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
| { | |
| "_name_or_path": "briaai/RMBG-1.4", | |
| "architectures": [ | |
| "BriaRMBG" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "MyConfig.RMBGConfig", | |
| "AutoModelForImageSegmentation": "briarmbg.BriaRMBG" | |
| }, | |
| "custom_pipelines": { | |
| "image-segmentation": { | |
| "impl": "MyPipe.RMBGPipe", | |
| "pt": [ | |
| "AutoModelForImageSegmentation" | |
| ], | |
| "tf": [], | |
| "type": "image" | |
| } | |
| }, | |
| "in_ch": 3, | |
| "model_type": "SegformerForSemanticSegmentation", | |
| "out_ch": 1, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.38.0.dev0" | |
| } | |