Image-to-Text
Transformers
ONNX
Safetensors
vision-encoder-decoder
image-text-to-text
typst
math-ocr
formula-recognition
browser
grayscale
Instructions to use dbcccc/TypLens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbcccc/TypLens with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="dbcccc/TypLens")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("dbcccc/TypLens") model = AutoModelForMultimodalLM.from_pretrained("dbcccc/TypLens", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 838 Bytes
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"image_processor_type": "DeiTImageProcessor",
"do_resize": true,
"size": {
"height": 384,
"width": 384
},
"resample": 3,
"do_center_crop": false,
"do_rescale": true,
"rescale_factor": 0.00392156862745098,
"do_normalize": true,
"image_mean": [
0.5
],
"image_std": [
0.5
],
"requires_custom_preprocessing": true,
"typlens_preprocessing": {
"version": "native-content-box-gray-v2",
"channels": 1,
"width": 384,
"height": 384,
"grayscale_weights": [
77,
150,
29
],
"grayscale_divisor": 256,
"alpha_background": 255,
"foreground_delta": 12,
"margin_ratio": 0.02,
"min_margin": 1,
"polarity": "perimeter-median-below-128",
"resampler": "pillow-bicubic-u8-v1",
"mean": [
0.5
],
"std": [
0.5
]
}
}
|