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Training\nbatch=64\nsubdivisions=16\nwidth=608\nheight=608\nchannels=3\nmomentum=0.9\ndecay=0.0005\nangle=0\nsaturation = 1.5\nexposure = 1.5\nhue=.1\n\nlearning_rate=0.001\nburn_in=1000\nmax_batches = 50150\npolicy=steps\nsteps=400000,450000\nscales=.1,.1\n\n[convolutional]\nbatch_normalize=1\nfilters=32\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=32\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=1024\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=1024\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=1024\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=1024\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=1024\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n######################\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=1024\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=1024\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=1024\nactivation=leaky\n\n[convolutional]\nsize=1\nstride=1\npad=1\nfilters=255\nactivation=linear\n\n\n[yolo]\nmask = 6,7,8\nanchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326\nclasses=80\nnum=9\njitter=.3\nignore_thresh = .7\ntruth_thresh = 1\nrandom=1\n\n\n[route]\nlayers = -4\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[upsample]\nstride=2\n\n[route]\nlayers = -1, 61\n\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=512\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=512\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=512\nactivation=leaky\n\n[convolutional]\nsize=1\nstride=1\npad=1\nfilters=255\nactivation=linear\n\n\n[yolo]\nmask = 3,4,5\nanchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326\nclasses=80\nnum=9\njitter=.3\nignore_thresh = .7\ntruth_thresh = 1\nrandom=1\n\n\n\n[route]\nlayers = -4\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[upsample]\nstride=2\n\n[route]\nlayers = -1, 36\n\n\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=256\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=256\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nsize=3\nstride=1\npad=1\nfilters=256\nactivation=leaky\n\n[convolutional]\nsize=1\nstride=1\npad=1\nfilters=255\nactivation=linear\n\n\n[yolo]\nmask = 0,1,2\nanchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326\nclasses=80\nnum=9\njitter=.3\nignore_thresh = .7\ntruth_thresh = 1\nrandom=1\n\n", "placeholder": "", "describe": "模型参数配置、训练配置", "editable": 1, "condition": "", "sub_args": {} }, "--data_cfg": { "type": "text", "item_type": "str", "label": "训练数据配置", "require": 1, "choice": [], "range": "", "default": "classes= 80\ntrain = /root/darknet/coco_data/coco/trainvalno5k.txt\n#valid = coco_testdev\nvalid = /root/darknet/coco_data/coco/5k.txt\nnames = /root/darknet/data/coco.names\nbackup = /root/darknet/backup\neval=coco\n\n", "placeholder": "", "describe": "训练数据配置", "editable": 1, "condition": "", "sub_args": {} }, "--weights": { "type": "str", "item_type": "str", "label": "预训练模型权重文件", "require": 1, "choice": [], "range": "", "default": "", "placeholder": "", "describe": "预训练模型权重文件", "editable": 1, "condition": "", "sub_args": {} } } } }, "ner":{ "project_name":"多媒体类模板", 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"sub_args": {} }, "--filename": { "type": "str", "item_type": "str", "label": "数据集的名字", "require": 1, "choice": [ "resume_BIO.txt", "people_daily_BIO.txt" ], "range": "", "default": "resume_BIO.txt", "placeholder": "", "describe": " 数据集的名字", "editable": 1, "condition": "", "sub_args": {} }, "--epochs": { "type": "str", "item_type": "str", "label": "训练的次数，次数越大效果越好，建议 5 以上", "require": 1, "choice": [], "range": "", "default": "5", "placeholder": "", "describe": "训练的次数，次数越大效果越好，建议 5 以上", "editable": 1, "condition": "", "sub_args": {} }, "-pp": { "type": "str", "item_type": "str", "label": "模型保存地址", "require": 1, "choice": [], "range": "", "default": "/mnt/admin/model.pkl", "placeholder": "", "describe": "模型保存地址", "editable": 1, "condition": "", "sub_args": {} } } } }, "model-register":{ "project_name":"模型服务化", "image_name":"ccr.ccs.tencentyun.com/cube-studio/model:20221001", "gitpath": "https://github.com/tencentmusic/cube-studio/tree/master/job-template/job/model_register", 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"placeholder": "", "describe": "模型版本号", "editable": 1, "condition": "", "sub_args": {} }, "--model_path": { "type": "str", "item_type": "str", "label": "模型地址", "require": 1, "choice": [], "range": "", "default": "", "placeholder": "", "describe": "模型地址", "editable": 1, "condition": "", "sub_args": {} }, "--describe": { "type": "str", "item_type": "str", "label": "模型描述", "require": 1, "choice": [], "range": "", "default": "", "placeholder": "", "describe": "模型描述", "editable": 1, "condition": "", "sub_args": {} }, "--framework": { "type": "str", "item_type": "str", "label": "模型框架", "require": 1, "choice": [ "xgb", "tf", "pytorch", "onnx", "tensorrt" ], "range": "", "default": "tf", "placeholder": "", "describe": "模型框架", "editable": 1, "condition": "", "sub_args": {} }, "--inference_framework": { "type": "str", "item_type": "str", "label": "推理框架", "require": 1, "choice": [ "tfserving", "torch-server", "onnxruntime", "triton-server" ], "range": "", "default": "tfserving", "placeholder": 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