YOLOX \u5b66\u7fd2\u65b9\u6cd5\u30ac\u30a4\u30c9<\/title>\n <style>\n body { font-family: \"Noto Sans JP\", sans-serif; line-height: 1.6; padding: 1rem; max-width: 900px; margin: auto; }\n h1, h2, h3 { color: #2c3e50; }\n h1 { border-bottom: 2px solid #2c3e50; padding-bottom: 0.3em; }\n code { background: #f1f1f1; padding: 2px 4px; border-radius: 4px; }\n pre { background: #f9f9f9; padding: 1em; border-radius: 4px; overflow-x: auto; }\n ul { margin-left: 1.2em; }\n section { margin-top: 1.5em; }\n <\/style>\n<\/head>\n<body>\n\n<section>\n <h2>1. \u306f\u3058\u3081\u306b<\/h2>\n <p>\n \u305f\u3073\u305f\u3073Blog\u306b\u7d39\u4ecb\u3057\u3066\u3044\u308bYOLOX \u306f\u9ad8\u901f\u30fb\u9ad8\u7cbe\u5ea6\u306a\u7269\u4f53\u691c\u51fa\u30e2\u30c7\u30eb\u3068\u3057\u3066\u6ce8\u76ee\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u672c\u7a3f\u3067\u306f\u3001YOLOX \u306e\u5b66\u7fd2\u6e96\u5099\u304b\u3089\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u3001\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u307e\u3067\u3001\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002\u521d\u5fc3\u8005\u3067\u3082\u8ff7\u308f\u306a\u3044\u3088\u3046\u3001\u5177\u4f53\u7684\u306a\u30b3\u30de\u30f3\u30c9\u4f8b\u3084\u8a2d\u5b9a\u30d5\u30a1\u30a4\u30eb\u4f8b\u3092\u4ea4\u3048\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\n <\/p>\n<\/section>\n\n<section>\n <h2>1.5. YOLOX \u306e\u30e1\u30ea\u30c3\u30c8<\/h2>\n <ul>\n <li>\u30a2\u30f3\u30ab\u30fc\u30d5\u30ea\u30fc\u8a2d\u8a08\u3067\u30dc\u30c3\u30af\u30b9\u4e88\u6e2c\u304c\u30b7\u30f3\u30d7\u30eb\u304b\u3064\u9ad8\u901f<\/li>\n <li>Decoupled Head \u306b\u3088\u308b\u691c\u51fa\u6027\u80fd\u5411\u4e0a\u3068\u5b66\u7fd2\u5b89\u5b9a\u6027<\/li>\n <li>Mosaic \u3084 MixUp\u3001RandomAffine \u3068\u3044\u3063\u305f\u5f37\u529b\u306a\u30c7\u30fc\u30bf\u62e1\u5f35\u3067\u6c4e\u5316\u6027\u80fd\u304c\u9ad8\u3044<\/li>\n <li>\u5b66\u7fd2\u7387\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u3084\u30b3\u30b5\u30a4\u30f3\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306a\u3069\u6700\u65b0\u306e\u5b66\u7fd2\u30b9\u30b1\u30b8\u30e5\u30fc\u30e9\u3092\u642d\u8f09<\/li>\n <li>FP16 \u5bfe\u5fdc\u3067\u30e1\u30e2\u30ea\u52b9\u7387\u304c\u826f\u304f\u3001\u5927\u898f\u6a21\u30d0\u30c3\u30c1\u5b66\u7fd2\u304c\u53ef\u80fd<\/li>\n <li>\u516c\u5f0f\u5b9f\u88c5\u304c\u8c4a\u5bcc\u306a\u8a2d\u5b9a\u4f8b\uff08YOLOX-S\/M\/L\/X\uff09\u3092\u7528\u3044\u305f\u8fc5\u901f\u306a\u5b9f\u9a13\u958b\u59cb\u304c\u53ef\u80fd<\/li>\n <li>ONNX\/TensorRT \u5909\u63db\u3067\u63a8\u8ad6\u6700\u9069\u5316\u3057\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u5fdc\u7528\u306b\u9069\u5408<\/li>\n <\/ul>\n<\/section>\n\n<section>\n <h2>2. \u74b0\u5883\u69cb\u7bc9<\/h2>\n <h3>2.1 Python \u3068\u4f9d\u5b58\u30e9\u30a4\u30d6\u30e9\u30ea<\/h3>\n <ul>\n <li>Python 3.8\u301c3.10 \u3092\u63a8\u5968\u3002<\/li>\n <li>\u4eee\u60f3\u74b0\u5883\u306e\u4f5c\u6210\uff1a\n <pre><code>python -m venv yolox_env\nsource yolox_env\/bin\/activate<\/code><\/pre>\n <\/li>\n <li>\u5fc5\u8981\u30d1\u30c3\u30b1\u30fc\u30b8\uff1a\n <pre><code>pip install torch torchvision cython -f https:\/\/download.pytorch.org\/whl\/torch_stable.html\npip install -U pip\npip install -r requirements.txt<\/code><\/pre>\n <\/li>\n <\/ul>\n <h3>2.2 YOLOX \u306e\u30af\u30ed\u30fc\u30f3<\/h3>\n <pre><code>git clone https:\/\/github.com\/Megvii-BaseDetection\/YOLOX.git\ncd YOLOX\npip install -v -e . # \u958b\u767a\u30e2\u30fc\u30c9\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>3. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6e96\u5099<\/h2>\n <h3>3.1 COCO \u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u3078\u306e\u5909\u63db<\/h3>\n <p>\n \u72ec\u81ea\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u3001COCO \u5f62\u5f0f\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f JSON \u5f62\u5f0f\u3067\u4ee5\u4e0b\u306e\u69cb\u6210\u304c\u5fc5\u8981\u3067\u3059\u3002\n <\/p>\n <ul>\n <li><code>images\/<\/code> \u30d5\u30a9\u30eb\u30c0\uff1a\u753b\u50cf\u30d5\u30a1\u30a4\u30eb<\/li>\n <li><code>annotations\/instances_train2017.json<\/code>\uff1a\u5b66\u7fd2\u7528\u30e9\u30d9\u30eb<\/li>\n <li><code>annotations\/instances_val2017.json<\/code>\uff1a\u691c\u8a3c\u7528\u30e9\u30d9\u30eb<\/li>\n <\/ul>\n <h3>3.2 \u30c7\u30fc\u30bf\u306e\u5206\u5272<\/h3>\n <p>\u5b66\u7fd2\u7528\u3068\u691c\u8a3c\u7528\u3092 8:2 \u306a\u3069\u3067\u5206\u5272\u3057\u307e\u3059\u3002\u30b9\u30af\u30ea\u30d7\u30c8\u4f8b\uff1a<\/p>\n <pre><code>python tools\/train_val_split.py --input_dir .\/dataset --out_dir .\/coco_dataset --val_ratio 0.2<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>4. \u8a2d\u5b9a\u30d5\u30a1\u30a4\u30eb\uff08.yaml\uff09<\/h2>\n <p>YOLOX \u3067\u306f <code>exps\/default\/yolox_s.py<\/code> \u306e\u3088\u3046\u306a Python \u30b9\u30af\u30ea\u30d7\u30c8\u3067\u8a2d\u5b9a\u3092\u7ba1\u7406\u3057\u307e\u3059\u3002\u4e3b\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\uff1a<\/p>\n <ul>\n <li><code>num_classes<\/code>\uff1a\u30af\u30e9\u30b9\u6570<\/li>\n <li><code>input_size<\/code>\uff1a\u5165\u529b\u89e3\u50cf\u5ea6\uff08\u4f8b\uff1a640\uff09<\/li>\n <li><code>random_size<\/code>\uff1a\u30e9\u30f3\u30c0\u30e0\u30ea\u30b5\u30a4\u30ba\u30d0\u30c3\u30c1\u306e\u7bc4\u56f2<\/li>\n <li><code>batch_size<\/code>\uff1a\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba<\/li>\n <li><code>max_epoch<\/code>\uff1a\u6700\u5927\u30a8\u30dd\u30c3\u30af\u6570<\/li>\n <li><code>lr<\/code>\uff1a\u521d\u671f\u5b66\u7fd2\u7387<\/li>\n <\/ul>\n <p>\u4f8b\uff1a<\/p>\n <pre><code>exp = {\n \"name\": \"yolox_s_custom\",\n \"num_classes\": 3,\n \"input_size\": (640, 640),\n \"random_size\": (10, 20),\n \"batch_size\": 16,\n \"max_epoch\": 50,\n \"lr\": 0.01,\n}<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>5. \u5b66\u7fd2\u5b9f\u884c<\/h2>\n <p>\u57fa\u672c\u7684\u306a\u30b3\u30de\u30f3\u30c9\uff1a<\/p>\n <pre><code>python tools\/train.py -f exps\/default\/yolox_s.py -d 1 -b 16 --fp16 -o<\/code><\/pre>\n <ul>\n <li><code>-d<\/code>\uff1aGPU \u6570<\/li>\n <li><code>-b<\/code>\uff1a\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba<\/li>\n <li><code>--fp16<\/code>\uff1a\u534a\u7cbe\u5ea6\uff08AMP\uff09\u6709\u52b9<\/li>\n <li><code>-o<\/code>\uff1a\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u8a2d\u5b9a\u8aad\u307f\u8fbc\u307f<\/li>\n <\/ul>\n <p>\n \u5b66\u7fd2\u4e2d\u306f TensorBoard \u3067\u30ed\u30b0\u3092\u53ef\u8996\u5316\u3067\u304d\u307e\u3059\u3002\n <\/p>\n <pre><code>tensorboard --logdir runs\/<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>6. \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574<\/h2>\n <h3>6.1 \u5b66\u7fd2\u7387\u30b9\u30b1\u30b8\u30e5\u30fc\u30e9<\/h3>\n <p>\n Cosine Annealing \u3084 StepLR \u3092\u8a66\u3057\u3001\u53ce\u675f\u30b9\u30d4\u30fc\u30c9\u3068\u6700\u7d42 mAP \u3092\u6539\u5584\u3057\u307e\u3059\u3002\n <\/p>\n <h3>6.2 \u30d0\u30c3\u30c1\u30b5\u30a4\u30ba<\/h3>\n <p>\n \u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c\u5927\u304d\u3044\u307b\u3069\u5b89\u5b9a\u3057\u307e\u3059\u304c\u3001GPU \u30e1\u30e2\u30ea\u306b\u6ce8\u610f\u3002\u5c0f\u3055\u3044\u5834\u5408\u306f\u5b66\u7fd2\u7387\u3092\u4e0b\u3052\u308b\u306a\u3069\u306e\u5de5\u592b\u3092\u3002\n <\/p>\n <h3>6.3 \u30c7\u30fc\u30bf\u62e1\u5f35<\/h3>\n <ul>\n <li>Mosaic\uff1a4 \u753b\u50cf\u3092\u7d50\u5408\u3057\u3066\u5b66\u7fd2\u3055\u305b\u308b<\/li>\n <li>MixUp\uff1a2 \u753b\u50cf\u3092\u91cd\u306d\u3066\u30e9\u30d9\u30eb\u3092\u88dc\u9593<\/li>\n <li>RandomFlip \/ ColorJitter\uff1a\u57fa\u672c\u62e1\u5f35<\/li>\n <\/ul>\n<\/section>\n\n<section>\n <h2>7. \u691c\u8a3c\u3068\u8a55\u4fa1<\/h2>\n <p>\u5b66\u7fd2\u5f8c\u3001\u691c\u8a3c\u30c7\u30fc\u30bf\u3067 mAP (0.5:0.95) \u3092\u7b97\u51fa\uff1a<\/p>\n <pre><code>python tools\/eval.py -n yolox-s -c .\/weights\/yolox_s.pth -b 16 -d 1 --conf 0.001<\/code><\/pre>\n <p>\u7d50\u679c\u306f JSON \u5f62\u5f0f\u3067\u3082\u51fa\u529b\u53ef\u80fd\u3002\u4f4e\u3044\u5834\u5408\u306f\u524d\u7bc0\u306e\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u898b\u76f4\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/section>\n\n<section>\n <h2>8. \u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0<\/h2>\n <h3>8.1 \u5b66\u7fd2\u304c\u9032\u307e\u306a\u3044<\/h3>\n <ul>\n <li>\u5b66\u7fd2\u7387\u304c\u9ad8\u3059\u304e\u308b \u2192 \u5c0f\u3055\u304f\u3059\u308b<\/li>\n <li>\u30c7\u30fc\u30bf\u30d0\u30e9\u30f3\u30b9\u304c\u504f\u3063\u3066\u3044\u308b \u2192 \u30ea\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0<\/li>\n <li>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c\u5c0f\u3055\u3044 \u2192 \u5c0f\u3055\u304f\u3066\u3082\u30ec\u30a4\u30e4\u30ce\u30eb\u30e0\u3092\u8ffd\u52a0<\/li>\n <\/ul>\n <h3>8.2 \u30aa\u30fc\u30d0\u30fc\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0<\/h3>\n <ul>\n <li>\u30c7\u30fc\u30bf\u62e1\u5f35\u306e\u5f37\u5316<\/li>\n <li>\u65e9\u671f\u505c\u6b62\uff08EarlyStopping\uff09\u306e\u5c0e\u5165<\/li>\n <li>\u91cd\u307f\u6e1b\u8870\uff08weight decay\uff09\u306e\u8abf\u6574<\/li>\n <\/ul>\n<\/section>\n\n<section>\n <h2>9. \u63a8\u8ad6\u3068\u30c7\u30d7\u30ed\u30a4<\/h2>\n <h3>9.1 ONNX \u30a8\u30af\u30b9\u30dd\u30fc\u30c8<\/h3>\n <pre><code>python tools\/export_onnx.py -n yolox-s -c .\/weights\/yolox_s.pth --opset 12<\/code><\/pre>\n <h3>9.2 TensorRT \u6700\u9069\u5316<\/h3>\n <p>\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3057\u305f ONNX \u30e2\u30c7\u30eb\u3092 TensorRT \u3067\u6700\u9069\u5316\u3057\u3066\u9ad8\u901f\u63a8\u8ad6\u3002<\/p>\n <pre><code>trtexec --onnx=yolox_s.onnx --fp16 --saveEngine=yolox_s.trt<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>10. \u307e\u3068\u3081\u3068\u6b21\u306e\u30b9\u30c6\u30c3\u30d7<\/h2>\n <p>\n \u672c\u30ac\u30a4\u30c9\u3067\u306f\u3001YOLOX \u306e\u74b0\u5883\u69cb\u7bc9\u304b\u3089\u30c7\u30fc\u30bf\u6e96\u5099\u3001\u5b66\u7fd2\u3001\u8a55\u4fa1\u3001\u63a8\u8ad6\u307e\u3067\u3092\u7db2\u7f85\u3057\u307e\u3057\u305f\u3002\n \u307e\u305a\u306f\u516c\u5f0f\u306e <a href=\"https:\/\/github.com\/Megvii-BaseDetection\/YOLOX\">YOLOX \u30ea\u30dd\u30b8\u30c8\u30ea<\/a> \u3092\u53c2\u7167\u3057\u3001\u5c0f\u898f\u6a21\u30c7\u30fc\u30bf\u3067\u52d5\u4f5c\u78ba\u8a8d\u5f8c\u306b\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3092\u52a0\u3048\u3066\u3044\u304f\u3053\u3068\u3092\u304a\u3059\u3059\u3081\u3057\u307e\u3059\u3002\n <\/p>\n \n<\/section>\n\n<section>\n <h2>11. \u30e9\u30a4\u30bb\u30f3\u30b9\u3068\u5546\u7528\u5229\u7528\u306b\u95a2\u3059\u308b\u6ce8\u610f<\/h2>\n <p>\n YOLOX \u306f Apache License 2.0 \u306e\u3082\u3068\u3067\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5546\u7528\u30fb\u975e\u5546\u7528\u5229\u7528\u3068\u3082\u306b\u307b\u307c\u5236\u9650\u306a\u304f\u5229\u7528\u53ef\u80fd\u3067\u3059\u304c\u3001\u4ee5\u4e0b\u306e\u70b9\u306b\u3054\u6ce8\u610f\u304f\u3060\u3055\u3044\u3002\n <\/p>\n <ul>\n <li>\u8457\u4f5c\u6a29\u8868\u793a\u304a\u3088\u3073\u30e9\u30a4\u30bb\u30f3\u30b9\u6587\u3092\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306b\u542b\u3081\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n <li>\u6d3e\u751f\u7269\uff08\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u6e08\u30e2\u30c7\u30eb\u3084\u6539\u5909\u30b3\u30fc\u30c9\uff09\u3092\u518d\u914d\u5e03\u3059\u308b\u5834\u5408\u3082\u3001\u540c\u3058\u30e9\u30a4\u30bb\u30f3\u30b9\u3092\u9069\u7528\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u304c\u3001\u5143\u306e\u30e9\u30a4\u30bb\u30f3\u30b9\u6587\u3092\u660e\u793a\u3059\u308b\u3053\u3068\u304c\u63a8\u5968\u3055\u308c\u307e\u3059\u3002<\/li>\n <li>\u5b66\u7fd2\u6642\u306b\u5229\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3084\u524d\u51e6\u7406\u30c4\u30fc\u30eb\u306b\u5225\u30e9\u30a4\u30bb\u30f3\u30b9\u306e\u3082\u306e\u304c\u542b\u307e\u308c\u308b\u5834\u5408\u3001\u305d\u306e\u30e9\u30a4\u30bb\u30f3\u30b9\u6761\u4ef6\u306b\u3082\u5f93\u3046\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n <li>ONNX \u3084 TensorRT \u306a\u3069\u4ed6\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306b\u5909\u63db\u30fb\u7d44\u307f\u8fbc\u3093\u3067\u5229\u7528\u3059\u308b\u969b\u3001\u5909\u63db\u5f8c\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3084\u30d7\u30e9\u30b0\u30a4\u30f3\u306e\u30e9\u30a4\u30bb\u30f3\u30b9\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n <li>\u7279\u8a31\u6a29\u3084\u5546\u6a19\u6a29\u306a\u3069\u3001\u30e9\u30a4\u30bb\u30f3\u30b9\u5916\u306e\u77e5\u7684\u8ca1\u7523\u6a29\u306f\u9069\u7528\u5916\u3067\u3059\u306e\u3067\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002<\/li>\n 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$\"\"$ <\/div>\n\n\n\n \n \n YOLOX \u5b66\u7fd2\u65b9\u6cd5\u30ac\u30a4\u30c9<\/title>\n <style>\n body { font-family: \"Noto Sans JP\", sans-serif; line-height: 1.6; padding: 1rem; max-width: 900px; margin: auto; }\n h1, h2, h3 { color: #2c3e50; }\n h1 { border-bottom: 2px solid #2c3e50; padding-bottom: 0.3em; }\n code { background: #f1f1f1; padding: 2px 4px; border-radius: 4px; }\n pre { background: #f9f9f9; padding: 1em; border-radius: 4px; overflow-x: auto; }\n ul { margin-left: 1.2em; }\n section { margin-top: 1.5em; }\n <\/style>\n<\/head>\n<body>\n\n<section>\n <h2>1. \u306f\u3058\u3081\u306b<\/h2>\n <p>\n \u305f\u3073\u305f\u3073Blog\u306b\u7d39\u4ecb\u3057\u3066\u3044\u308bYOLOX \u306f\u9ad8\u901f\u30fb\u9ad8\u7cbe\u5ea6\u306a\u7269\u4f53\u691c\u51fa\u30e2\u30c7\u30eb\u3068\u3057\u3066\u6ce8\u76ee\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u672c\u7a3f\u3067\u306f\u3001YOLOX \u306e\u5b66\u7fd2\u6e96\u5099\u304b\u3089\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u3001\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u307e\u3067\u3001\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002\u521d\u5fc3\u8005\u3067\u3082\u8ff7\u308f\u306a\u3044\u3088\u3046\u3001\u5177\u4f53\u7684\u306a\u30b3\u30de\u30f3\u30c9\u4f8b\u3084\u8a2d\u5b9a\u30d5\u30a1\u30a4\u30eb\u4f8b\u3092\u4ea4\u3048\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\n <\/p>\n<\/section>\n\n<section>\n <h2>1.5. YOLOX \u306e\u30e1\u30ea\u30c3\u30c8<\/h2>\n <ul>\n <li>\u30a2\u30f3\u30ab\u30fc\u30d5\u30ea\u30fc\u8a2d\u8a08\u3067\u30dc\u30c3\u30af\u30b9\u4e88\u6e2c\u304c\u30b7\u30f3\u30d7\u30eb\u304b\u3064\u9ad8\u901f<\/li>\n <li>Decoupled Head \u306b\u3088\u308b\u691c\u51fa\u6027\u80fd\u5411\u4e0a\u3068\u5b66\u7fd2\u5b89\u5b9a\u6027<\/li>\n <li>Mosaic \u3084 MixUp\u3001RandomAffine \u3068\u3044\u3063\u305f\u5f37\u529b\u306a\u30c7\u30fc\u30bf\u62e1\u5f35\u3067\u6c4e\u5316\u6027\u80fd\u304c\u9ad8\u3044<\/li>\n <li>\u5b66\u7fd2\u7387\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u3084\u30b3\u30b5\u30a4\u30f3\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306a\u3069\u6700\u65b0\u306e\u5b66\u7fd2\u30b9\u30b1\u30b8\u30e5\u30fc\u30e9\u3092\u642d\u8f09<\/li>\n <li>FP16 \u5bfe\u5fdc\u3067\u30e1\u30e2\u30ea\u52b9\u7387\u304c\u826f\u304f\u3001\u5927\u898f\u6a21\u30d0\u30c3\u30c1\u5b66\u7fd2\u304c\u53ef\u80fd<\/li>\n <li>\u516c\u5f0f\u5b9f\u88c5\u304c\u8c4a\u5bcc\u306a\u8a2d\u5b9a\u4f8b\uff08YOLOX-S\/M\/L\/X\uff09\u3092\u7528\u3044\u305f\u8fc5\u901f\u306a\u5b9f\u9a13\u958b\u59cb\u304c\u53ef\u80fd<\/li>\n <li>ONNX\/TensorRT \u5909\u63db\u3067\u63a8\u8ad6\u6700\u9069\u5316\u3057\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u5fdc\u7528\u306b\u9069\u5408<\/li>\n <\/ul>\n<\/section>\n\n<section>\n <h2>2. \u74b0\u5883\u69cb\u7bc9<\/h2>\n <h3>2.1 Python \u3068\u4f9d\u5b58\u30e9\u30a4\u30d6\u30e9\u30ea<\/h3>\n <ul>\n <li>Python 3.8\u301c3.10 \u3092\u63a8\u5968\u3002<\/li>\n <li>\u4eee\u60f3\u74b0\u5883\u306e\u4f5c\u6210\uff1a\n <pre><code>python -m venv yolox_env\nsource yolox_env\/bin\/activate<\/code><\/pre>\n <\/li>\n <li>\u5fc5\u8981\u30d1\u30c3\u30b1\u30fc\u30b8\uff1a\n <pre><code>pip install torch torchvision cython -f https:\/\/download.pytorch.org\/whl\/torch_stable.html\npip install -U pip\npip install -r requirements.txt<\/code><\/pre>\n <\/li>\n <\/ul>\n <h3>2.2 YOLOX \u306e\u30af\u30ed\u30fc\u30f3<\/h3>\n <pre><code>git clone https:\/\/github.com\/Megvii-BaseDetection\/YOLOX.git\ncd YOLOX\npip install -v -e . # \u958b\u767a\u30e2\u30fc\u30c9\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>3. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6e96\u5099<\/h2>\n <h3>3.1 COCO \u30d5\u30a9\u30fc\u30de\u30c3\u30c8\u3078\u306e\u5909\u63db<\/h3>\n <p>\n \u72ec\u81ea\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u3001COCO \u5f62\u5f0f\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u306f JSON \u5f62\u5f0f\u3067\u4ee5\u4e0b\u306e\u69cb\u6210\u304c\u5fc5\u8981\u3067\u3059\u3002\n <\/p>\n <ul>\n <li><code>images\/<\/code> \u30d5\u30a9\u30eb\u30c0\uff1a\u753b\u50cf\u30d5\u30a1\u30a4\u30eb<\/li>\n <li><code>annotations\/instances_train2017.json<\/code>\uff1a\u5b66\u7fd2\u7528\u30e9\u30d9\u30eb<\/li>\n <li><code>annotations\/instances_val2017.json<\/code>\uff1a\u691c\u8a3c\u7528\u30e9\u30d9\u30eb<\/li>\n <\/ul>\n <h3>3.2 \u30c7\u30fc\u30bf\u306e\u5206\u5272<\/h3>\n <p>\u5b66\u7fd2\u7528\u3068\u691c\u8a3c\u7528\u3092 8:2 \u306a\u3069\u3067\u5206\u5272\u3057\u307e\u3059\u3002\u30b9\u30af\u30ea\u30d7\u30c8\u4f8b\uff1a<\/p>\n <pre><code>python tools\/train_val_split.py --input_dir .\/dataset --out_dir .