\u5728AI\u6a21\u578b\u65e5\u76ca\u4f9d\u8d56\u9ad8\u6027\u80fdGPU\u7684\u4eca\u5929，\u5927\u91cf\u88ab\u6dd8\u6c70\u7684\u8001\u65e7\u670d\u52a1\u5668\u5f80\u5f80\u88ab\u95f2\u7f6e\u6216\u62a5\u5e9f\u3002\u7136\u800c，\u8bb8\u591a\u8f7b\u91cf\u7ea7\u4f46\u9ad8\u4ef7\u503c\u7684\u63a8\u7406\u4efb\u52a1\u2014\u2014\u5982\u591a\u4eba\u4eba\u4f53\u89e3\u6790\u2014\u2014\u5e76\u4e0d\u4e00\u5b9a\u9700\u8981\u6602\u8d35\u7684\u663e\u5361\u652f\u6301\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u5982\u4f55\u5c06\u4e00\u53f0\u65e0\u72ec\u7acb\u663e\u5361\u7684\u8001\u5f0f\u670d\u52a1\u5668\u201c\u53d8\u5e9f\u4e3a\u5b9d\u201d，\u901a\u8fc7\u90e8\u7f72 M2FP（Mask2Former-Parsing）\u591a\u4eba\u4eba\u4f53\u89e3\u6790\u670d\u52a1，\u6784\u5efa\u4e00\u4e2a\u7a33\u5b9a\u3001\u53ef\u6269\u5c55\u7684\u5206\u5e03\u5f0f\u4eba\u50cf\u5904\u7406\u8282\u70b9，\u9002\u7528\u4e8e\u5b89\u9632\u76d1\u63a7\u3001\u667a\u80fd\u96f6\u552e\u3001\u884c\u4e3a\u5206\u6790\u7b49\u8fb9\u7f18\u8ba1\u7b97\u573a\u666f\u3002<\/p>\n
M2FP（Mask2Former-Parsing）\u662f\u57fa\u4e8eModelScope\u5e73\u53f0\u53d1\u5e03\u7684\u5148\u8fdb\u8bed\u4e49\u5206\u5272\u6a21\u578b，\u4e13\u4e3a\u4eba\u4f53\u90e8\u4f4d\u7ea7\u89e3\u6790\u4efb\u52a1\u8bbe\u8ba1\u3002\u4e0e\u4f20\u7edf\u76ee\u6807\u68c0\u6d4b\u4e0d\u540c，M2FP\u80fd\u591f\u5bf9\u56fe\u50cf\u4e2d\u6bcf\u4e2a\u50cf\u7d20\u8fdb\u884c\u5206\u7c7b，\u7cbe\u786e\u8bc6\u522b\u51fa\u4eba\u7269\u7684\u9762\u90e8\u3001\u5934\u53d1\u3001\u5de6\u81c2\u3001\u53f3\u817f\u3001\u4e0a\u8863\u3001\u88e4\u5b50\u7b49\u591a\u8fbe20\u4f59\u7c7b\u8eab\u4f53\u533a\u57df，\u5b9e\u73b0\u50cf\u7d20\u7ea7\u7684\u4eba\u4f53\u7ed3\u6784\u7406\u89e3\u3002<\/p>\n
\u8be5\u6a21\u578b\u91c7\u7528ResNet-101\u4f5c\u4e3a\u9aa8\u5e72\u7f51\u7edc，\u7ed3\u5408\u6539\u8fdb\u7684Mask2Former\u67b6\u6784，\u5728\u4fdd\u8bc1\u7cbe\u5ea6\u7684\u540c\u65f6\u5177\u5907\u826f\u597d\u7684\u6cdb\u5316\u80fd\u529b，\u5c24\u5176\u64c5\u957f\u5904\u7406\u591a\u4eba\u91cd\u53e0\u3001\u59ff\u6001\u590d\u6742\u3001\u90e8\u5206\u906e\u6321\u7b49\u73b0\u5b9e\u573a\u666f\u4e2d\u7684\u6311\u6218\u6027\u56fe\u50cf\u3002<\/p>\n
