TextNet T/S/B: Efficient Text Detection Models Overview TextNet is a lightweight and efficient architecture designed specifically for text detection, offering superior performance compared to traditional models like MobileNetV3. With variants TextNet T , TextNet S , and TextNet B (6.8M, 8.0M, and 8.9M parameters respectively), it achieves an excellent balance between accuracy and inference speed. Performance TextNet achieves state of the art results in text detection, outperforming hand crafted models in both accuracy and speed. Its architecture is highly efficient, making it ideal for GPU based applications. How to use Transformers Training We first compare TextNet with representative hand crafted backbones, such as ResNets and VGG16. For a fair comparison, all models are first pre trained on IC17 MLT [52] and then finetuned on Total Text. The proposed TextNet models achieve a better trade off between accuracy and inference speed than previous hand crafted models by a significant margin. In addition, notably, our TextNet T, S, and B only have 6.8M, 8.0M, and 8.9M parameters respectively, which are more parameter efficient than ResNets and VGG16. These results demonstrate that Text…
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