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--- |
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library_name: transformers |
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--- |
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## TextNet-T/S/B: Efficient Text Detection Models |
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### **Overview** |
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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. |
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### **Performance** |
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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. |
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### How to use |
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### Transformers |
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```bash |
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pip install transformers |
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``` |
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```python |
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import torch |
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import requests |
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from PIL import Image |
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from transformers import AutoImageProcessor, AutoBackbone |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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processor = AutoImageProcessor.from_pretrained("jadechoghari/textnet-tiny") |
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model = AutoBackbone.from_pretrained("jadechoghari/textnet-base") |
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inputs = processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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``` |
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### **Training** |
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We first compare TextNet with representative hand-crafted backbones, |
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such as ResNets and VGG16. For a fair comparison, |
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all models are first pre-trained on IC17-MLT [52] and then |
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finetuned on Total-Text. The proposed |
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TextNet models achieve a better trade-off between accuracy |
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and inference speed than previous hand-crafted models by a |
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significant margin. In addition, notably, our TextNet-T, -S, and |
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-B only have 6.8M, 8.0M, and 8.9M parameters respectively, |
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which are more parameter-efficient than ResNets and VGG16. |
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These results demonstrate that TextNet models are effective for |
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text detection on the GPU device. |
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### **Applications** |
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Perfect for real-world text detection tasks, including: |
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- Natural scene text recognition |
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- Multi-lingual and multi-oriented text detection |
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- Document text region analysis |
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### **Contribution** |
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This model was contributed by [Raghavan](https://huggingface.co/Raghavan), |
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[jadechoghari](https://huggingface.co/jadechoghari) |
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and [nielsr](https://huggingface.co/nielsr). |
