---
license: apache-2.0
library_name: onnxruntime
tags:
  - background-removal
  - image-segmentation
  - alpha-matting
  - onnx
  - computer-vision
  - remove-background
pipeline_tag: image-segmentation
---

# withoutBG Open Weights (ONNX)

Open-source background removal and alpha matting from RGB images. This repository
hosts the **OSS variant** exported as a self-contained ONNX graph for ONNX Runtime.

The graph includes the full WBGNet pipeline — upstream encoders, matting head, and
OSS refiner — so no PyTorch checkpoints are needed at inference time.

- **Try it live:** [withoutBG on Hugging Face Spaces](https://huggingface.co/spaces/withoutbg/withoutbg)
- **Website:** [withoutbg.com/open-weights-model](https://withoutbg.com/open-weights-model)
- **Benchmarks:** [withoutbg.com/open-weights-model/results](https://withoutbg.com/open-weights-model/results)

## Model details

| Field | Value |
|-------|-------|
| Variant | `oss` |
| Version | `4.1.0` |
| Format | ONNX (opset 18) |
| Precision | fp32 |
| Max resolution | 768 |
| ONNX input tensor | 1024 × 1024 (fixed letterbox) |
| ONNX output tensor | 768 × 768 |
| Transformer opt | disabled |
| ORT offline opt | extended |
| Size | ~495 MB |
| SHA256 | `7873ec427ac6928bc91a3b6e1ddd32715a02d4b85836e78f0afacacee533b82f` |

## Files

Always distribute the ONNX file and its sidecar JSON together:

- `withoutbg-open-weights.onnx` — inference graph (WBGNet pipeline with OSS upstreams and refiner)
- `withoutbg-open-weights.onnx.json` — sidecar metadata (I/O names, shapes, SHA256, canvas sizes)

Read the sidecar first. It is the authoritative source for `canvas_size` (ONNX
input letterbox size), `output_canvas_size` (768 — the fixed alpha tensor size),
`refiner_canvas_size` (768 — the effective max resolution), input/output names,
precision, model version, and SHA256.

## Architecture

The OSS variant uses smaller open-source-friendly upstream models:

- **Depth:** DepthAnythingV2 `vits`
- **Foreground segmentation:** DINOv3 `vits16`
- **Semantic:** ISNet
- **Matting:** shared with the API variant
- **Refiner:** OSS refiner baked into the graph at **768px max resolution**

The refiner runs inside the ONNX graph. **Maximum output resolution is 768px** —
not 1024. Consumers letterbox to the fixed ONNX input tensor (`canvas_size` in the
sidecar) and run a single inference session.

## Input / output contract

> **Max resolution is 768px.** Input letterboxing must match `canvas_size` (1024);
> the graph returns alpha at `output_canvas_size` (768). Detail refinement is
> capped at `refiner_canvas_size` (768).

The graph expects a **letterboxed** RGB tensor sized to `canvas_size` from the sidecar:

| | Name | Shape | Dtype | Range |
|---|------|-------|-------|-------|
| Input | `rgb` | `[1, 3, 1024, 1024]` | float32 | `[0, 1]`, NCHW |
| Output | `alpha` | `[1, 1, 768, 768]` | float32 | `[0, 1]` |

Preprocessing (required):

1. Convert image to RGB.
2. Read `canvas_size` from the sidecar (1024 for this export).
3. Resize longest side to `canvas_size`, preserve aspect ratio.
4. Paste at top-left on a black `canvas_size` × `canvas_size` canvas.
5. Normalize to float32 `[0, 1]`, transpose HWC → CHW, add batch dim.

Effective refinement is limited to **768px** (`refiner_canvas_size` in the sidecar).

Postprocessing (required):

1. Scale the resized image dimensions from `canvas_size` to `output_canvas_size`.
2. Crop alpha to that region on the output tensor (top-left, before padding).
3. Resize alpha back to the original image dimensions.
4. Attach as PNG alpha channel for cutout output.

## Download

```python
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="withoutbg/withoutbg-openweights-onnx",
    filename="withoutbg-open-weights.onnx",
)
sidecar_path = hf_hub_download(
    repo_id="withoutbg/withoutbg-openweights-onnx",
    filename="withoutbg-open-weights.onnx.json",
)
```

Or with the CLI:

```bash
hf download withoutbg/withoutbg-openweights-onnx \
  withoutbg-open-weights.onnx \
  withoutbg-open-weights.onnx.json
```

## Usage

```python
from pathlib import Path
import json
import numpy as np
import onnxruntime as ort
from PIL import Image

model_path = Path("withoutbg-open-weights.onnx")
sidecar = json.loads(model_path.with_suffix(model_path.suffix + ".json").read_text())
canvas = sidecar.get("canvas_size", 1024)
output_canvas = sidecar.get("output_canvas_size", sidecar["output_shape"][2])
input_name = sidecar.get("input_name", "rgb")

session = ort.InferenceSession(str(model_path), providers=["CPUExecutionProvider"])

image = Image.open("input.jpg").convert("RGB")
orig_w, orig_h = image.size
scale = canvas / max(orig_w, orig_h)
new_w = max(1, round(orig_w * scale))
new_h = max(1, round(orig_h * scale))

resized = image.resize((new_w, new_h), Image.Resampling.BILINEAR)
padded = Image.new("RGB", (canvas, canvas), (0, 0, 0))
padded.paste(resized, (0, 0))

rgb = np.asarray(padded, dtype=np.float32) / 255.0
rgb = np.transpose(rgb, (2, 0, 1))[None, ...]

alpha_canvas = session.run(None, {input_name: rgb})[0][0, 0]
crop_h = max(1, round(new_h * output_canvas / canvas))
crop_w = max(1, round(new_w * output_canvas / canvas))
alpha_crop = alpha_canvas[:crop_h, :crop_w]
alpha_u8 = np.clip(alpha_crop * 255.0, 0, 255).astype(np.uint8)
alpha = Image.fromarray(alpha_u8, "L").resize((orig_w, orig_h), Image.Resampling.BILINEAR)

out = image.copy()
out.putalpha(alpha)
out.save("output.png")
```

## Runtime dependencies

```text
python >=3.11
numpy
pillow
onnxruntime
```

For Hugging Face downloads, also install `huggingface_hub`.

## License

Apache-2.0 — see [withoutbg.com/open-weights-model/license](https://withoutbg.com/open-weights-model/license).

## Third-party terms

This model uses DINOv3 as an upstream component. See the
[DINOv3 license](https://ai.meta.com/resources/models-and-libraries/dinov3-license/).

## Links

- [GitHub: withoutbg-inference](https://github.com/withoutbg/withoutbg-inference)
- [Python package (PyPI)](https://pypi.org/project/withoutbg/)
- [Docker Hub: app (CPU)](https://hub.docker.com/r/withoutbg/withoutbg-openweights-v3-app-cpu)
- [Docker Hub: service (CPU)](https://hub.docker.com/r/withoutbg/withoutbg-openweights-v3-service-cpu)
- [GIMP plugin](https://github.com/withoutbg/withoutbg-gimp)
- [Mac app](https://withoutbg.com/open-weights-model/mac-app)