Provide baseline to solve different kinds of vision classification tasks

in some days, repo will add kinds of Attentions-Modules, so stay tuned

params setting refer to configs/complete.yaml, include model, data, hyp

model: support BN freeze, backbone freeze. set out_channels as you like, maybe not equal to num_classes if you want to do training strategy
data: support custom-style data augment, all the data augment are placed in utils/augment.py, eg: cutout, colerjitter...
hyp: support many loss function eg: ce and bce, support custom-style training stategy, eg: focalloss, mixup and progressive learning

1、the repo support "split epochs into augment-epoch and no-augment-epoch, set in XXX.yaml" -> aug_epoch: 25, means augment for 25 epochs then train in no-augment
2、the repo support "OHEM, set in XXX.yaml" -> bceloss: True, focal: True 30, True means turn on focalloss, 30 means bceloss before 30th epoch and focalloss after. if no focalloss, just set focal: 0 30
3、the repo support "mixup in given epochs, set in XXX.yaml" -> mixup: 0.2 [20,30], 0.2 means prob to use mixup every epoch, [20,30] means interval to start/delete mixup. if no mixup, just set mixup: 0 [20,30]
4、the repo support "progressive learning $refer to EfficientV2, set in XXX.yaml" -> prog_learn: True, will effect on image size & mixup, divide into 3 parts in default

mobilenet_v2, mobilenet_v3_small, mobilenet_v3_large
shufflenet_v2_x0_5, shufflenet_v2_x1_0, shufflenet_v2_x1_5, shufflenet_v2_x2_0
resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d, resnext101_32x8d, resnext101_64x4d
convnext_tiny, convnext_small, convnext_base, convnext_large
efficientnet_b0 -> efficientnet_b7, efficientnet_v2_s, efficientnet_v2_m, efficientnet_v2_l

--data
  --train
    -- clsXXX
      -- XXX.jpg/png
  --val
    -- clsXXX
      -- XXX.jpg/png

python main.py

CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 main.py