mmyolo/configs/yolov5/voc/yolov5_x-v61_fast_1xb32-50e...

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[Feature] Support VOC Dataset in YOLOv5 (#134) * add yolov5 voc training * fix mosaic bug * fix mosaic bug and temp config * fix mosaic bug * update config * support training on voc dataset * format code * format code * Optimize Code. Change `RandomTransform` to `OneOf` * Change `OneOf` to `mmcv.RandomChoice` * fix yolov5coco dataset * fix yolov5coco dataset * fix bug, format code * format config * format code * add yolov5 voc training * rebase * fix mosaic bug * update config * support training on voc dataset * format code * format code * Optimize Code. Change `RandomTransform` to `OneOf` * Change `OneOf` to `mmcv.RandomChoice` * fix yolov5coco dataset * fix yolov5coco dataset * fix bug, format code * format code * add yolov5 voc training * fix mosaic bug and temp config * fix mosaic bug * update config * support training on voc dataset * format code * format code * Optimize Code. Change `RandomTransform` to `OneOf` * Change `OneOf` to `mmcv.RandomChoice` * fix yolov5coco dataset * fix yolov5coco dataset * fix bug, format code * format code * add yolov5 voc training * rebase * fix mosaic bug * update config * support training on voc dataset * format code * format code * Optimize Code. Change `RandomTransform` to `OneOf` * Change `OneOf` to `mmcv.RandomChoice` * fix yolov5coco dataset * fix yolov5coco dataset * fix bug, format code * format code * format code * fix lint * add unittest * add auto loss_weight * add doc; add model log url * add doc; add model log url * add doc; add model log url
2022-10-18 17:06:49 +08:00
_base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
deepen_factor = 1.33
widen_factor = 1.25
train_batch_size_per_gpu = 32
train_num_workers = 8
# TODO: need to add pretrained_model
load_from = None
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
train_dataloader = dict(
batch_size=train_batch_size_per_gpu, num_workers=train_num_workers)
optim_wrapper = dict(
optimizer=dict(batch_size_per_gpu=train_batch_size_per_gpu))