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
File size: 370 Bytes
a9ee9ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "BiRefNetImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 1024,
"width": 1024
}
}
|