bbox文件，包含mmdet-anime-face-yolov3.pth，mmdet-anime-face-yolov3，

# _base_ = ["../_base_/schedules/schedule_1x.py", "../_base_/default_runtime.py"] # model settings data_preprocessor = dict( type="DetDataPreprocessor", mean=[0, 0, 0], std=[255.0, 255.0, 255.0], bgr_to_rgb=True, pad_size_divisor=32, ) model = dict( type="YOLOV3", data_preprocessor=data_preprocessor, backbone=dict( type="Darknet", depth=53, out_indices=(3, 4, 5), init_cfg=dict(type="Pretrained", checkpoint="open-mmlab://darknet53"), ), neck=dict( type="YOLOV3Neck", num_scales=3, in_channels=[1024, 512, 256], out_channels=[512, 256, 128], ), bbox_head=dict( type="YOLOV3Head", num_classes=1, in_channels=[512, 256, 128], out_channels=[1024, 512, 256], anchor_generator=dict( type="YOLOAnchorGenerator", base_sizes=[ [(116, 90), (156, 198), (373, 326)], [(30, 61), (62, 45), (59, 119)], [(10, 13), (16, 30), (33, 23)], ], strides=[32, 16, 8], ), bbox_coder=dict(type="YOLOBBoxCoder"), featmap_strides=[32, 16, 8], loss_cls=dict( type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0, reduction="sum" ), loss_conf=dict( type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0, reduction="sum" ), loss_xy=dict( type="CrossEntropyLoss", use_sigmoid=True, loss_weight=2.0, reduction="sum" ), loss_wh=dict(type="MSELoss", loss_weight=2.0, reduction="sum"), ), # training and testing settings train_cfg=dict( assigner=dict( type="GridAssigner", pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0 ) ), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, conf_thr=0.005, nms=dict(type="nms", iou_threshold=0.45), max_per_img=100, ), ) # dataset settings dataset_type = "CocoDataset" data_root = "data/coco/" # Example to use different file client # Method 1: simply set the data root and let the file I/O module # automatically infer from prefix (not support LMDB and Memcache yet) # data_root = 's3://openmmlab/datasets/detection/coco/' # Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6 # backend_args = dict( # backend='petrel', # path_mapping=dict({ # './data/': 's3://openmmlab/datasets/detection/', # 'data/': 's3://openmmlab/datasets/detection/' # })) backend_args = None train_pipeline = [ dict(type="LoadImageFromFile", backend_args=backend_args), dict(type="LoadAnnotations", with_bbox=True), dict( type="Expand", mean=data_preprocessor["mean"], to_rgb=data_preprocessor["bgr_to_rgb"], ratio_range=(1, 2), ), dict( type="MinIoURandomCrop", min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), min_crop_size=0.3, ), dict(type="RandomResize", scale=[(320, 320), (608, 608)], keep_ratio=True), dict(type="RandomFlip", prob=0.5), dict(type="PhotoMetricDistortion"), dict(type="PackDetInputs"), ] test_pipeline = [ dict(type="LoadImageFromFile", backend_args=backend_args), dict(type="Resize", scale=(608, 608), keep_ratio=True), dict(type="LoadAnnotations", with_bbox=True), dict( type="PackDetInputs", meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"), ), ] train_dataloader = dict( batch_size=8, num_workers=4, persistent_workers=True, sampler=dict(type="DefaultSampler", shuffle=True), batch_sampler=dict(type="AspectRatioBatchSampler"), dataset=dict( type=dataset_type, data_root=data_root, ann_file="annotations/instances_train2017.json", data_prefix=dict(img="train2017/"), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=train_pipeline, backend_args=backend_args, ), ) val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type="DefaultSampler", shuffle=False), dataset=dict( type=dataset_type, data_root=data_root, ann_file="annotations/instances_val2017.json", data_prefix=dict(img="val2017/"), test_mode=True, pipeline=test_pipeline, backend_args=backend_args, ), ) test_dataloader = val_dataloader val_evaluator = dict( type="CocoMetric", ann_file=data_root + "annotations/instances_val2017.json", metric="bbox", backend_args=backend_args, ) test_evaluator = val_evaluator train_cfg = dict(max_epochs=273, val_interval=7) # optimizer optim_wrapper = dict( type="OptimWrapper", optimizer=dict(type="SGD", lr=0.001, momentum=0.9, weight_decay=0.0005), clip_grad=dict(max_norm=35, norm_type=2), ) # learning policy param_scheduler = [ dict(type="LinearLR", start_factor=0.1, by_epoch=False, begin=0, end=2000), dict(type="MultiStepLR", by_epoch=True, milestones=[218, 246], gamma=0.1), ] default_hooks = dict(checkpoint=dict(type="CheckpointHook", interval=7)) # NOTE: `auto_scale_lr` is for automatically scaling LR, # USER SHOULD NOT CHANGE ITS VALUES. # base_batch_size = (8 GPUs) x (8 samples per GPU) auto_scale_lr = dict(base_batch_size=64)