Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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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, device_map="auto")# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True, dtype="auto", device_map="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
Code example fix
#23
by rbgo - opened
README.md
CHANGED
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@@ -105,7 +105,7 @@ Either load the pipeline
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from transformers import pipeline
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image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
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pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
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pillow_mask = pipe(
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pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
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```
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# prepare input
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image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
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orig_im = io.imread(
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orig_im_size = orig_im.shape[0:2]
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image = preprocess_image(orig_im, model_input_size).to(device)
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@@ -150,7 +150,7 @@ result_image = postprocess_image(result[0][0], orig_im_size)
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# save result
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pil_im = Image.fromarray(result_image)
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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(
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no_bg_image.paste(orig_image, mask=pil_im)
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```
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from transformers import pipeline
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image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
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pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
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pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
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pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
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```
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# prepare input
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image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
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orig_im = io.imread(image_path)
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orig_im_size = orig_im.shape[0:2]
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image = preprocess_image(orig_im, model_input_size).to(device)
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# save result
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pil_im = Image.fromarray(result_image)
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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(image_path)
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no_bg_image.paste(orig_image, mask=pil_im)
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```
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