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README.md

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 ---
+title: Open Remove Background Model (ormbg)
+emoji: 💻
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 4.29.0
+app_file: app.py
+pinned: false
 license: apache-2.0
-tags:
-  - segmentation
-  - remove background
-  - background
-  - background-removal
-  - Pytorch
-pretty_name: Open Remove Background Model
-datasets:
-  - schirrmacher/humans
 ---
 
-# Open Remove Background Model (ormbg)
-
-[>>> DEMO <<<](https://huggingface.co/spaces/schirrmacher/ormbg)
-
-![](examples.jpg)
-
-This model is a **fully open-source background remover** optimized for images with humans. It is based on [Highly Accurate Dichotomous Image Segmentation research](https://github.com/xuebinqin/DIS).
-
-This model is similar to [RMBG-1.4](https://huggingface.co/briaai/RMBG-1.4), but with open training data/process and commercially free to use.
-
-## Inference
-
-```
-python utils/inference.py
-```
-
-## Training
-
-The model was trained with the [Human Segmentation Dataset](https://huggingface.co/datasets/schirrmacher/humans).
-
-After 10.000 iterations with a single NVIDIA GeForce RTX 4090 the following achievements were made:
-
-- Training Time: 8 hours
-- Training Loss: 0.1179
-- Validation Loss: 0.1284
-- Maximum F1 Score: 0.9928
-- Mean Absolute Error: 0.005
-
-Output model: `/models/ormbg.pth`.
-
-## Want to train your own model?
-
-Checkout _Highly Accurate Dichotomous Image Segmentation_ code:
-
-```
-git clone https://github.com/xuebinqin/DIS.git
-cd DIS
-```
-
-Follow the installation instructions on https://github.com/xuebinqin/DIS?tab=readme-ov-file#1-clone-this-repo.
-Download or create some data ([like this](https://huggingface.co/datasets/schirrmacher/humans)) and place it into the DIS project folder.
-
-I am using the folder structure:
-
-- training/im (images)
-- training/gt (ground truth)
-- validation/im (images)
-- validation/gt (ground truth)
-
-Apply this git patch for setting the right paths and remove normalization of images:
-
-```
-git apply dis-repo.patch
-```
-
-Start training:
-
-```
-cd IS-Net
-python train_valid_inference_main.py
-```
-
-Export to ONNX (modify paths if needed):
-
-```
-python utils/pth_to_onnx.py
-```
-
-# Research
-
-Synthetic datasets have limitations for achieving great segmentation results. This is because artificial lighting, occlusion, scale or backgrounds create a gap between synthetic and real images. A "model trained solely on synthetic data generated with naïve domain randomization struggles to generalize on the real domain", see [PEOPLESANSPEOPLE: A Synthetic Data Generator for Human-Centric Computer Vision (2022)](https://arxiv.org/pdf/2112.09290). However, hybrid training approaches seem to be promising and can even improve segmentation results.
-
-Currently I am doing research how to close this gap with the resources I have. There are approaches like considering the pose of humans for improving segmentation results, see [Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation (2019)](https://arxiv.org/pdf/1907.05193).
-
-## Support
-
-This is the first iteration of the model, so there will be improvements!
-
-If you identify cases were the model fails, <a href='https://huggingface.co/schirrmacher/ormbg/discussions' target='_blank'>upload your examples</a>!
-
-Known issues (work in progress):
-
-- close-ups: from above, from below, profile, from side
-- minor issues with hair segmentation when hair creates loops
-- more various backgrounds needed
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference