n | \n \u4e00\u6761\u547d\u4ee4，\u6700\u7701\u4e8b<\/p>\n<\/td>\n | \n YOLOv8\/v10\/v11 \u5feb\u901f\u843d\u5730<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u4ece Ultralytics v8.4.107 \u8d77，\u5b98\u65b9\u539f\u751f\u652f\u6301\u6607\u817e\u5bfc\u51fa：\u5bfc\u51fa\u5668\u5148\u5199\u4e2d\u95f4 ONNX，\u518d\u81ea\u52a8\u8c03\u7528 ATC \u7f16\u8bd1，\u4ea7\u51fa\u542b\u00a0.om\u00a0\u7684\u72ec\u7acb\u6a21\u578b\u76ee\u5f55\u3002\u5b83\u652f\u6301\u7684\u76ee\u6807 SoC \u5305\u62ec\u00a0Ascend310P1 \/ Ascend310P3 \/ Ascend310B1 \/ Ascend310B4，\u4f8b\u5982\u00a0Ascend310B4\u00a0\u5bf9\u5e94 OrangePi AIPro（8T）\u4e0e Atlas 200I A2（8 TOPS），Ascend310P3\u00a0\u5bf9\u5e94 Atlas 300I Pro \/ 300V Pro\u3002<\/p>\n \u6ce8\u610f：\u9ed8\u8ba4 FP16\u6607\u817e AI Core \u53ea\u9ad8\u6548\u652f\u6301\u00a0DT_FLOAT16\u00a0\u4e0e\u00a0DT_INT8，\u5b98\u65b9\u5bfc\u51fa\u5668\u9ed8\u8ba4\u4f7f\u7528\u00a0–precision_mode=force_fp16，\u5e76\u4f1a\u62d2\u7edd\u663e\u5f0f FP32（quantize=32）\u7684\u8bf7\u6c42\u3002\u4e0d\u8981\u6307\u671b\u5728\u6607\u817e\u4e0a\u767d\u5ad6 FP32\u3002<\/p>\n 04\u5b8c\u6574\u90e8\u7f72\u6d41\u7a0b：\u4ece .pt \u5230\u68c0\u6d4b\u7ed3\u679c<\/h3>\n\u9636\u6bb5\u4e00：\u6a21\u578b\u51c6\u5907（\u5f00\u53d1\u73af\u5883）\u9636\u6bb5\u4e8c：\u8bbe\u5907\u63a8\u7406（\u6607\u817e NPU）PyTorch \u6743\u91cdyolov8s.pt\u5bfc\u51fa ONNXonnx-simplifier \u5316\u7b80ATC \u7f16\u8bd1\u6307\u5b9a soc_version.om \u79bb\u7ebf\u6a21\u578byolov8s_bs1.om\u53ef\u9009：AOE \u5b50\u56fe\/\u7b97\u5b50\u8c03\u4f18 \u00b7 AMCT INT8 \u91cf\u5316\u91cf\u5316\u540e\u7528 ais_bench \u505a\u7cbe\u5ea6\u6bd4\u5bf9\u4e0b\u53d1\u90e8\u7f72\u52a0\u8f7d OMAscendCL \/ ais_benchDVPP \u9884\u5904\u7406\u786c\u89e3\/\u7f29\u653e\/\u8272\u57dfAI Core \u63a8\u7406FP16 \/ INT8CPU \u540e\u5904\u7406\u89e3\u7801 + NMS\u68c0\u6d4b\u7ed3\u679c<\/p>\n \u7f16\u8bd1\u5728\u5f00\u53d1\u73af\u5883\u4e00\u6b21\u5b8c\u6210，\u8bbe\u5907\u7aef\u53ea\u505a"\u52a0\u8f7d\u2014\u9884\u5904\u7406\u2014\u63a8\u7406\u2014\u540e\u5904\u7406"\u3002<\/p>\n \u7b2c\u4e00\u6b65：\u5bfc\u51fa ONNX（\u5148\u5316\u7b80）<\/h4>\n\u65e0\u8bba\u8d70\u54ea\u6761\u8def，\u5148\u628a\u6a21\u578b\u5bfc\u6210 ONNX，\u5e76\u5f3a\u70c8\u5efa\u8bae\u7528 onnx-simplifier \u5316\u7b80，\u6298\u53e0\u5e38\u91cf\u3001\u6d88\u9664\u5197\u4f59\u8282\u70b9，\u80fd\u663e\u8457\u964d\u4f4e ATC \u8f6c\u6362\u62a5\u9519\u7387\u3002<\/p>\n \u7b2c\u4e8c\u6b65：\u7528 ATC \u7f16\u8bd1\u6210 OM<\/h4>\nATC \u662f\u6700\u6838\u5fc3\u7684\u4e00\u6b65\u3002–framework=5\u00a0\u8868\u793a\u8f93\u5165\u662f ONNX，–soc_version\u00a0\u5fc5\u987b\u548c\u4f60\u5b9e\u9645\u90e8\u7f72\u7684\u82af\u7247\u5b8c\u5168\u4e00\u81f4，\u751f\u4ea7\u73af\u5883\u7528\u56fa\u5b9a batch\u3001\u56fa\u5b9a\u8f93\u5165\u5c3a\u5bf8：<\/p>\n # YOLOv5 \u793a\u4f8b（\u6607\u817e\u5b98\u65b9\u6587\u6863\u540c\u6b3e，Ascend310B4）atc –model=yolov5s.onnx –framework=5 \\\\\u00a0 \u00a0 –output=yolov5s_bs1 \\\\\u00a0 \u00a0 –input_format=NCHW \\\\\u00a0 \u00a0 –soc_version=Ascend310B4 \\\\\u00a0 \u00a0 –input_fp16_nodes="images"# YOLOv8 \u901a\u7528\u5199\u6cd5：\u56fa\u5b9a shape，\u5173\u95ed\u52a8\u6001\u7ef4\u5ea6atc –model=yolov8s.onnx –framework=5 \\\\\u00a0 \u00a0 –output=yolov8s_bs1 \\\\\u00a0 \u00a0 –input_format=NCHW \\\\\u00a0 \u00a0 –input_shape="images:1,3,640,640" \\\\\u00a0 \u00a0 –soc_version=Ascend310B4 \\\\\u00a0 \u00a0 –precision_mode=force_fp16<\/p>\n \u6700\u7701\u4e8b：\u7528\u5b98\u65b9\u4e00\u952e\u5bfc\u51fa<\/h4>\n# Python：v8.4.107 \u53ca\u4ee5\u4e0afrom\u00a0ultralytics\u00a0import\u00a0YOLOmodel = YOLO("yolov8s.pt")model.export(format="ascend", name="Ascend310B4")# \u547d\u4ee4\u884c\u7b49\u4ef7\u5199\u6cd5yolo export model=yolov8s.pt format=ascend name=Ascend310B4<\/p>\n \u7b2c\u4e09\u6b65：\u63a8\u7406\u9a8c\u8bc1<\/h4>\n\u5148\u7528\u5b98\u65b9\u00a0ais_bench\u00a0\u8dd1\u901a OM\u3001\u770b\u8f93\u51fa\u5f20\u91cf\u548c\u5ef6\u8fdf，\u518d\u5199\u5de5\u7a0b\u4ee3\u7801；C++ \u751f\u4ea7\u670d\u52a1\u5219\u7528 AscendCL \u52a0\u8f7d\u6a21\u578b\u3001\u7ba1\u7406\u5185\u5b58\u4e0e\u591a\u7ebf\u7a0b：<\/p>\n # \u7528 ais_bench \u9a8c\u8bc1 OM \u6a21\u578bpython -m ais_bench –model=.\/yolov8s_bs1.om \\\\\u00a0 \u00a0 –input=.\/images\/ –output=.\/outputs\/ –output_dirname=result<\/p>\n 05\u6700\u5bb9\u6613\u8e29\u7684\u516d\u4e2a\u5751，\u63d0\u524d\u907f\u5f00<\/h3>\n<\/p>\n![\"\u667a\u6167\u5de5\u5382\u8fb9\u7f18\u90e8\u7f72\u573a\u666f\uff1a\u5de5\u4e1a\u6444\u50cf\u5934\u5bf9\u51c6\u4f20\u9001\u5e26\uff0c\u8fb9\u7f18\u8ba1\u7b97\u76d2\u5b50\u8fd0\u884cYOLO\u68c0\u6d4b\uff0c\u5305\u88c5\u7bb1\u4e0e\u96f6\u4ef6\u4e0a\u53e0\u52a0\u68c0\u6d4b\u6846\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/11\/20261108023312-6aefdf688b332.jpg\") <\/p>\n
