# Ultralytics YOLO 🚀, AGPL-3.0 license # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n' # [depth, width, max_channels] x: [1.00, 1.00, 2048] backbone: # [from, repeats, module, args] - [-1, 1, HGStem, [32, 64]] # 0-P2/4 - [-1, 6, HGBlock, [64, 128, 3]] # stage 1 - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8 - [-1, 6, HGBlock, [128, 512, 3]] - [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2 - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16 - [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut - [-1, 6, HGBlock, [256, 1024, 5, True, True]] - [-1, 6, HGBlock, [256, 1024, 5, True, True]] - [-1, 6, HGBlock, [256, 1024, 5, True, True]] - [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3 - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32 - [-1, 6, HGBlock, [512, 2048, 5, True, False]] - [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4 head: - [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2 - [-1, 1, AIFI, [2048, 8]] - [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0 - [-1, 1, nn.Upsample, [None, 2, 'nearest']] - [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1 - [[-2, -1], 1, Concat, [1]] - [-1, 3, RepC3, [384]] # 20, fpn_blocks.0 - [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1 - [-1, 1, nn.Upsample, [None, 2, 'nearest']] - [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0 - [[-2, -1], 1, Concat, [1]] # cat backbone P4 - [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1 - [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0 - [[-1, 21], 1, Concat, [1]] # cat Y4 - [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0 - [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1 - [[-1, 16], 1, Concat, [1]] # cat Y5 - [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1 - [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)