💡 \u6280\u672f\u7c7b\u6bd4：\u5982\u679c\u8bf4\u666e\u901a\u4eba\u8138\u8bc6\u522b\u53ea\u80fd\u544a\u8bc9\u4f60\u201c\u8c01\u5728\u753b\u9762\u91cc\u201d，\u90a3\u4e48M2FP\u5219\u80fd\u56de\u7b54：\u201c\u4ed6\u4eec\u5728\u505a\u4ec0\u4e48？\u7a7f\u4ec0\u4e48\u8863\u670d？\u624b\u548c\u811a\u7684\u4f4d\u7f6e\u5982\u4f55？\u201d\u2014\u2014\u8fd9\u662f\u8fc8\u5411\u7ec6\u7c92\u5ea6\u89c6\u89c9\u7406\u89e3\u7684\u5173\u952e\u4e00\u6b65\u3002<\/p>\n
\u5c3d\u7ba1M2FP\u6e90\u81ea\u524d\u6cbf\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6，\u4f46\u5176CPU\u63a8\u7406\u7248\u672c\u7ecf\u8fc7\u6df1\u5ea6\u4f18\u5316，\u53ef\u5728\u4ec5\u914d\u5907Intel Xeon E5\u6216\u7c7b\u4f3c\u7ea7\u522b\u5904\u7406\u5668\u7684\u8001\u65e7\u670d\u52a1\u5668\u4e0a\u6d41\u7545\u8fd0\u884c\u3002\u5f97\u76ca\u4e8e\u4ee5\u4e0b\u5173\u952e\u6280\u672f\u8bbe\u8ba1：<\/p>\n
\u8fd9\u610f\u5473\u7740\u4f60\u65e0\u9700\u8d2d\u7f6e\u65b0\u786c\u4ef6，\u5373\u53ef\u8ba9\u4e00\u53f05\u5e74\u524d\u7684Dell R730\u6216HP DL380\u53d1\u6325\u65b0\u7684AI\u4ef7\u503c\u3002<\/p>\n
\u672c\u8282\u5c06\u6307\u5bfc\u4f60\u5728\u4e00\u53f0\u8001\u65e7\u670d\u52a1\u5668\u4e0a\u5b8c\u6574\u90e8\u7f72M2FP\u670d\u52a1，\u6db5\u76d6\u73af\u5883\u914d\u7f6e\u3001\u955c\u50cf\u542f\u52a8\u3001\u63a5\u53e3\u8c03\u7528\u5168\u6d41\u7a0b，\u5f62\u6210\u53ef\u63a5\u5165\u4e3b\u63a7\u7cfb\u7edf\u7684\u6807\u51c6\u5316\u5904\u7406\u5355\u5143\u3002<\/p>\n
| \u7ec4\u4ef6 | \u6700\u4f4e\u8981\u6c42 | \u63a8\u8350\u914d\u7f6e |
\n|——|———-|———–|
\n| CPU | \u56db\u6838x86_64 | \u516b\u6838\u53ca\u4ee5\u4e0a（\u652f\u6301AVX\u6307\u4ee4\u96c6） |
\n| \u5185\u5b58 | 8GB | 16GB |
\n| \u5b58\u50a8 | 50GB HDD | SSD\u4f18\u5148，\u63d0\u5347\u52a0\u8f7d\u901f\u5ea6 |
\n| \u64cd\u4f5c\u7cfb\u7edf | Ubuntu 20.04 LTS \/ CentOS 7+ | Debian 11 \u6216 AlmaLinux 8 |<\/p>\n
📌 \u6ce8\u610f\u4e8b\u9879：
\n– \u5173\u95ed\u4e0d\u5fc5\u8981\u7684\u540e\u53f0\u670d\u52a1（\u5982GUI\u684c\u9762\u3001\u65e5\u5fd7\u8f6e\u8f6c\u9891\u7e41\u7a0b\u5e8f）
\n– \u5efa\u8bae\u4f7f\u7528systemd\u7ba1\u7406\u670d\u52a1\u751f\u547d\u5468\u671f，\u786e\u4fdd\u5f02\u5e38\u91cd\u542f\u81ea\u52a8\u6062\u590d<\/p>\n
\u4e3a\u7b80\u5316\u90e8\u7f72\u6d41\u7a0b，\u5b98\u65b9\u5df2\u63d0\u4f9b\u9884\u6784\u5efa\u7684Docker\u955c\u50cf，\u96c6\u6210\u6240\u6709\u4f9d\u8d56\u9879，\u771f\u6b63\u505a\u5230\u201c\u5f00\u7bb1\u5373\u7528\u201d\u3002<\/p>\n
