Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
SegformerForSemanticSegmentation
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use OwlMaster/FixRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OwlMaster/FixRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="OwlMaster/FixRM", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("OwlMaster/FixRM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| from typing import List | |
| class RMBGConfig(PretrainedConfig): | |
| model_type = "SegformerForSemanticSegmentation" | |
| def __init__( | |
| self, | |
| in_ch=3, | |
| out_ch=1, | |
| **kwargs): | |
| self.in_ch = in_ch | |
| self.out_ch = out_ch | |
| super().__init__(**kwargs) | |