Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Pytorch
vision
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custom_code
Instructions to use frederikboisen/RMBG-1.4-wardrobe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frederikboisen/RMBG-1.4-wardrobe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="frederikboisen/RMBG-1.4-wardrobe", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("frederikboisen/RMBG-1.4-wardrobe", trust_remote_code=True, dtype="auto") - Transformers.js
How to use frederikboisen/RMBG-1.4-wardrobe with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'frederikboisen/RMBG-1.4-wardrobe'); - Notebooks
- Google Colab
- Kaggle
File size: 527 Bytes
5d50bf3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"per_channel": false,
"reduce_range": false,
"per_model_config": {
"model": {
"op_types": [
"Concat",
"MaxPool",
"Resize",
"Conv",
"Unsqueeze",
"Cast",
"Shape",
"Relu",
"Sigmoid",
"Gather",
"Constant",
"Slice",
"Add"
],
"weight_type": "QUInt8"
}
}
} |