\/coco_dataset --val_ratio 0.2<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>4. \u8a2d\u5b9a\u30d5\u30a1\u30a4\u30eb\uff08.yaml\uff09<\/h2>\n <p>YOLOX \u3067\u306f <code>exps\/default\/yolox_s.py<\/code> \u306e\u3088\u3046\u306a Python \u30b9\u30af\u30ea\u30d7\u30c8\u3067\u8a2d\u5b9a\u3092\u7ba1\u7406\u3057\u307e\u3059\u3002\u4e3b\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\uff1a<\/p>\n <ul>\n <li><code>num_classes<\/code>\uff1a\u30af\u30e9\u30b9\u6570<\/li>\n <li><code>input_size<\/code>\uff1a\u5165\u529b\u89e3\u50cf\u5ea6\uff08\u4f8b\uff1a640\uff09<\/li>\n <li><code>random_size<\/code>\uff1a\u30e9\u30f3\u30c0\u30e0\u30ea\u30b5\u30a4\u30ba\u30d0\u30c3\u30c1\u306e\u7bc4\u56f2<\/li>\n <li><code>batch_size<\/code>\uff1a\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba<\/li>\n <li><code>max_epoch<\/code>\uff1a\u6700\u5927\u30a8\u30dd\u30c3\u30af\u6570<\/li>\n <li><code>lr<\/code>\uff1a\u521d\u671f\u5b66\u7fd2\u7387<\/li>\n <\/ul>\n <p>\u4f8b\uff1a<\/p>\n <pre><code>exp = {\n \"name\": \"yolox_s_custom\",\n \"num_classes\": 3,\n \"input_size\": (640, 640),\n \"random_size\": (10, 20),\n \"batch_size\": 16,\n \"max_epoch\": 50,\n \"lr\": 0.01,\n}<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>5. \u5b66\u7fd2\u5b9f\u884c<\/h2>\n <p>\u57fa\u672c\u7684\u306a\u30b3\u30de\u30f3\u30c9\uff1a<\/p>\n <pre><code>python tools\/train.py -f exps\/default\/yolox_s.py -d 1 -b 16 --fp16 -o<\/code><\/pre>\n <ul>\n <li><code>-d<\/code>\uff1aGPU \u6570<\/li>\n <li><code>-b<\/code>\uff1a\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba<\/li>\n <li><code>--fp16<\/code>\uff1a\u534a\u7cbe\u5ea6\uff08AMP\uff09\u6709\u52b9<\/li>\n <li><code>-o<\/code>\uff1a\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u8a2d\u5b9a\u8aad\u307f\u8fbc\u307f<\/li>\n <\/ul>\n <p>\n \u5b66\u7fd2\u4e2d\u306f TensorBoard \u3067\u30ed\u30b0\u3092\u53ef\u8996\u5316\u3067\u304d\u307e\u3059\u3002\n <\/p>\n <pre><code>tensorboard --logdir runs\/<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>6. \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574<\/h2>\n <h3>6.1 \u5b66\u7fd2\u7387\u30b9\u30b1\u30b8\u30e5\u30fc\u30e9<\/h3>\n <p>\n Cosine Annealing \u3084 StepLR \u3092\u8a66\u3057\u3001\u53ce\u675f\u30b9\u30d4\u30fc\u30c9\u3068\u6700\u7d42 mAP \u3092\u6539\u5584\u3057\u307e\u3059\u3002\n <\/p>\n <h3>6.2 \u30d0\u30c3\u30c1\u30b5\u30a4\u30ba<\/h3>\n <p>\n \u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c\u5927\u304d\u3044\u307b\u3069\u5b89\u5b9a\u3057\u307e\u3059\u304c\u3001GPU \u30e1\u30e2\u30ea\u306b\u6ce8\u610f\u3002\u5c0f\u3055\u3044\u5834\u5408\u306f\u5b66\u7fd2\u7387\u3092\u4e0b\u3052\u308b\u306a\u3069\u306e\u5de5\u592b\u3092\u3002\n <\/p>\n <h3>6.3 \u30c7\u30fc\u30bf\u62e1\u5f35<\/h3>\n <ul>\n <li>Mosaic\uff1a4 \u753b\u50cf\u3092\u7d50\u5408\u3057\u3066\u5b66\u7fd2\u3055\u305b\u308b<\/li>\n <li>MixUp\uff1a2 \u753b\u50cf\u3092\u91cd\u306d\u3066\u30e9\u30d9\u30eb\u3092\u88dc\u9593<\/li>\n <li>RandomFlip \/ ColorJitter\uff1a\u57fa\u672c\u62e1\u5f35<\/li>\n <\/ul>\n<\/section>\n\n<section>\n <h2>7. \u691c\u8a3c\u3068\u8a55\u4fa1<\/h2>\n <p>\u5b66\u7fd2\u5f8c\u3001\u691c\u8a3c\u30c7\u30fc\u30bf\u3067 mAP (0.5:0.95) \u3092\u7b97\u51fa\uff1a<\/p>\n <pre><code>python tools\/eval.py -n yolox-s -c .\/weights\/yolox_s.pth -b 16 -d 1 --conf 0.001<\/code><\/pre>\n <p>\u7d50\u679c\u306f JSON \u5f62\u5f0f\u3067\u3082\u51fa\u529b\u53ef\u80fd\u3002\u4f4e\u3044\u5834\u5408\u306f\u524d\u7bc0\u306e\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u898b\u76f4\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/section>\n\n<section>\n <h2>8. \u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0<\/h2>\n <h3>8.1 \u5b66\u7fd2\u304c\u9032\u307e\u306a\u3044<\/h3>\n <ul>\n <li>\u5b66\u7fd2\u7387\u304c\u9ad8\u3059\u304e\u308b \u2192 \u5c0f\u3055\u304f\u3059\u308b<\/li>\n <li>\u30c7\u30fc\u30bf\u30d0\u30e9\u30f3\u30b9\u304c\u504f\u3063\u3066\u3044\u308b \u2192 \u30ea\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0<\/li>\n <li>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u304c\u5c0f\u3055\u3044 \u2192 \u5c0f\u3055\u304f\u3066\u3082\u30ec\u30a4\u30e4\u30ce\u30eb\u30e0\u3092\u8ffd\u52a0<\/li>\n <\/ul>\n <h3>8.2 \u30aa\u30fc\u30d0\u30fc\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0<\/h3>\n <ul>\n <li>\u30c7\u30fc\u30bf\u62e1\u5f35\u306e\u5f37\u5316<\/li>\n <li>\u65e9\u671f\u505c\u6b62\uff08EarlyStopping\uff09\u306e\u5c0e\u5165<\/li>\n <li>\u91cd\u307f\u6e1b\u8870\uff08weight decay\uff09\u306e\u8abf\u6574<\/li>\n <\/ul>\n<\/section>\n\n<section>\n <h2>9. \u63a8\u8ad6\u3068\u30c7\u30d7\u30ed\u30a4<\/h2>\n <h3>9.1 ONNX \u30a8\u30af\u30b9\u30dd\u30fc\u30c8<\/h3>\n <pre><code>python tools\/export_onnx.py -n yolox-s -c .\/weights\/yolox_s.pth --opset 12<\/code><\/pre>\n <h3>9.2 TensorRT \u6700\u9069\u5316<\/h3>\n <p>\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3057\u305f ONNX \u30e2\u30c7\u30eb\u3092 TensorRT \u3067\u6700\u9069\u5316\u3057\u3066\u9ad8\u901f\u63a8\u8ad6\u3002<\/p>\n <pre><code>trtexec --onnx=yolox_s.onnx --fp16 --saveEngine=yolox_s.trt<\/code><\/pre>\n<\/section>\n\n<section>\n <h2>10. \u307e\u3068\u3081\u3068\u6b21\u306e\u30b9\u30c6\u30c3\u30d7<\/h2>\n <p>\n \u672c\u30ac\u30a4\u30c9\u3067\u306f\u3001YOLOX \u306e\u74b0\u5883\u69cb\u7bc9\u304b\u3089\u30c7\u30fc\u30bf\u6e96\u5099\u3001\u5b66\u7fd2\u3001\u8a55\u4fa1\u3001\u63a8\u8ad6\u307e\u3067\u3092\u7db2\u7f85\u3057\u307e\u3057\u305f\u3002\n \u307e\u305a\u306f\u516c\u5f0f\u306e <a href=\"https:\/\/github.com\/Megvii-BaseDetection\/YOLOX\">YOLOX \u30ea\u30dd\u30b8\u30c8\u30ea<\/a> \u3092\u53c2\u7167\u3057\u3001\u5c0f\u898f\u6a21\u30c7\u30fc\u30bf\u3067\u52d5\u4f5c\u78ba\u8a8d\u5f8c\u306b\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3092\u52a0\u3048\u3066\u3044\u304f\u3053\u3068\u3092\u304a\u3059\u3059\u3081\u3057\u307e\u3059\u3002\n <\/p>\n \n<\/section>\n\n<section>\n <h2>11. \u30e9\u30a4\u30bb\u30f3\u30b9\u3068\u5546\u7528\u5229\u7528\u306b\u95a2\u3059\u308b\u6ce8\u610f<\/h2>\n <p>\n YOLOX \u306f Apache License 2.0 \u306e\u3082\u3068\u3067\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5546\u7528\u30fb\u975e\u5546\u7528\u5229\u7528\u3068\u3082\u306b\u307b\u307c\u5236\u9650\u306a\u304f\u5229\u7528\u53ef\u80fd\u3067\u3059\u304c\u3001\u4ee5\u4e0b\u306e\u70b9\u306b\u3054\u6ce8\u610f\u304f\u3060\u3055\u3044\u3002\n <\/p>\n <ul>\n <li>\u8457\u4f5c\u6a29\u8868\u793a\u304a\u3088\u3073\u30e9\u30a4\u30bb\u30f3\u30b9\u6587\u3092\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306b\u542b\u3081\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n <li>\u6d3e\u751f\u7269\uff08\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u6e08\u30e2\u30c7\u30eb\u3084\u6539\u5909\u30b3\u30fc\u30c9\uff09\u3092\u518d\u914d\u5e03\u3059\u308b\u5834\u5408\u3082\u3001\u540c\u3058\u30e9\u30a4\u30bb\u30f3\u30b9\u3092\u9069\u7528\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u304c\u3001\u5143\u306e\u30e9\u30a4\u30bb\u30f3\u30b9\u6587\u3092\u660e\u793a\u3059\u308b\u3053\u3068\u304c\u63a8\u5968\u3055\u308c\u307e\u3059\u3002<\/li>\n <li>\u5b66\u7fd2\u6642\u306b\u5229\u7528\u3059\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3084\u524d\u51e6\u7406\u30c4\u30fc\u30eb\u306b\u5225\u30e9\u30a4\u30bb\u30f3\u30b9\u306e\u3082\u306e\u304c\u542b\u307e\u308c\u308b\u5834\u5408\u3001\u305d\u306e\u30e9\u30a4\u30bb\u30f3\u30b9\u6761\u4ef6\u306b\u3082\u5f93\u3046\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n <li>ONNX \u3084 TensorRT \u306a\u3069\u4ed6\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306b\u5909\u63db\u30fb\u7d44\u307f\u8fbc\u3093\u3067\u5229\u7528\u3059\u308b\u969b\u3001\u5909\u63db\u5f8c\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3084\u30d7\u30e9\u30b0\u30a4\u30f3\u306e\u30e9\u30a4\u30bb\u30f3\u30b9\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n <li>\u7279\u8a31\u6a29\u3084\u5546\u6a19\u6a29\u306a\u3069\u3001\u30e9\u30a4\u30bb\u30f3\u30b9\u5916\u306e\u77e5\u7684\u8ca1\u7523\u6a29\u306f\u9069\u7528\u5916\u3067\u3059\u306e\u3067\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002<\/li>\n <\/ul>\n<p>\u5f0a\u793e\u3000\u00a9\u3000\u682a\u5f0f\u4f1a\u793e\u30d3\u30fc\u30fb\u30ca\u30ec\u30c3\u30b8\u30fb\u30c7\u30b6\u30a4\u30f3\u3000\u3067\u306fYOLOX\u3092\u30e1\u30a4\u30f3\u306b\u7269\u4f53\u8a8d\u8b58\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002\u7406\u7531\u306f\u30e9\u30a4\u30bb\u30f3\u30b9\u30c8\u30e9\u30d6\u30eb\u304c\u5c11\u306a\u3044\u3001\u62e1\u5f35\u53ef\u80fd\u3068\u3044\u3063\u305f\u3082\u306e\u3060\u3051\u3067\u306a\u304f\u3001\u4e0a\u3067\u7d39\u4ecb\u3057\u305f\u5185\u5bb9\u3092Web\u30d6\u30e9\u30a6\u30b6\u4e0a\u3067\u6700\u5c0f\u306e\u624b\u7d9a\u304d\u306b\u3066\u3001\u5b66\u7fd2\u3084\u305d\u306e\u5b66\u7fd2\u5185\u5bb9\u3092\u30b0\u30e9\u30d5\u7b49\u306b\u53ef\u8996\u5316\u3059\u308b\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u30c4\u30fc\u30eb\u3092\u6e96\u5099\u3057\u3066\u3044\u307e\u3059\u3002\u7c21\u6613\u306b\u4f7f\u3044\u305f\u3044\u3001\u30b7\u30b9\u30c6\u30e0\u306e\u69cb\u6210\u7b49\u3067\u304a\u60a9\u307f\u306e\u65b9\u306f\u304a\u554f\u3044\u5408\u308f\u305b\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/section>\n\n<\/body>\n<\/html>\n\n","protected":false},"excerpt":{"rendered":"<p>YOLOX \u5b66\u7fd2\u65b9\u6cd5\u30ac\u30a4\u30c9 1. \u306f\u3058\u3081\u306b \u305f\u3073\u305f\u3073Blog\u306b\u7d39\u4ecb\u3057\u3066\u3044\u308bYOLOX \u306f\u9ad8\u901f\u30fb\u9ad8\u7cbe\u5ea6\u306a\u7269\u4f53\u691c\u51fa\u30e2\u30c7\u30eb\u3068\u3057\u3066\u6ce8\u76ee\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u672c\u7a3f\u3067\u306f\u3001YOLOX \u306e\u5b66\u7fd2\u6e96\u5099\u304b\u3089\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u3001\u30c8\u30e9\u30d6\u30eb\u30b7\u30e5\u30fc\u30c6\u30a3\u30f3 […]<\/p>\n","protected":false},"author":1,"featured_media":1534,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","vkExUnit_EyeCatch_disable":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"_lightning_design_setting":{"layout":"default"},"footnotes":"","jetpack_post_was_ever_published":false},"categories":[6,64],"tags":[26,27,51,36],"class_list":["post-1529","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-programing","category-64","tag-ai","tag-27","tag-51","tag-36"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"takashide\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/beeknowledge.co.jp\/?p=1529\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 4.9.10\" \/>\n\t\t<meta property=\"og:locale\" content=\"ja_JP\" \/>\n\t\t<meta property=\"og:site_name\" content=\"Bee Knowledge\" \/>\n\t\t<meta property=\"og:type\" content=\"article\" \/>\n\t\t<meta property=\"og:title\" content=\"YOLOX \u5b66\u7fd2\u65b9\u6cd5\u30ac\u30a4\u30c9 | \u77e5\u3092\u89e3\u304d\u653e\u3066 \u672a\u6765\u3092\u5275\u308b\u30c7\u30b8\u30bf\u30eb\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u4f01\u696d\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/beeknowledge.co.jp\/?p=1529\" \/>\n\t\t<meta property=\"fb:admins\" content=\"takashide@beeknowledge.co.jp\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/07\/phoyinphoyi.jpg\" \/>\n\t\t<meta 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