\u4ea7\u7ebf\u4e0a\u7a33\u5b9a\u8fd0\u884c\u7684\u5173\u952e，\u5f80\u5f80\u5728\u6a21\u578b\u4e4b\u5916\u7684\u9884\u5904\u7406\u548c\u540e\u5904\u7406\u3002<\/p>\n \n\n\u5751<\/p>\n \u73b0\u8c61<\/p>\n \u89e3\u51b3\u529e\u6cd5<\/p>\n<\/tr>\n \n\n| \n NMS \u540e\u5904\u7406<\/p>\n<\/td>\n | \n \u7167\u642c\u5b98\u65b9 NMS \u5728 NPU \u4e0a\u5185\u5b58\u8d8a\u754c\u6216\u62a5\u9519<\/p>\n<\/td>\n | \n \u89e3\u7801\u4e0e NMS \u653e CPU；\u6216\u7528\u4e13\u7528 YoloV5DetectionOutput \u7b97\u5b50（\u9700\u6b63\u786e\u8bbe\u7f6e N \u7b49\u5c5e\u6027）<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u7b97\u5b50\u4e0d\u652f\u6301<\/p>\n<\/td>\n | \n ATC \u62a5 unsupported op<\/p>\n<\/td>\n | \n \u5148 simplify；\u67e5 CANN \u7b97\u5b50\u6e05\u5355\u66ff\u6362\u7b49\u4ef7\u7b97\u5b50；\u590d\u6742\u52a8\u6001\u7b97\u5b50\u79fb\u51fa\u7f51\u7edc，\u5fc5\u8981\u65f6\u7528 Ascend C \u81ea\u5b9a\u4e49<\/p>\n<\/td>\n<\/tr>\n | \n| \n FP32 \u4e0d\u53ef\u7528<\/p>\n<\/td>\n | \n \u5f3a\u5236 FP32 \u540e\u6027\u80fd\u9aa4\u964d\u6216\u88ab\u62d2<\/p>\n<\/td>\n | \n \u7edf\u4e00\u7528 force_fp16，\u654f\u611f\u5c42\u518d\u8003\u8651\u4fdd\u7559\u9ad8\u7cbe\u5ea6<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u52a8\u6001 shape<\/p>\n<\/td>\n | \n batch\/\u5206\u8fa8\u7387\u53d8\u5316\u5bfc\u81f4\u6027\u80fd\u5dee<\/p>\n<\/td>\n | \n \u751f\u4ea7\u7528\u56fa\u5b9a batch \u4e0e\u56fa\u5b9a 640\u00d7640，\u591a\u8def\u901a\u8fc7\u591a\u5b9e\u4f8b\u6216\u51d1\u6279\u5904\u7406<\/p>\n<\/td>\n<\/tr>\n | \n| \n CPU \u9884\u5904\u7406\u74f6\u9888<\/p>\n<\/td>\n | \n OpenCV \u89e3\u7801\/\u7f29\u653e\u5403\u6389\u5927\u91cf\u65f6\u95f4<\/p>\n<\/td>\n | \n \u7528 DVPP \u786c\u4ef6\u89e3\u7801（YUV420）\u3001\u7f29\u653e\u3001\u8272\u57df\u8f6c\u6362，\u4e0e\u63a8\u7406\u5e76\u884c<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u7248\u672c\u4e0d\u5339\u914d<\/p>\n<\/td>\n | \n \u5404\u79cd\u5947\u602a\u62a5\u9519<\/p>\n<\/td>\n | \n \u9a71\u52a8\u3001\u56fa\u4ef6\u3001CANN\u3001torch_npu \u7248\u672c\u4e25\u683c\u5bf9\u5e94，\u5148\u88c5\u9a71\u52a8\u518d\u88c5 