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- # Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
- enable_modelarts: False
- # Url for modelarts
- data_url: ""
- vgg_ckpt_url: ""
- train_url: ""
- checkpoint_url: ""
- # Path for local
- data_path: "/disk0/dataset/RCF_DATA/"
- output_path: "./output"
- load_path: "/disk0/cyf/RCFmodelarts/LOG4/output/ckpt_0/rcf_mindspore-200_4900.ckpt"
- vgg_ckpt_path: "/disk0/cyf/RCFmodelarts/vgg16.ckpt"
- cxx_path: "/disk0/cyf/RCFmodelarts/cxx"
- device_target: "Ascend"
- need_modelarts_dataset_unzip: False
- modelarts_dataset_unzip_name: ""
-
- distribute: False
- # ==============================================================================
- # Train options
- lr: 1e-6
- epoch_size: 200
- weight_decay: 0.0002
- momentum: 0.9
- ckpt_path: "outputs/"
- is_distributed: 1
- rank: 0
- para_workers: 24
-
-
- save_checkpoint_epochs: 5
- keep_checkpoint_max: 10
- # Eval options
- log_path: "outputs/"
- ckpt_path: './ckpt'
- save_checkpoint_path: "/cache/train/checkpoint"
- res_output_path : './rcf'
- save_checkpoint_path_dis: './ckpt'
- # Test option
- ckpt_p: "rcf_mindspore-400_612.ckpt"
- alg: "RCF"
- model_name_list: "rcf"
- result_dir: ""
- save_dir: ""
- gt_dir: ""
- key: "result"
- file_format_eval: ".mat"
- workers: -1
-
- # Export options
- device_id: 0
- device_num: 4
- batch_size: 10
- ckpt_file: ""
- file_format: "AIR"
-
- img_id_file_path: ""
- result_files: './result_Files'
-
- each_multiscale: True
- hue: 0.1
- saturation: 1.5
- value: 1.5
- jitter: 0.3
- resize_rate: 24
- multi_scale: [[384, 209],
- [544, 384],
- [503, 209],
- [273, 273],
- [385, 384],
- [272, 192],
- [252, 105],
- [385, 384],
- [137, 137],
- [192, 105],
- [193, 192],
- [816, 576],
- [755, 314],
- [410, 410],
- [576, 314],
- [578, 578],
- [578, 576],
- [500, 200],
- [500, 250],
- [500, 300],
- [500, 350],
- [500, 400],
- [500, 450],
- [500, 500],
- ]
-
-
- ---
-
- # Help description for each configuration
- # Train options
- data_dir: "Train dataset directory."
- per_batch_size: "Batch size for Training."
- pretrained_backbone: "The ckpt file of CspDarkNet53."
- resume_yolov4: "The ckpt file of YOLOv4, which used to fine tune."
- pretrained_checkpoint: "The ckpt file of YoloV4CspDarkNet53."
- filter_weight: "Filter the last weight parameters"
- lr_scheduler: "Learning rate scheduler, options: exponential, cosine_annealing."
- lr: "Learning rate."
- lr_epochs: "Epoch of changing of lr changing, split with ','."
- lr_gamma: "Decrease lr by a factor of exponential lr_scheduler."
- eta_min: "Eta_min in cosine_annealing scheduler."
- t_max: "T-max in cosine_annealing scheduler."
- max_epoch: "Max epoch num to train the model."
- warmup_epochs: "Warmup epochs."
- weight_decay: "Weight decay factor."
- momentum: "Momentum."
- loss_scale: "Static loss scale."
- label_smooth: "Whether to use label smooth in CE."
- label_smooth_factor: "Smooth strength of original one-hot."
- log_interval: "Logging interval steps."
- ckpt_path: "Checkpoint save location."
- ckpt_interval: "Save checkpoint interval."
- is_save_on_master: "Save ckpt on master or all rank, 1 for master, 0 for all ranks."
- is_distributed: "Distribute train or not, 1 for yes, 0 for no."
- rank: "Local rank of distributed."
- group_size: "World size of device."
- need_profiler: "Whether use profiler. 0 for no, 1 for yes."
- training_shape: "Fix training shape."
- resize_rate: "Resize rate for multi-scale training."
- run_eval: "Run evaluation when training."
- save_best_ckpt: "Save best checkpoint when run_eval is True."
- eval_start_epoch: "Evaluation start epoch when run_eval is True."
- eval_interval: "Evaluation interval when run_eval is True"
- ann_file: "path to annotation"
- each_multiscale: "Apply multi-scale for each scale"
- detect_head_loss_coff: "the loss coefficient of detect head.
- The order of coefficients is large head, medium head and small head"
- bbox_class_loss_coff: "bbox and class loss coefficient.
- The order of coefficients is ciou loss, confidence loss and class loss"
- labels: "the label of train data"
- mosaic: "use mosaic data augment"
- multi_label: "use multi label to nms"
- multi_label_thresh: "multi label thresh"
-
- # Eval options
- pretrained: "model_path, local pretrained model to load"
- log_path: "checkpoint save location"
- ann_val_file: "path to annotation"
-
- # Export options
- device_id: "Device id for export"
- batch_size: "batch size for export"
- testing_shape: "shape for test"
- ckpt_file: "Checkpoint file path for export"
- file_name: "output file name for export"
- file_format: "file format for export"
- keep_detect: "keep the detect module or not, default: True"
- img_id_file_path: 'path of image dataset'
- result_files: 'path to 310 infer result floder'
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