{"id":29768,"date":"2025-04-02T16:41:16","date_gmt":"2025-04-02T08:41:16","guid":{"rendered":"https:\/\/www.aisharenet.com\/?p=29768"},"modified":"2025-04-02T16:42:20","modified_gmt":"2025-04-02T08:42:20","slug":"yoloe","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/ja\/yoloe\/","title":{"rendered":"YOLOE\uff1a\u5b9e\u65f6\u89c6\u9891\u68c0\u6d4b\u548c\u5206\u5272\u7269\u4f53\u7684\u5f00\u6e90\u5de5\u5177"},"content":{"rendered":"<p>YOLOE \u662f\u6e05\u534e\u5927\u5b66\u8f6f\u4ef6\u5b66\u9662\u591a\u5a92\u4f53\u667a\u80fd\u7ec4\uff08THU-MIG\uff09\u5f00\u53d1\u7684\u4e00\u4e2a\u5f00\u6e90\u9879\u76ee\uff0c\u5168\u79f0\u201cYou Only Look Once Eye\u201d\u3002\u5b83\u57fa\u4e8e PyTorch \u6846\u67b6\uff0c\u5c5e\u4e8e <a href=\"https:\/\/www.kdjingpai.com\/yolov12\/\">YOLO<\/a> \u7cfb\u5217\u7684\u6269\u5c55\uff0c\u80fd\u5b9e\u65f6\u68c0\u6d4b\u548c\u5206\u5272\u4efb\u4f55\u7269\u4f53\u3002\u9879\u76ee\u6258\u7ba1\u5728 GitHub \u4e0a\uff0c\u6838\u5fc3\u7279\u70b9\u662f\u652f\u6301\u4e09\u79cd\u6a21\u5f0f\uff1a\u6587\u672c\u63d0\u793a\u3001\u89c6\u89c9\u63d0\u793a\u548c\u65e0\u63d0\u793a\u68c0\u6d4b\u3002\u7528\u6237\u53ef\u4ee5\u7528\u6587\u5b57\u6216\u56fe\u7247\u6307\u5b9a\u76ee\u6807\uff0c\u4e5f\u80fd\u8ba9\u6a21\u578b\u81ea\u52a8\u8bc6\u522b\u8d85\u8fc7 1200 \u79cd\u7269\u4f53\u3002\u5b98\u65b9\u6570\u636e\u663e\u793a\uff0cYOLOE \u5728 LVIS \u6570\u636e\u96c6\u4e0a\u7684\u901f\u5ea6\u6bd4 YOLO-Worldv2 \u5feb 1.4 \u500d\uff0c\u8bad\u7ec3\u6210\u672c\u4f4e 3 \u500d\uff0c\u540c\u65f6\u4fdd\u6301\u9ad8\u7cbe\u5ea6\u3002\u6a21\u578b\u8fd8\u80fd\u65e0\u7f1d\u8f6c\u6362\u4e3a YOLOv8 \u6216 YOLO11\uff0c\u65e0\u989d\u5916\u5f00\u9500\uff0c\u9002\u5408\u591a\u79cd\u8bbe\u5907\u90e8\u7f72\u3002<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-29769\" title=\"YOLOE\uff1a\u5b9e\u65f6\u68c0\u6d4b\u548c\u5206\u5272\u4efb\u4f55\u7269\u4f53\u7684\u5f00\u6e90\u5de5\u5177-1\" src=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/04\/41ade968b970190.png\" alt=\"YOLOE\uff1a\u5b9e\u65f6\u68c0\u6d4b\u548c\u5206\u5272\u4efb\u4f55\u7269\u4f53\u7684\u5f00\u6e90\u5de5\u5177-1\" width=\"991\" height=\"402\" srcset=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/04\/41ade968b970190.png 991w, https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/04\/41ade968b970190-768x312.png 768w, https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/04\/41ade968b970190-18x7.png 18w\" sizes=\"auto, (max-width: 