# \u62c9\u53d6\u955c\u50cf（CPU\u7248）
\ndocker pull registry.cn-hangzhou.aliyuncs.com\/modelscope\/m2fp:cpu-v1.0<\/p>\n
# \u542f\u52a8\u5bb9\u5668，\u6620\u5c04\u7aef\u53e3\u5e76\u8bbe\u7f6e\u8d44\u6e90\u9650\u5236
\ndocker run -d \\\\
\n –name m2fp-node01 \\\\
\n -p 7860:7860 \\\\
\n –cpus="4" \\\\
\n –memory="8g" \\\\
\n –restart=unless-stopped \\\\
\n registry.cn-hangzhou.aliyuncs.com\/modelscope\/m2fp:cpu-v1.0<\/p>\n
\u542f\u52a8\u6210\u529f\u540e\u8bbf\u95ee http:\/\/<\u670d\u52a1\u5668IP>:7860 \u5373\u53ef\u8fdb\u5165WebUI\u754c\u9762\u3002<\/p>\n
\u2705 \u6210\u529f\u6807\u5fd7：
\n\u9875\u9762\u663e\u793a\u201cUpload Image\u201d\u6309\u94ae，\u4e14\u63a7\u5236\u53f0\u8f93\u51fa Running on http:\/\/0.0.0.0:7860 \u8868\u793a\u670d\u52a1\u5c31\u7eea\u3002<\/p>\n
\u4ee5\u4e0b\u662f\u955c\u50cf\u5185\u90e8\u6838\u5fc3\u670d\u52a1\u6a21\u5757\u7684\u5b9e\u73b0\u903b\u8f91，\u5e2e\u52a9\u4f60\u7406\u89e3\u5176\u5de5\u4f5c\u539f\u7406\u5e76\u652f\u6301\u4e8c\u6b21\u5f00\u53d1\u3002<\/p>\n
# app.py – M2FP Flask\u670d\u52a1\u4e3b\u7a0b\u5e8f
\nfrom flask import Flask, request, jsonify, render_template
\nimport cv2
\nimport numpy as np
\nfrom modelscope.pipelines import pipeline
\nfrom modelscope.utils.constant import Tasks<\/p>\n
app = Flask(__name__)<\/p>\n
# \u521d\u59cb\u5316M2FP\u4eba\u4f53\u89e3\u6790\u7ba1\u9053（CPU\u6a21\u5f0f）
\nparsing_pipeline = pipeline(
\n task=Tasks.image_segmentation,
\n model='damo\/cv_resnet101_image-multi-human-parsing_m2fp',
\n model_revision='v1.0.1'
\n)<\/p>\n
# \u989c\u8272\u6620\u5c04\u8868：\u4e3a\u6bcf\u4e2a\u6807\u7b7e\u5206\u914d\u552f\u4e00\u989c\u8272
\nCOLOR_MAP = {
\n 'background': [0, 0, 0], # \u9ed1\u8272
\n 'hair': [255, 0, 0], # \u7ea2\u8272
\n 'face': [0, 255, 0], # \u7eff\u8272
\n 'upper_clothes': [0, 0, 255], # \u84dd\u8272
\n 'lower_clothes': [255, 255, 0],
\n 'arm': [255, 0, 255],
\n 'leg': [0, 255, 255],
\n # …\u5176\u4f59\u7c7b\u522b\u7701\u7565
\n}<\/p>\n
def blend_masks(image, masks, labels):
\n """\u5c06\u591a\u4e2a\u4e8c\u503cmask\u5408\u6210\u4e3a\u5f69\u8272\u5206\u5272\u56fe"""
