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# iTeXSnip
Image -> LaTeX
Works with handwritten formulae as well!
## TODO
### V1
- [x] Rating API
- [x] Preferences
- Model load preferences
- Detailed view preferences
- Rating API server
- [x] Complete Detailed Snippet View
### V2
- [ ] Math Solver
- [ ] TeX Snippet Editor
- [ ] Image Export
- [ ] UI Overhaul
- [ ] Optimizations
## Misc
### Quantization
#### Encoder Model
```bash
python -m onnxruntime.quantization.preprocess --input iTexSnip/models/encoder_model.onnx --output encoder-infer.onnx
```
```python
import onnx
from onnxruntime.quantization import quantize_dynamic, QuantType
og = "encoder-infer.onnx"
quant = "encoder-quant.onnx"
quantized_model = quantize_dynamic(og, quant, nodes_to_exclude=['/embeddings/patch_embeddings/projection/Conv'])
```
It might be better if we quantize the encoder using static quantization.
#### Decoder Model
```bash
python -m onnxruntime.quantization.preprocess --input iTexSnip/models/decoder_model.onnx --output decoder-infer.onnx
```
```python
import onnx
from onnxruntime.quantization import quantize_dynamic, QuantType
og = "decoder-infer.onnx"
quant = "decoder-quant.onnx"
quantized_model = quantize_dynamic(og, quant)
```
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