991px) 100vw, 991px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h2>\u529f\u80fd\u5217\u8868<\/h2>\n<ul>\n<li>\u652f\u6301\u5b9e\u65f6\u7269\u4f53\u68c0\u6d4b\uff0c\u5feb\u901f\u8bc6\u522b\u56fe\u50cf\u6216\u89c6\u9891\u4e2d\u7684\u76ee\u6807\u3002<\/li>\n<li>\u63d0\u4f9b\u5b9e\u4f8b\u5206\u5272\u529f\u80fd\uff0c\u7cbe\u786e\u52fe\u52d2\u7269\u4f53\u8f6e\u5ed3\u3002<\/li>\n<li>\u652f\u6301\u6587\u672c\u63d0\u793a\u68c0\u6d4b\uff0c\u7528\u6237\u8f93\u5165\u6587\u5b57\u6307\u5b9a\u68c0\u6d4b\u76ee\u6807\u3002<\/li>\n<li>\u63d0\u4f9b\u89c6\u89c9\u63d0\u793a\u68c0\u6d4b\uff0c\u901a\u8fc7\u53c2\u8003\u56fe\u7247\u8bc6\u522b\u76f8\u4f3c\u7269\u4f53\u3002<\/li>\n<li>\u5185\u7f6e\u65e0\u63d0\u793a\u6a21\u5f0f\uff0c\u81ea\u52a8\u68c0\u6d4b\u8d85\u8fc7 1200 \u79cd\u5e38\u89c1\u7269\u4f53\u3002<\/li>\n<li>\u6a21\u578b\u53ef\u91cd\u65b0\u53c2\u6570\u5316\uff0c\u4e0e YOLOv8\/YOLO11 \u65e0\u63a8\u7406\u5f00\u9500\u3002<\/li>\n<li>\u63d0\u4f9b\u591a\u79cd\u9884\u8bad\u7ec3\u6a21\u578b\uff08S\/M\/L \u89c4\u6a21\uff09\uff0c\u652f\u6301\u4e0d\u540c\u6027\u80fd\u9700\u6c42\u3002<\/li>\n<li>\u5f00\u6e90\u4ee3\u7801\u548c\u6587\u6863\uff0c\u65b9\u4fbf\u5f00\u53d1\u8005\u4fee\u6539\u548c\u6269\u5c55\u3002<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2>\u4f7f\u7528\u5e2e\u52a9<\/h2>\n<p>YOLOE \u7684\u4f7f\u7528\u5206\u4e3a\u5b89\u88c5\u548c\u64cd\u4f5c\u4e24\u90e8\u5206\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff0c\u786e\u4fdd\u7528\u6237\u80fd\u8f7b\u677e\u4e0a\u624b\u3002<\/p>\n<h3>\u5b89\u88c5\u6d41\u7a0b<\/h3>\n<ol>\n<li><strong>\u51c6\u5907\u73af\u5883<\/strong><br \/>\n\u9700\u8981 Python 3.10 \u548c PyTorch\u3002\u63a8\u8350\u4f7f\u7528 Conda \u521b\u5efa\u865a\u62df\u73af\u5883\uff1a<\/li>\n<\/ol>\n<pre><code>conda create -n yoloe python=3.10 -y\r\nconda activate yoloe\r\n<\/code><\/pre>\n<ol start=\"2\">\n<li><strong>\u514b\u9686\u4ee3\u7801<\/strong><br \/>\n\u4ece GitHub \u4e0b\u8f7d YOLOE \u9879\u76ee\uff1a<\/li>\n<\/ol>\n<pre><code>git clone https:\/\/github.com\/THU-MIG\/yoloe.git\r\ncd yoloe\r\n<\/code><\/pre>\n<ol start=\"3\">\n<li><strong>\u5b89\u88c5\u4f9d\u8d56<\/strong><br \/>\n\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\uff0c\u5305\u62ec CLIP \u548c MobileCLIP\uff1a<\/li>\n<\/ol>\n<pre><code>pip install -r requirements.txt\r\npip install git+https:\/\/github.com\/THU-MIG\/yoloe.git#subdirectory=third_party\/CLIP\r\npip install