Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use briaai/RMBG-1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use briaai/RMBG-1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True, dtype="auto") - Transformers.js
How to use briaai/RMBG-1.4 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'briaai/RMBG-1.4'); - Notebooks
- Google Colab
- Kaggle
Interview request: Thoughts on genAI evaluation & documentation
#49
by evatang - opened
Hi! We are researchers from Carnegie Mellon University conducting a study on the evaluation and documentation practices of generative AI developers. Given the popularity and success of your model, we're particularly interested in learning from your team's experiences.
Our study aims to:
- Understand current practices in Gen AI model evaluation and reporting
- Identify challenges faced by developers in these areas
- Explore potential improvements in evaluation and documentation processes
We're seeking participants with hands-on experience in these aspects of Gen AI development. Would any members of your team be interested in participating in a (compensated) interview to share their insights?
For more details about the study and to express interest, here is our recruitment page: https://forms.gle/fbn4734YxrRg6mkBA
origubany changed discussion status to closed