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- # Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
- enable_modelarts: True
- # Url for modelarts
- data_url: ""
- train_url: ""
- checkpoint_url: ""
- # Path for local
- outputs_dir: "/home/work/user-job-dir/outputs/model/"
- data_path: "/home/work/user-job-dir/inputs/data/"
- output_path: "/home/work/user-job-dir/outputs/model/"
- load_path: "/cache/checkpoint_path"
- device_target: "Ascend"
- need_modelarts_dataset_unzip: False
- modelarts_dataset_unzip_name: "coco2017"
-
- # ==============================================================================
- # Train options
- data_dir: "/home/work/user-job-dir/inputs/data/"
- per_batch_size: 32
- yolov5_version: "yolov5s"
- pretrained_backbone: ""
- resume_yolov5: ""
- pretrained_checkpoint: ""
-
- lr_scheduler: "cosine_annealing"
- lr: 0.013
- lr_epochs: "220,250"
- lr_gamma: 0.1
- eta_min: 0.0
- T_max: 300
- max_epoch: 320
- warmup_epochs: 20
- weight_decay: 0.0005
- momentum: 0.9
- loss_scale: 1024
- label_smooth: 0
- label_smooth_factor: 0.1
- log_interval: 100
- ckpt_path: "outputs/"
- ckpt_interval: -1
- is_save_on_master: 1
- is_distributed: 0
- bind_cpu: True
- device_num: 8
- rank: 0
- group_size: 1
- need_profiler: 0
- training_shape: ""
- resize_rate: 10
- is_modelArts: 0
-
- # Eval options
- pretrained: ""
- log_path: "outputs/"
- ann_val_file: ""
- eval_nms_thresh: 0.6
- eval_shape: ""
- ignore_threshold: 0.7
- test_ignore_threshold: 0.001
- multi_label: True
- multi_label_thresh: 0.1
-
- # Export options
- device_id: 0
- batch_size: 1
- testing_shape: 640
- ckpt_file: ""
- file_name: "yolov5"
- file_format: "MINDIR"
- dataset_path: ""
- ann_file: ""
-
-
- # Other default config
- hue: 0.015
- saturation: 1.5
- value: 0.4
- jitter: 0.3
-
- multi_scale: [[320, 320],
- [352, 352],
- [384, 384],
- [416, 416],
- [448, 448],
- [480, 480],
- [512, 512],
- [544, 544],
- [576, 576],
- [608, 608],
- [640, 640],
- [672, 672],
- [704, 704],
- [736, 736],
- [768, 768]]
- num_classes: 80
- max_box: 150
-
- # h->w
- anchor_scales: [[12, 16],
- [19, 36],
- [40, 28],
- [36, 75],
- [76, 55],
- [72, 146],
- [142, 110],
- [192, 243],
- [459, 401]]
-
- out_channel: 255 # 3 * (num_classes + 5)
-
- input_shape: [[3, 32, 64, 128, 256, 512, 1],
- [3, 48, 96, 192, 384, 768, 2],
- [3, 64, 128, 256, 512, 1024, 3],
- [3, 80, 160, 320, 640, 1280, 4]]
-
- # test_param
- test_img_shape: [640, 640]
-
- labels: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
- 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
- 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
- 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
- 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
- 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
- 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
- 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
- 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ]
-
- coco_ids: [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27,
- 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53,
- 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,
- 81, 82, 84, 85, 86, 87, 88, 89, 90 ]
-
- result_files: './result_Files'
-
- ---
-
- # 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_yolov5: "The ckpt file of YOLOv5, which used to fine tune."
- pretrained_checkpoint: "The ckpt file of YOLOv5CspDarkNet53."
- 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."
- bind_cpu: "Whether bind cpu when distributed training."
- device_num: "Device numbers per server"
- 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."
- ann_file: "path to annotation"
- each_multiscale: "Apply multi-scale for each scale"
- labels: "the label of train data"
- 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"
- result_files: 'path to 310 infer result floder'
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