git+https:\/\/github.com\/THU-MIG\/yoloe.git#subdirectory=third_party\/ml-mobileclip\r\npip install git+https:\/\/github.com\/THU-MIG\/yoloe.git#subdirectory=third_party\/lvis-api\r\nwget https:\/\/docs-assets.developer.apple.com\/ml-research\/datasets\/mobileclip\/mobileclip_blt.pt\r\n<\/code><\/pre>\n<ol start=\"4\">\n<li><strong>\u4e0b\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/strong><br \/>\nYOLOE \u63d0\u4f9b\u591a\u79cd\u6a21\u578b\uff0c\u6bd4\u5982\u00a0<code>yoloe-v8l-seg.pt<\/code>\u3002\u7528\u4ee5\u4e0b\u547d\u4ee4\u4e0b\u8f7d\uff1a<\/li>\n<\/ol>\n<pre><code>pip install huggingface-hub==0.26.3\r\nhuggingface-cli download jameslahm\/yoloe yoloe-v8l-seg.pt --local-dir pretrain\r\n<\/code><\/pre>\n<p>\u6216\u8005\u7528 Python \u81ea\u52a8\u52a0\u8f7d\uff1a<\/p>\n<pre><code>from ultralytics import YOLOE\r\nmodel = YOLOE.from_pretrained(\"jameslahm\/yoloe-v8l-seg.pt\")\r\n<\/code><\/pre>\n<ol start=\"5\">\n<li><strong>\u9a8c\u8bc1\u5b89\u88c5<\/strong><br \/>\n\u8fd0\u884c\u6d4b\u8bd5\u547d\u4ee4\uff0c\u68c0\u67e5\u73af\u5883\u662f\u5426\u6b63\u5e38\uff1a<\/p>\n<pre><code>python predict_text_prompt.py --source ultralytics\/assets\/bus.jpg --checkpoint pretrain\/yoloe-v8l-seg.pt --names person dog cat --device cuda:0\r\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<h3>\u4e3b\u8981\u529f\u80fd\u64cd\u4f5c<\/h3>\n<h4>1. \u6587\u672c\u63d0\u793a\u68c0\u6d4b<\/h4>\n<ul>\n<li><strong>\u529f\u80fd\u8bf4\u660e<\/strong>\uff1a\u8f93\u5165\u6587\u5b57\uff0c\u68c0\u6d4b\u5bf9\u5e94\u7269\u4f53\u3002<\/li>\n<li><strong>\u64cd\u4f5c\u6b65\u9aa4<\/strong>\uff1a\n<ol>\n<li>\u51c6\u5907\u56fe\u7247\uff0c\u6bd4\u5982\u00a0<code>bus.jpg<\/code>\u3002<\/li>\n<li>\u8fd0\u884c\u547d\u4ee4\uff0c\u6307\u5b9a\u76ee\u6807\uff08\u6bd4\u5982\u201c\u4eba\u3001\u72d7\u3001\u732b\u201d\uff09\uff1a\n<pre><code>python predict_text_prompt.py --source ultralytics\/assets\/bus.jpg --checkpoint pretrain\/yoloe-v8l-seg.pt --names person dog cat --device cuda:0\r\n<\/code><\/pre>\n<\/li>\n<li>\u67e5\u770b\u7ed3\u679c\uff0c\u56fe\u7247\u4f1a\u6807\u6ce8\u68c0\u6d4b\u5230\u7684\u7269\u4f53\u3002<\/li>\n<\/ol>\n<\/li>\n<li><strong>\u8c03\u6574\u65b9\u6cd5<\/strong>\uff1a\u5982\u679c\u6f0f\u68c0\uff0c\u53ef\u964d\u4f4e\u7f6e\u4fe1\u5ea6\u9608\u503c\uff1a\n<pre><code>--conf 