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@@ -33,17 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-example1.jpeg filter=lfs diff=lfs merge=lfs -text
-example2.jpeg filter=lfs diff=lfs merge=lfs -text
-example3.jpeg filter=lfs diff=lfs merge=lfs -text
-dataset/training/im/p_00a4eda7.png filter=lfs diff=lfs merge=lfs -text
-dataset/training/im/p_00a5b702.png filter=lfs diff=lfs merge=lfs -text
-dataset/validation/im/p_00a7a27c.png filter=lfs diff=lfs merge=lfs -text
-examples/image/example01.jpeg filter=lfs diff=lfs merge=lfs -text
-examples/image/example02.jpeg filter=lfs diff=lfs merge=lfs -text
-examples/image/example03.jpeg filter=lfs diff=lfs merge=lfs -text
-examples/image/image01.png filter=lfs diff=lfs merge=lfs -text
-examples/image/image01_no_background.png filter=lfs diff=lfs merge=lfs -text
-hf_space/example01.jpeg filter=lfs diff=lfs merge=lfs -text
-hf_space/example02.jpeg filter=lfs diff=lfs merge=lfs -text
-hf_space/example03.jpeg filter=lfs diff=lfs merge=lfs -text
+example01.jpeg filter=lfs diff=lfs merge=lfs -text
+example02.jpeg filter=lfs diff=lfs merge=lfs -text
+example03.jpeg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,73 +1,13 @@
 ---
 title: Open Remove Background Model (ormbg)
-license: apache-2.0
-tags:
-  - segmentation
-  - remove background
-  - background
-  - background-removal
-  - Pytorch
-pretty_name: Open Remove Background Model
-models:
-  - schirrmacher/ormbg
-datasets:
-  - schirrmacher/humans
 emoji: 💻
 colorFrom: red
 colorTo: red
 sdk: gradio
 sdk_version: 4.29.0
-app_file: hf_space/app.py
+app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-# Open Remove Background Model (ormbg)
-
-[>>> DEMO <<<](https://huggingface.co/spaces/schirrmacher/ormbg)
-
-Join our [Research Discord Group](https://discord.gg/YYZ3D66t)!
-
-![](examples/image/image01_no_background.png)
-
-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). The model was trained with the synthetic [Human Segmentation Dataset](https://huggingface.co/datasets/schirrmacher/humans), [P3M-10k](https://paperswithcode.com/dataset/p3m-10k), [PPM-100](https://github.com/ZHKKKe/PPM) and [AIM-500](https://paperswithcode.com/dataset/aim-500).
-
-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 ormbg/inference.py
-```
-
-## Training
-
-Install dependencies:
-
-```
-conda env create -f environment.yaml
-conda activate ormbg
-```
-
-Replace dummy dataset with [training dataset](https://huggingface.co/datasets/schirrmacher/humans).
-
-```
-python3 ormbg/train_model.py
-```
-
-# Research
-
-I started training the model with synthetic images of the [Human Segmentation Dataset](https://huggingface.co/datasets/schirrmacher/humans) crafted with [LayerDiffuse](https://github.com/layerdiffusion/LayerDiffuse). However, I noticed that the model struggles to perform well on real images.
-
-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).
-
-Latest changes (05/07/2024):
-
-- Added [P3M-10K](https://paperswithcode.com/dataset/p3m-10k) dataset for training and validation
-- Added [AIM-500](https://paperswithcode.com/dataset/aim-500) dataset for training and validation
-- Added [PPM-100](https://github.com/ZHKKKe/PPM) dataset for training and validation
-- Applied [Grid Dropout](https://albumentations.ai/docs/api_reference/augmentations/dropout/grid_dropout/) to make the model smarter
-
-Next steps:
-
-- Expand dataset with synthetic and real images
-- Research on multi-step segmentation/matting by incorporating [ViTMatte](https://github.com/hustvl/ViTMatte)
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -6,7 +6,7 @@ import gradio as gr
 from ormbg import ORMBG
 from PIL import Image
 
-model_path = "models/ormbg.pth"
+model_path = "../models/ormbg.pth"
 
 # Load the model globally but don't send to device yet
 net = ORMBG()
@@ -70,9 +70,9 @@ If you identify cases where the model fails, <a href='https://huggingface.co/sch
 """
 
 examples = [
-    "./examples/image/example1.jpeg",
-    "./examples/image/example2.jpeg",
-    "./examples/image/example3.jpeg",
+    "example1.jpeg",
+    "example2.jpeg",
+    "example3.jpeg",
 ]
 
 demo = gr.Interface(
@@ -85,4 +85,6 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch(share=False, allowed_paths=["ormbg", "models", "examples"])
+    demo.launch(
+        share=False, root_path="../", allowed_paths=["../hf_space", "../models"]
+    )