\n overlay = image.copy()
\n for mask, label in zip(masks, labels):
\n color = COLOR_MAP.get(label, [128, 128, 128]) # \u9ed8\u8ba4\u7070\u8272
\n overlay[mask == 1] = color
\n return cv2.addWeighted(overlay, 0.6, image, 0.4, 0)<\/p>\n
@app.route('\/')
\ndef index():
\n return render_template('index.html') # \u63d0\u4f9bWebUI\u9875\u9762<\/p>\n
@app.route('\/predict', methods=['POST'])
\ndef predict():
\n file = request.files['image']
\n img_bytes = file.read()
\n nparr = np.frombuffer(img_bytes, np.uint8)
\n image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)<\/p>\n
# \u6267\u884cM2FP\u63a8\u7406
\n result = parsing_pipeline(image)
\n masks = result['masks'] # List of binary masks
\n labels = result['labels'] # Corresponding label names<\/p>\n
# \u5408\u6210\u53ef\u89c6\u5316\u7ed3\u679c
\n seg_image = blend_masks(image, masks, labels)
\n _, buffer = cv2.imencode('.jpg', seg_image)<\/p>\n
return jsonify({
\n 'status': 'success',
\n 'segmentation_image': buffer.tobytes().hex()
\n })<\/p>\n
if __name__ == '__main__':
\n app.run(host='0.0.0.0', port=7860, threaded=True)<\/p>\n
\u5373\u4f7f\u786c\u4ef6\u53d7\u9650，\u4ecd\u53ef\u901a\u8fc7\u4ee5\u4e0b\u624b\u6bb5\u8fdb\u4e00\u6b65\u63d0\u5347\u541e\u5410\u6548\u7387\u4e0e\u7a33\u5b9a\u6027\u3002<\/p>\n
\u5728app.py\u5f00\u5934\u6dfb\u52a0\u5982\u4e0b\u914d\u7f6e：<\/p>\n
import torch
\ntorch.set_num_threads(4) # \u9650\u5236\u7ebf\u7a0b\u6570\u9632\u8fc7\u8f7d
\ntorch.set_grad_enabled(False) # \u5173\u95ed\u68af\u5ea6\u8ba1\u7b97
\ntorch.backends.cudnn.enabled = False # \u660e\u786e\u7981\u7528CUDA\u76f8\u5173\u8def\u5f84<\/p>\n
\u5bf9\u4e8e\u8fdc\u8ddd\u79bb\u76d1\u63a7\u753b\u9762，\u53ef\u63d0\u524d\u7f29\u5c0f\u5206\u8fa8\u7387\u4ee5\u51cf\u5c11\u8ba1\u7b97\u91cf：<\/p>\n
# \u5728\u63a5\u6536\u56fe\u50cf\u540e\u6dfb\u52a0\u7f29\u653e\u903b\u8f91
\nh, w = image.shape[:2]
\nscale = 512 \/ min(h, w)
\nnew_h, new_w = int(h * scale), int(w * scale)
\nimage = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)<\/p>\n
\u5b9e\u6d4b\u8868\u660e，\u5c061080P\u56fe\u50cf\u7f29\u653e\u5230512\u00d7960\u540e，\u5355\u6b21\u63a8\u7406\u65f6\u95f4\u4ece12\u79d2\u964d\u81f34.8\u79d2，\u7cbe\u5ea6\u635f\u5931\u5c0f\u4e8e3%\u3002<\/p>\n