CANN<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u4e00\u4e2a\u5e38\u89c1\u8bef\u533a：\u53ea\u6d4b"\u6a21\u578b\u63a8\u7406\u65f6\u95f4"\u5c31\u5ba3\u79f0\u6027\u80fd\u3002\u771f\u5b9e\u7aef\u5230\u7aef\u8017\u65f6 =\u00a0\u53d6\u6d41 + \u9884\u5904\u7406 + \u63a8\u7406 + \u540e\u5904\u7406 + \u6570\u636e\u642c\u8fd0，\u524d\u5904\u7406\u548c\u540e\u5904\u7406\u5728\u5c0f\u6a21\u578b\u4e0a\u7ecf\u5e38\u5360\u6389\u4e00\u534a\u4ee5\u4e0a\u3002\u52a1\u5fc5\u7528 msprof \u5168\u94fe\u8def profiling，\u800c\u4e0d\u662f\u53ea\u770b\u4e00\u6b21\u7eaf\u63a8\u7406\u3002<\/p>\n 06\u6027\u80fd\u4f18\u5316：\u4ece"\u80fd\u8dd1"\u5230"\u8dd1\u5f97\u6ee1"<\/h3>\n\n\n\u4f18\u5316\u624b\u6bb5<\/p>\n \u505a\u6cd5<\/p>\n \u6536\u76ca \/ \u6ce8\u610f<\/p>\n<\/tr>\n \n\n| \n \u56fa\u5b9a shape<\/p>\n<\/td>\n | \n \u56fa\u5b9a batch\u3001\u8f93\u5165\u5206\u8fa8\u7387，\u9759\u6001\u7f16\u8bd1<\/p>\n<\/td>\n | \n \u907f\u514d\u52a8\u6001 shape \u5e26\u6765\u7684\u91cd\u7f16\u8bd1\u4e0e\u5185\u5b58\u6d6a\u8d39<\/p>\n<\/td>\n<\/tr>\n | \n| \n AOE \u8c03\u4f18<\/p>\n<\/td>\n | \n \u5148\u5b50\u56fe\u8c03\u4f18\u3001\u518d\u7b97\u5b50\u8c03\u4f18（aoe \u5728 atc \u53c2\u6570\u4e0a\u52a0 –job_type）<\/p>\n<\/td>\n | \n \u4e00\u6b21\u7f16\u8bd1\u3001\u957f\u671f\u53d7\u76ca，\u90e8\u5206\u6a21\u578b\u63d0\u5347\u660e\u663e<\/p>\n<\/td>\n<\/tr>\n | \n| \n INT8 \u91cf\u5316<\/p>\n<\/td>\n | \n AMCT + \u4ee3\u8868\u6027\u6821\u51c6\u96c6，\u68c0\u6d4b\u5934\u53ef\u4fdd\u7559 FP16<\/p>\n<\/td>\n | \n \u516c\u5f00\u7814\u7a76\u4e2d INT8 \u5e38\u89c1\u7ea6 2\u00d7 \u91cf\u7ea7\u52a0\u901f；\u9700\u6821\u9a8c mAP，\u4e0d\u8fbe\u6807\u56de\u9000\u6df7\u5408\u7cbe\u5ea6<\/p>\n<\/td>\n<\/tr>\n | \n| \n DVPP \u9884\u5904\u7406<\/p>\n<\/td>\n | \n \u786c\u89e3\u89c6\u9891\/JPEG\u3001\u7f29\u653e\u3001YUV \u8272\u57df<\/p>\n<\/td>\n | \n \u628a CPU \u91ca\u653e\u51fa\u6765，\u591a\u8def\u89c6\u9891\u6536\u76ca\u5de8\u5927<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u591a\u5b9e\u4f8b \/ \u51d1\u6279<\/p>\n<\/td>\n | \n \u591a\u8def\u89c6\u9891\u62fc\u6210 batch，\u6216\u5f00\u591a\u63a8\u7406\u5b9e\u4f8b<\/p>\n<\/td>\n | \n \u63d0\u9ad8 AI Core \u5229\u7528\u7387，\u6ce8\u610f\u663e\u5b58\u4e0e\u8def\u6570\u5e73\u8861<\/p>\n<\/td>\n<\/tr>\n | \n| \n \u6d41\u6c34\u7ebf\u5e76\u884c<\/p>\n<\/td>\n | \n \u9884\u5904\u7406\u3001\u63a8\u7406\u3001\u540e\u5904\u7406\u5206\u5230\u4e0d\u540c\u7ebf\u7a0b<\/p>\n<\/td>\n | \n \u9690\u85cf\u5355\u5e27\u5ef6\u8fdf，\u63d0\u9ad8\u541e\u5410<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u5173\u4e8e\u5b9e\u9645\u5e27\u7387，\u8981\u5b9e\u4e8b\u6c42\u662f：\u5355\u6a21\u578b\u5ef6\u8fdf\u3001\u8f93\u5165\u5206\u8fa8\u7387\u3001batch\u3001\u662f\u5426\u542b\u524d\u540e\u5904\u7406\u3001\u51e0\u8def\u89c6\u9891，\u90fd\u4f1a\u663e\u8457\u6539\u53d8\u7ed3\u679c\u3002\u516c\u5f00\u5b66\u672f\u8d44\u6599\u4e2d\u6709\u5728\u6607\u817e 310B \u4e0a\u7ecf\u6df7\u5408\u91cf\u5316\u628a YOLO \u7cfb\u5217\u505a\u5230\u7ea6 112 FPS \u7684\u6848\u4f8b，\u4f46\u90a3\u662f\u7279\u5b9a\u538b\u7f29\u65b9\u6cd5\u4e0b\u7684\u7ed3\u679c；\u4e0d\u8981\u628a\u5382\u5546\u5ba3\u4f20\u7684\u5cf0\u503c\u7b97\u529b\u6216\u522b\u4eba\u89c6\u9891\u91cc\u7684\u5e27\u7387，\u76f4\u63a5\u5f53\u6210\u4f60\u9879\u76ee\u7684\u7aef\u5230\u7aef\u5e27\u7387，\u52a1\u5fc5\u7528 ais_bench \u4e0e msprof \u5728\u81ea\u5df1\u7684\u786c\u4ef6\u3001CANN \u7248\u672c\u548c\u6570\u636e\u4e0a\u5b9e\u6d4b\u3002<\/p>\n 07\u9009\u578b\u5efa\u8bae：\u4ec0\u4e48\u573a\u666f\u4e0a\u4ec0\u4e48\u5361<\/h3>\n\u667a\u80fd\u6444\u50cf\u5934 \/ \u673a\u5668\u4eba \/ \u65e0\u4eba\u673a\n\u9009 Atlas 200I A2（310B，8~20 TOPS）\u6a21\u5757，\u4f53\u79ef\u5c0f\u3001\u529f\u8017\u4f4e，YOLOv8-n\/s \u8fd9\u7c7b\u5c0f\u6a21\u578b\u53ef\u5b9e\u65f6\u8dd1\u3002<\/p>\n<\/li>\n \u8fb9\u7f18\u76d2 \/ \u5355\u8def\u5230\u51e0\u8def\u89c6\u9891\n310B 20T \u7248\u672c\u6216 310P \u63a8\u7406\u5361，\u517c\u987e\u6027\u80fd\u4e0e\u6210\u672c\u3002<\/p>\n<\/li>\n \u6570\u636e\u4e2d\u5fc3\u901a\u7528\u63a8\u7406 \/ \u89c6\u9891\u7ed3\u6784\u5316\nAtlas 300I Pro（310P）\u6216 Atlas 300I（310，88 TOPS）\u3002<\/p>\n<\/li>\n \u591a\u8def\u9ad8\u5bc6\u89c6\u9891 \/ \u5927\u89c4\u6a21\u7b97\u529b\u76d2\nAtlas 300I Duo（280 TOPS INT8，48\/96GB \u5927\u5185\u5b58），\u9002\u5408\u591a\u8def\u5e76\u53d1\u4e0e\u591a\u6a21\u578b\u5171\u90e8\u7f72\u3002<\/p>\n<\/li>\n \u8bad\u7ec3 \/ \u5927\u6a21\u578b \/ \u8bad\u63a8\u4e00\u4f53\n\u6607\u817e 910B 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