updates
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			@ -305,6 +305,11 @@ if __name__ == '__main__':
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                x = (np.random.randn(1) * 0.3 + 1) ** 1.1  # plt.hist(x.ravel(), 100)
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                hyp[k] = hyp[k] * float(x)  # vary by about 30% 1sigma
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            # Apply limits
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            hyp['iou_t'] = np.clip(hyp['iou_t'], 0, 0.90)
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            hyp['momentum'] = np.clip(hyp['momentum'], 0, 0.98)
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            hyp['weight_decay'] = np.clip(hyp['weight_decay'], 0, 0.01)
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            # Normalize loss components (sum to 1)
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            lcf = ['xy', 'wh', 'cls', 'conf']
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            s = sum([v for k, v in hyp.items() if k in lcf])
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