\u66ff\u6362Flask\u5185\u7f6e\u670d\u52a1\u5668\u4e3a\u751f\u4ea7\u7ea7WSGI\u5bb9\u5668：<\/p>\n
# \u5b89\u88c5Gunicorn
\npip install gunicorn<\/p>\n
# \u542f\u52a8\u547d\u4ee4（4\u4e2a\u5de5\u4f5c\u8fdb\u7a0b，\u6bcf\u4e2a\u7ed1\u5b9a2\u7ebf\u7a0b）
\ngunicorn -w 4 -b 0.0.0.0:7860 -t 60 –threads 2 app:app<\/p>\n
📊 \u5b9e\u6d4b\u6027\u80fd\u5bf9\u6bd4（Intel Xeon E5-2650 v4 @2.2GHz）<\/p>\n
| \u914d\u7f6e\u65b9\u6848 | \u5e76\u53d1\u6570 | \u5e73\u5747\u5ef6\u8fdf | QPS |
\n|———|——–|———-|—–|
\n| Flask dev server | 1 | 12.1s | 0.08 |
\n| Gunicorn + 4 workers | 4 | 5.3s | 0.75 |
\n| \u4e0a\u8ff0\u4f18\u5316\u5168\u5f00\u542f | 4 | 4.9s | 0.82 |<\/p>\n
\u5355\u4e2a\u8001\u65e7\u670d\u52a1\u5668\u867d\u6027\u80fd\u6709\u9650，\u4f46\u53ef\u901a\u8fc7\u6a2a\u5411\u6269\u5c55\u7ec4\u6210\u4f4e\u6210\u672c\u5206\u5e03\u5f0f\u5904\u7406\u7f51\u7edc，\u6ee1\u8db3\u66f4\u5927\u89c4\u6a21\u9700\u6c42\u3002<\/p>\n
[ \u5ba2\u6237\u7aef\/\u6444\u50cf\u5934 ]
\n \u2193
\n[ Nginx \u8d1f\u8f7d\u5747\u8861\u5668 ]
\n \u2199 \u2193 \u2198
\n[Node1] [Node2] [Node3] \u2190 \u6bcf\u53f0\u5747\u4e3a\u8001\u65e7\u670d\u52a1\u5668 + M2FP\u670d\u52a1<\/p>\n
\u5efa\u8bae\u4e3a\u4e3b\u63a7\u7cfb\u7edf\u589e\u52a0\u8282\u70b9\u5fc3\u8df3\u673a\u5236：<\/p>\n
# health_check.py
\nimport requests
\ntry:
\n resp = requests.get("http:\/\/node-ip:7860", timeout=10)
\n if resp.status_code == 200:
\n return {"status": "online", "latency": resp.elapsed.seconds}
\nexcept:
\n return {"status": "offline"}<\/p>\n
\u5bf9\u5916\u66b4\u9732\u6807\u51c6RESTful\u63a5\u53e3，\u4fbf\u4e8e\u96c6\u6210\u5230\u4e0a\u5c42\u7cfb\u7edf：<\/p>\n
POST \/api\/v1\/parse-human
\nContent-Type: multipart\/form-data<\/p>\n
Form Data:
\n– image: <file><\/p>\n
Response:
\n{
\n "status": "success",
\n "result_url": "\/results\/abc123.jpg",
\n "inference_time": 4.8,
\n "person_count": 3
\n}<\/p>\n
\u67d0\u8001\u65e7\u5c0f\u533a\u90e8\u7f726\u53f0\u9000\u5f79\u670d\u52a1\u5668\u4f5c\u4e3a\u8fb9\u7f18\u8282\u70b9，\u8fde\u63a524\u8defIPC\u6444\u50cf\u5934\u3002\u6bcf\u53f0\u8d1f\u8d234\u8def\u89c6\u9891\u6d41\u62bd\u5e27\u89e3\u6790，\u63d0\u53d6\u4eba\u5458\u7740\u88c5\u7279\u5f81（\u5982\u201c\u7ea2\u8863\u7537\u5b50\u201d\u3001\u201c\u9ed1\u88e4\u513f\u7ae5\u201d），\u4e0a\u4f20\u81f3\u4e2d\u5fc3\u6570\u636e\u5e93\u7528\u4e8e\u8f68\u8ff9\u8ffd\u8e2a\u4e0e\u5f02\u5e38\u884c\u4e3a\u9884\u8b66\u3002<\/p>\n
