Datasets:
File size: 2,462 Bytes
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train: sam_40_mss_lines/train/images
val: sam_40_mss_lines/val/images
nc: 1
names: ['text_line']
scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024]
s: [0.50, 0.50, 1024]
m: [0.50, 1.00, 512]
l: [1.00, 1.00, 512]
x: [1.00, 1.50, 512]
# Backbone (P3-P6)
backbone:
# First layers, using convolutions and C3 blocks for feature extraction
- [-1, 1, Conv, [64, 3, 2]] # P1/2
- [-1, 1, Conv, [128, 3, 2]] # P2/4
- [-1, 2, C3k2, [128, False, 0.25]] # C3 block for P2/4
- [-1, 1, Conv, [256, 3, 2]] # P3/8
- [-1, 2, C3k2, [256, False, 0.25]] # C3 block for P3/8
- [-1, 1, Conv, [512, 3, 2]] # P4/16
- [-1, 2, C3k2, [512, True]] # C3 block for P4/16
- [-1, 1, Conv, [1024, 3, 2]] # P5/32
- [-1, 2, C3k2, [1024, True]] # C3 block for P5/32
- [-1, 1, Conv, [1024, 3, 2]] # P6/64
- [-1, 2, C3k2, [1024, True]] # C3 block for P6/64
- [-1, 1, SPPF, [1024, 5]] # SPPF block for improved feature extraction
- [-1, 1, C2PSA, [1024]] # Cross-stage partial aggregation (PSA)
# Segmentation head for predicting masks
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]] # Up-sample P6 (nearest neighbor)
- [[-1, 8], 1, Concat, [1]] # Concatenate backbone P5 features
- [-1, 2, C3k2, [1024, False]] # C3 block for P5
- [-1, 1, nn.Upsample, [None, 2, "nearest"]] # Up-sample P5
- [[-1, 6], 1, Concat, [1]] # Concatenate backbone P4 features
- [-1, 2, C3k2, [1024, False]] # C3 block for P4
- [-1, 1, nn.Upsample, [None, 2, "nearest"]] # Up-sample P4
- [[-1, 4], 1, Concat, [1]] # Concatenate backbone P3 features
- [-1, 2, C3k2, [512, False]] # C3 block for P3
# Additional convolutional layers for refinement
- [-1, 1, Conv, [512, 3, 2]] # Conv on P4
- [[-1, 18], 1, Concat, [1]] # Concatenate refined P4 features
- [-1, 2, C3k2, [1024, False]] # Further refinement on P4 features
- [-1, 1, Conv, [1024, 3, 2]] # Conv on P5
- [[-1, 15], 1, Concat, [1]] # Concatenate refined P5 features
- [-1, 2, C3k2, [1024, True]] # Further refinement on P5 features
- [-1, 1, Conv, [1024, 3, 2]] # Conv on P6
- [[-1, 12], 1, Concat, [1]] # Concatenate refined P6 features
- [-1, 2, C3k2, [1024, True]] # Further refinement on P6 features
# Final segmentation output (mask output layer)
- [[21, 24, 27, 30], 1, Segment, [nc, 32, 256]] # Segmentation head
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