Hugging Face's logo

schirrmacher
/
ormbg

ONNX
segmentation
remove background
background
background-removal
Pytorch
Model card Files Community
9
ormbg
File size: 1,871 Bytes 
150d962
 
 
d7b2280
 
150d962
d7b2280
 
150d962
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d7b2280
 
 
 
150d962
 
 
 
 
 
 
 
 
 
 
 
 
d7b2280
150d962
 
d7b2280
150d962
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
 
---
license: apache-2.0
tags:
  - segmentation
  - background remover
pretty_name: Open Remove Background Model
datasets:
  - schirrmacher/humans
---

# Open Remove Background Model (ormbg)

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

```
test
```

## 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
```

## Support

If you identify edge cases or issues with the model, please contact me!