0.001\r\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<h4>2. \u89c6\u89c9\u63d0\u793a\u68c0\u6d4b<\/h4>\n<ul>\n<li><strong>\u529f\u80fd\u8bf4\u660e<\/strong>\uff1a\u7528\u53c2\u8003\u56fe\u7247\u68c0\u6d4b\u76f8\u4f3c\u7269\u4f53\u3002<\/li>\n<li><strong>\u64cd\u4f5c\u6b65\u9aa4<\/strong>\uff1a\n<ol>\n<li>\u51c6\u5907\u53c2\u8003\u56fe\u7247\u548c\u76ee\u6807\u56fe\u7247\u3002<\/li>\n<li>\u8bad\u7ec3\u89c6\u89c9\u63d0\u793a\u6a21\u5757\uff1a\n<pre><code>python tools\/convert_segm2det.py\r\npython train_vp.py\r\npython tools\/get_vp_segm.py\r\n<\/code><\/pre>\n<\/li>\n<li>\u8fd0\u884c\u68c0\u6d4b\uff1a\n<pre><code>python predict_visual_prompt.py --source test.jpg --ref reference.jpg --checkpoint pretrain\/yoloe-v8l-seg.pt\r\n<\/code><\/pre>\n<\/li>\n<li>\u68c0\u67e5\u8f93\u51fa\uff0c\u786e\u8ba4\u7ed3\u679c\u3002<\/li>\n<\/ol>\n<\/li>\n<li><strong>\u6ce8\u610f\u4e8b\u9879<\/strong>\uff1a\u53c2\u8003\u56fe\u7247\u9700\u6e05\u6670\uff0c\u7279\u5f81\u660e\u663e\u3002<\/li>\n<\/ul>\n<h4>3. \u65e0\u63d0\u793a\u68c0\u6d4b<\/h4>\n<ul>\n<li><strong>\u529f\u80fd\u8bf4\u660e<\/strong>\uff1a\u81ea\u52a8\u8bc6\u522b\u56fe\u7247\u4e2d\u7684\u7269\u4f53\uff0c\u65e0\u9700\u8f93\u5165\u63d0\u793a\u3002<\/li>\n<li><strong>\u64cd\u4f5c\u6b65\u9aa4<\/strong>\uff1a\n<ol>\n<li>\u786e\u4fdd\u6a21\u578b\u52a0\u8f7d\u4e86\u9884\u8bad\u7ec3\u8bcd\u6c47\u8868\uff08\u652f\u6301 1200+ \u7c7b\u522b\uff09\u3002<\/li>\n<li>\u8fd0\u884c\u547d\u4ee4\uff1a\n<pre><code>python predict_prompt_free.py --source test.jpg --checkpoint pretrain\/yoloe-v8l-seg.pt --device cuda:0\r\n<\/code><\/pre>\n<\/li>\n<li>\u67e5\u770b\u7ed3\u679c\uff0c\u6240\u6709\u7269\u4f53\u4f1a\u88ab\u6807\u6ce8\u3002<\/li>\n<\/ol>\n<\/li>\n<li><strong>\u8c03\u6574\u65b9\u6cd5<\/strong>\uff1a\u82e5\u68c0\u6d4b\u4e0d\u5168\uff0c\u53ef\u589e\u52a0\u6700\u5927\u68c0\u6d4b\u6570\uff1a\n<pre><code>--max_det 1000\r\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<h4>4. \u6a21\u578b\u8f6c\u6362\u4e0e\u90e8\u7f72<\/h4>\n<ul>\n<li><strong>\u529f\u80fd\u8bf4\u660e<\/strong>\uff1a\u5c06 YOLOE \u8f6c\u6362\u4e3a YOLOv8\/YOLO11 \u683c\u5f0f\uff0c\u90e8\u7f72\u5230\u4e0d\u540c\u8bbe\u5907\u3002<\/li>\n<li><strong>\u64cd\u4f5c\u6b65\u9aa4<\/strong>\uff1a\n<ol>\n<li>\u5b89\u88c5\u5bfc\u51fa\u5de5\u5177\uff1a\n<pre><code>pip install onnx coremltools onnxslim\r\n<\/code><\/pre>\n<\/li>\n<li>\u8fd0\u884c\u5bfc\u51fa\u547d\u4ee4\uff1a\n<pre><code>python export.py --checkpoint pretrain\/yoloe-v8l-seg.pt\r\n<\/code><\/pre>\n<\/li>\n<li>\u8f93\u51fa\u683c\u5f0f\u652f\u6301 TensorRT\uff08GPU\uff09\u6216 