Image Segmentation
Transformers
PyTorch
Safetensors
segformer
Generated from Trainer
document-image-binarization
Instructions to use DiTo97/binarization-segformer-b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DiTo97/binarization-segformer-b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="DiTo97/binarization-segformer-b3")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("DiTo97/binarization-segformer-b3") model = SegformerForSemanticSegmentation.from_pretrained("DiTo97/binarization-segformer-b3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 511 Bytes
d0e3b58 172f844 d0e3b58 172f844 d0e3b58 c858861 d0e3b58 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"do_normalize": true,
"do_reduce_labels": false,
"do_rescale": true,
"do_resize": true,
"feature_extractor_type": "SegformerFeatureExtractor",
"image_mean": [
0.7178310783673725,
0.6656352826441061,
0.561250527769185
],
"image_processor_type": "SegformerImageProcessor",
"image_std": [
0.11939309086593752,
0.12023050606805916,
0.10785259768170144
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 1024,
"width": 1024
}
}
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