Image Segmentation
Transformers
PyTorch
modnet
feature-extraction
image-matting
background-removal
computer-vision
custom-architecture
custom_code
Instructions to use boopathiraj/MODNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boopathiraj/MODNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="boopathiraj/MODNet", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("boopathiraj/MODNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 457 Bytes
d8953e6 4bdad8d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import torch
from torch import nn
from transformers import PreTrainedModel, PretrainedConfig
from .configuration_modnet import MODNetConfig
from .modnet import MODNet
class HF_MODNet(PreTrainedModel):
config_class = MODNetConfig
def __init__(self, config):
super().__init__(config)
self.modnet = MODNet(backbone_pretrained=False)
def forward(self, x, inference=True):
return self.modnet(x, inference) |