Instructions to use nobg/FeyNobg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- nobg
How to use nobg/FeyNobg with nobg:
pip install nobg
import torch from loadimg import load_img from nobg import AutoModel, AutoProcessor model = AutoModel.from_pretrained("nobg/FeyNobg").eval() processor = AutoProcessor.from_pretrained("nobg/FeyNobg") image = load_img("input.jpg").convert("RGB") inputs = processor(image, return_tensors="pt") with torch.no_grad(): outputs = model(pixel_values=inputs["pixel_values"]) alpha = processor.post_process_alpha_matting(outputs, target_sizes=[(image.height, image.width)])[0] processor.cutout(image, alpha).save("output.png") - BiRefNet
How to use nobg/FeyNobg with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("nobg/FeyNobg", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("nobg/FeyNobg") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +23 -23
preprocessor_config.json
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{
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "BiRefNetImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 1024,
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"width": 1024
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}
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}
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{
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "BiRefNetImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 1024,
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"width": 1024
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}
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}
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