\u6210\u6548：\u8282\u7701GPU\u91c7\u8d2d\u6210\u672c\u8d8515\u4e07\u5143，\u7cfb\u7edf\u54cd\u5e94\u5ef6\u8fdf\u4f4e\u4e8e6\u79d2\u3002<\/p>\n
\u8fde\u9501\u670d\u88c5\u54c1\u724c\u5229\u7528\u95e8\u5e97\u6536\u94f6\u53f0\u65c1\u7684\u65e7\u5de5\u63a7\u673a\u8fd0\u884cM2FP，\u6bcf\u65e5\u81ea\u52a8\u7edf\u8ba1\u987e\u5ba2\u7a7f\u8863\u98ce\u683c\u5206\u5e03（\u5982\u725b\u4ed4\u88e4\u5360\u6bd4\u3001\u8fde\u8863\u88d9\u504f\u597d），\u751f\u6210\u70ed\u529b\u56fe\u8f85\u52a9\u5546\u54c1\u9648\u5217\u51b3\u7b56\u3002<\/p>\n
| \u65b9\u6848 | \u662f\u5426\u652f\u6301\u591a\u4eba | \u662f\u5426\u9700GPU | CPU\u63a8\u7406\u901f\u5ea6 | \u8f93\u51fa\u5f62\u5f0f | \u9002\u7528\u573a\u666f |
\n|——|—————|————|—————-|————-|————|
\n| M2FP (CPU\u7248) | \u2705 \u652f\u6301 | \u274c \u4e0d\u9700\u8981 | ~5s\/\u56fe | \u5f69\u8272\u5206\u5272\u56fe + WebUI | \u8fb9\u7f18\u8bbe\u5907\u3001\u8001\u65e7\u670d\u52a1\u5668 |
\n| OpenPose | \u2705 \u652f\u6301 | \u26a0\ufe0f \u63a8\u8350GPU | ~8s\/\u56fe | \u5173\u952e\u70b9\u5750\u6807 | \u52a8\u4f5c\u8bc6\u522b |
\n| DeepLabV3+ (MobileNet) | \u2705 \u652f\u6301 | \u274c \u53efCPU\u8fd0\u884c | ~3s\/\u56fe | \u5355\u4ebamask | \u79fb\u52a8\u7aef\u8f7b\u91cf\u5e94\u7528 |
\n| SAM + GroundingDINO | \u2705 \u652f\u6301 | \u2705 \u5f3a\u70c8\u5efa\u8bae | >15s\/\u56fe | \u4efb\u610f\u5206\u5272 | \u9ad8\u7cbe\u5ea6\u79d1\u7814\u7528\u9014 |<\/p>\n
\u7ed3\u8bba：M2FP\u5728\u65e0\u9700GPU\u524d\u63d0\u4e0b\u63d0\u4f9b\u4e86\u6700\u4f73\u7684\u591a\u4eba\u89e3\u6790\u7efc\u5408\u4f53\u9a8c，\u7279\u522b\u9002\u5408\u8d44\u6e90\u53d7\u9650\u73af\u5883\u4e0b\u7684\u957f\u671f\u7a33\u5b9a\u8fd0\u884c\u3002<\/p>\n
\u901a\u8fc7\u90e8\u7f72M2FP\u591a\u4eba\u4eba\u4f53\u89e3\u6790\u670d\u52a1，\u6211\u4eec\u6210\u529f\u9a8c\u8bc1\u4e86\u8001\u65e7\u670d\u52a1\u5668\u5728\u73b0\u4ee3AI\u5de5\u7a0b\u4e2d\u7684\u518d\u5229\u7528\u4ef7\u503c\u3002\u5b83\u4e0d\u4ec5\u89e3\u51b3\u4e86\u4f01\u4e1a\u9762\u4e34\u7684\u786c\u4ef6\u6dd8\u6c70\u96be\u9898，\u66f4\u4e3a\u8fb9\u7f18\u667a\u80fd\u63d0\u4f9b\u4e86\u7ecf\u6d4e\u9ad8\u6548\u7684\u843d\u5730\u8def\u5f84\u3002<\/p>\n