CoreML\uff08iPhone\uff09\u3002<\/li>\n<\/ol>\n<\/li>\n<li><strong>\u6027\u80fd\u6570\u636e<\/strong>\uff1a\u5728 T4 GPU \u4e0a\uff0c<code>yoloe-v8l-seg.pt<\/code>\u00a0\u7684 FPS \u4e3a 102.5\uff1b\u5728 iPhone 12 \u4e0a\u4e3a 27.2\u3002<\/li>\n<\/ul>\n<h4>5. \u8bad\u7ec3\u81ea\u5b9a\u4e49\u6a21\u578b<\/h4>\n<ul>\n<li><strong>\u529f\u80fd\u8bf4\u660e<\/strong>\uff1a\u7528\u81ea\u5df1\u7684\u6570\u636e\u96c6\u8bad\u7ec3 YOLOE\u3002<\/li>\n<li><strong>\u64cd\u4f5c\u6b65\u9aa4<\/strong>\uff1a\n<ol>\n<li>\u51c6\u5907\u6570\u636e\u96c6\uff0c\u6bd4\u5982 Objects365v1 \u6216 GQA\u3002<\/li>\n<li>\u751f\u6210\u5206\u5272\u6807\u6ce8\uff1a\n<pre><code>python tools\/generate_sam_masks.py --img-path ..\/datasets\/Objects365v1\/images\/train --json-path ..\/datasets\/Objects365v1\/annotations\/objects365_train.json\r\n<\/code><\/pre>\n<\/li>\n<li>\u751f\u6210\u8bad\u7ec3\u7f13\u5b58\uff1a\n<pre><code>python tools\/generate_grounding_cache.py --img-path ..\/datasets\/Objects365v1\/images\/train --json-path ..\/datasets\/Objects365v1\/annotations\/objects365_train_segm.json\r\n<\/code><\/pre>\n<\/li>\n<li>\u8fd0\u884c\u8bad\u7ec3\uff1a\n<pre><code>python train_seg.py\r\n<\/code><\/pre>\n<\/li>\n<li>\u9a8c\u8bc1\u6548\u679c\uff1a\n<pre><code>python val.py\r\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<h3>\u5176\u4ed6\u5de5\u5177<\/h3>\n<ul>\n<li><strong>\u7f51\u9875\u6f14\u793a<\/strong>\uff1a\u7528 Gradio \u542f\u52a8\u754c\u9762\uff1a\n<pre><code>pip install gradio==4.42.0\r\npython app.py\r\n<\/code><\/pre>\n<p>\u8bbf\u95ee\u00a0<code>http:\/\/127.0.0.1:7860<\/code>\u3002<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2>\u5e94\u7528\u573a\u666f<\/h2>\n<ol>\n<li><strong>\u5b89\u9632\u76d1\u63a7<\/strong><br \/>\n\u5728\u89c6\u9891\u4e2d\u5b9e\u65f6\u68c0\u6d4b\u4eba\u6216\u7269\u4f53\uff0c\u6807\u6ce8\u8f6e\u5ed3\uff0c\u7528\u4e8e\u5b89\u5168\u7ba1\u7406\u3002<\/li>\n<li><strong>\u667a\u80fd\u4ea4\u901a<\/strong><br \/>\n\u8bc6\u522b\u9053\u8def\u4e0a\u7684\u8f66\u8f86\u548c\u884c\u4eba\uff0c\u652f\u6301\u6d41\u91cf\u5206\u6790\u6216\u81ea\u52a8\u9a7e\u9a76\u3002<\/li>\n<li><strong>\u5de5\u4e1a\u8d28\u68c0<\/strong><br \/>\n\u7528\u89c6\u89c9\u63d0\u793a\u68c0\u6d4b\u96f6\u4ef6\u7f3a\u9677\uff0c\u63d0\u9ad8\u751f\u4ea7\u6548\u7387\u3002<\/li>\n<li><strong>\u79d1\u5b66\u7814\u7a76<\/strong><br \/>\n\u5904\u7406\u5b9e\u9a8c\u56fe\u50cf\uff0c\u81ea\u52a8\u6807\u6ce8\u7269\u4f53\uff0c\u52a0\u901f\u6570\u636e\u5904\u7406\u3002<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h2>QA<\/h2>\n<ol>\n<li><strong>YOLOE \u548c YOLOv8 \u6709\u4ec0\u4e48\u4e0d\u540c\uff1f<\/strong><br \/>\nYOLOE \u652f\u6301\u5f00\u653e\u573a\u666f\u68c0\u6d4b\uff08\u6587\u672c\u3001\u89c6\u89c9\u3001\u65e0\u63d0\u793a\uff09\uff0c\u800c YOLOv8 \u9650\u4e8e\u56fa\u5b9a\u7c7b\u522b\u3002YOLOE \u8fd8\u80fd\u8f6c\u6362\u4e3a YOLOv8\uff0c\u65e0\u989d\u5916\u5f00\u9500\u3002<\/li>\n<li><strong>\u9700\u8981 GPU \u5417\uff1f<\/strong><br \/>\n\u4e0d\u9700\u8981\u3002CPU \u53ef\u8fd0\u884c\uff0c\u4f46 GPU\uff08\u5982 CUDA\uff09\u4f1a\u66f4\u5feb\u3002<\/li>\n<li><strong>\u68c0\u6d4b\u4e0d\u51c6\u600e\u4e48\u529e\uff1f<\/strong><br \/>\n\u964d\u4f4e\u7f6e\u4fe1\u5ea6\u9608\u503c\uff08<code>--conf 0.001<\/code>\uff09\u6216\u589e\u52a0\u68c0\u6d4b\u6570\uff08<code>--max_det 1000<\/code>\uff09\u3002<\/li>\n<li><strong>\u652f\u6301\u54ea\u4e9b\u8bbe\u5907\uff1f<\/strong><br \/>\n\u652f\u6301 PC\uff08TensorRT\uff09\u3001iPhone\uff08CoreML\uff09\u7b49\u591a\u79cd\u8bbe\u5907\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>YOLOE \u662f\u6e05\u534e\u5927\u5b66\u8f6f\u4ef6\u5b66\u9662\u591a\u5a92\u4f53\u667a\u80fd\u7ec4\uff08THU-MIG\uff09\u5f00\u53d1\u7684\u4e00\u4e2a\u5f00\u6e90\u9879\u76ee\uff0c\u5168\u79f0\u201cYou Only Look Once Eye\u201d\u3002\u5b83\u57fa\u4e8e PyTorch \u6846\u67b6\uff0c\u5c5e\u4e8e YOLO \u7cfb\u5217\u7684\u6269\u5c55\uff0c\u80fd\u5b9e\u65f6\u68c0\u6d4b\u548c\u5206\u5272\u4efb\u4f55\u7269\u4f53\u3002\u9879\u76ee\u6258\u7ba1\u5728 GitHu&#8230;<\/p>\n","protected":false},"author":1,"featured_media":62189,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[230,219,378],"class_list":["post-29768","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tool","tag-aikaiyuanxiangmu","tag-aikoutugaibeijing","tag-shijuemubiaojiance"],"_links":{"self":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/posts\/29768","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/comments?post=29768"}],"version-history":[{"count":0,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/posts\/29768\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/media\/62189"}],"wp:attachment":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/media?parent=29768"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/categories?post=29768"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/tags?post=29768"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}