Hua
f5be7671bc
Split the single "3d" captcha type into three independent expert models: - 3d_text: 3D perspective text OCR (renamed from old "3d", CTC-based ThreeDCNN) - 3d_rotate: rotation angle regression (new RegressionCNN, circular loss) - 3d_slider: slider offset regression (new RegressionCNN, SmoothL1 loss) CAPTCHA_TYPES expanded from 3 to 5 classes. Classifier samples updated to 50000 (10000 per class). New generators, model, dataset, training utilities, and full pipeline/export/CLI support for all subtypes. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
39 lines
986 B
Python
39 lines
986 B
Python
"""
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训练 3D 滑块验证码回归模型 (RegressionCNN)
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用法: python -m training.train_3d_slider
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"""
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from config import (
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IMAGE_SIZE,
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SYNTHETIC_3D_SLIDER_DIR,
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REAL_3D_SLIDER_DIR,
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)
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from generators.threed_slider_gen import ThreeDSliderGenerator
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from models.regression_cnn import RegressionCNN
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from training.train_regression_utils import train_regression_model
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def main():
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img_h, img_w = IMAGE_SIZE["3d_slider"]
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model = RegressionCNN(img_h=img_h, img_w=img_w)
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print("=" * 60)
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print("训练 3D 滑块验证码回归模型 (RegressionCNN)")
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print(f" 输入尺寸: {img_h}×{img_w}")
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print(f" 任务: 预测滑块偏移 x 坐标")
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print("=" * 60)
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train_regression_model(
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model_name="threed_slider",
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model=model,
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synthetic_dir=SYNTHETIC_3D_SLIDER_DIR,
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real_dir=REAL_3D_SLIDER_DIR,
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generator_cls=ThreeDSliderGenerator,
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config_key="3d_slider",
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)
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if __name__ == "__main__":
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main()
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