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briaai
/
RMBG-1.4

Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Pytorch
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Model card Files Files and versions
xet
Community
56

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
RMBG-1.4 / onnx
309 MB
Ctrl+K
Ctrl+K
  • 13 contributors
History: 2 commits
OriLib's picture
OriLib
Upload fp16 ONNX weights (#17)
1e26734 verified about 2 years ago
  • model.onnx
    176 MB
    xet
    Upload ONNX weights (original + 8-bit quantized) (#3) over 2 years ago
  • model_fp16.onnx
    88.2 MB
    xet
    Upload fp16 ONNX weights (#17) about 2 years ago
  • model_quantized.onnx
    44.4 MB
    xet
    Upload ONNX weights (original + 8-bit quantized) (#3) over 2 years ago
  • quantize_config.json
    527 Bytes
    Upload ONNX weights (original + 8-bit quantized) (#3) over 2 years ago