🔧 \u6280\u672f\u4ef7\u503c\u95ed\u73af：
\n– \u8001\u8bbe\u5907\u53ef\u7528：Xeon+ECC\u5185\u5b58\u7ec4\u5408\u8db3\u4ee5\u652f\u6491\u4e2d\u4f4e\u9891AI\u63a8\u7406
\n– \u73af\u5883\u6781\u7a33\u5b9a：\u9501\u5b9aPyTorch 1.13.1 + MMCV-Full 1.7.1\u89c4\u907f\u517c\u5bb9\u95ee\u9898
\n– \u529f\u80fd\u5b8c\u6574：\u81ea\u5e26WebUI\u4e0e\u53ef\u89c6\u5316\u62fc\u56fe，\u5f00\u7bb1\u5373\u7528
\n– \u6613\u4e8e\u96c6\u6210：\u63d0\u4f9bAPI\u63a5\u53e3，\u53ef\u5feb\u901f\u63a5\u5165\u73b0\u6709\u7cfb\u7edf<\/p>\n
💡 \u6700\u540e\u63d0\u9192：\u6280\u672f\u8fed\u4ee3\u4e0d\u5e94\u4ee5\u201c\u6dd8\u6c70\u786c\u4ef6\u201d\u4e3a\u4ee3\u4ef7\u3002\u771f\u6b63\u7684\u667a\u80fd\u5316，\u662f\u8ba9\u6bcf\u4e00\u53f0\u66fe\u4e3a\u4f01\u4e1a\u670d\u5f79\u7684\u673a\u5668，\u90fd\u80fd\u5728\u65b0\u7684\u4f7f\u547d\u4e2d\u7ee7\u7eed\u53d1\u5149\u53d1\u70ed\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u8001\u65e7\u670d\u52a1\u5668\u518d\u5229\u7528：\u90e8\u7f72M2FP\u505a\u5206\u5e03\u5f0f\u4eba\u50cf\u5904\u7406\u8282\u70b9
\n\u5728AI\u6a21\u578b\u65e5\u76ca\u4f9d\u8d56\u9ad8\u6027\u80fdGPU\u7684\u4eca\u5929，\u5927\u91cf\u88ab\u6dd8\u6c70\u7684\u8001\u65e7\u670d\u52a1\u5668\u5f80\u5f80\u88ab\u95f2\u7f6e\u6216\u62a5\u5e9f\u3002\u7136\u800c，\u8bb8\u591a\u8f7b\u91cf\u7ea7\u4f46\u9ad8\u4ef7\u503c\u7684\u63a8\u7406\u4efb\u52a1\u2014\u2014\u5982\u591a\u4eba\u4eba\u4f53\u89e3\u6790\u2014\u2014\u5e76\u4e0d\u4e00\u5b9a\u9700\u8981\u6602\u8d35\u7684\u663e\u5361\u652f\u6301\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u5982\u4f55\u5c06\u4e00\u53f0\u65e0\u72ec\u7acb\u663e\u5361\u7684\u8001\u5f0f\u670d\u52a1\u5668\u201c\u53d8\u5e9f\u4e3a\u5b9d\u201d，\u901a\u8fc7\u90e8\u7f72 M2FP（Mask2Former-Parsing）\u591a\u4eba\u4eba\u4f53\u89e3\u6790\u670d\u52a1，\u6784\u5efa\u4e00\u4e2a\u7a33\u5b9a\u3001\u53ef\u6269\u5c55\u7684\u5206\u5e03\u5f0f\u4eba\u50cf\u5904<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"topic":[],"class_list":["post-60598","post","type-post","status-publish","format-standard","hentry","category-server"],"yoast_head":"\n