Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Pytorch
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custom_code
Instructions to use briaai/RMBG-1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briaai/RMBG-1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True, dtype="auto") - Transformers.js
How to use briaai/RMBG-1.4 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'briaai/RMBG-1.4'); - Notebooks
- Google Colab
- Kaggle
Update example_inference.py
Browse files- example_inference.py +3 -3
example_inference.py
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@@ -28,10 +28,10 @@ def example_inference():
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result_image = postprocess_image(result[0][0], orig_im_size)
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# save result
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no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
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orig_image = Image.open(im_path)
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no_bg_image
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no_bg_image.save("example_image_no_bg.png")
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result_image = postprocess_image(result[0][0], orig_im_size)
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# save result
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pil_mask_im = Image.fromarray(result_image)
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orig_image = Image.open(im_path)
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no_bg_image = orig_image.copy()
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no_bg_image.putalpha(pil_mask_im)
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no_bg_image.save("example_image_no_bg.png")
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