---
library_name: transformers
---
## 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
```bash
pip install transformers
```

```python
import torch
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoBackbone

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("jadechoghari/textnet-base")
model = AutoBackbone.from_pretrained("jadechoghari/textnet-base")

inputs = processor(image, return_tensors="pt")
with torch.no_grad():
  outputs = model(**inputs)
```
### **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 TextNet models are effective for
text detection on the GPU device.

### **Applications**
Perfect for real-world text detection tasks, including:
- Natural scene text recognition
- Multi-lingual and multi-oriented text detection
- Document text region analysis

### **Contribution**
This model was contributed by [Raghavan](https://huggingface.co/Raghavan), 
[jadechoghari](https://huggingface.co/jadechoghari) 
and [nielsr](https://huggingface.co/nielsr).