BEN2 Base MLX

MLX builds of BEN2 Base, made by Moonbox for Apple Silicon Macs. Each precision is a folder; keep a folder's files together. Only the parts listed below are converted: the rest of the pipeline comes from the original repository unless the section below says otherwise.

Files

The last column is how far each build is from the original PyTorch model on the same inputs (see the section below for what was measured).

Precision	File	Size	sha256	Checked against the original
bf16	`bf16/config.json`	0.2 kB	`1832ec222caf`	MAE <= 0.0002, mask IoU >= 0.9987
bf16	`bf16/mlx_model.py`	20.5 kB	`eff7a4afb5ad`	MAE <= 0.0002, mask IoU >= 0.9987
bf16	`bf16/model.safetensors`	188.3 MB	`5b8344c88ff8`	MAE <= 0.0002, mask IoU >= 0.9987
fp16	`fp16/config.json`	0.2 kB	`cdad08ebd797`	MAE <= 0.00003, mask IoU >= 0.9996
fp16	`fp16/mlx_model.py`	20.5 kB	`eff7a4afb5ad`	MAE <= 0.00003, mask IoU >= 0.9996
fp16	`fp16/model.safetensors`	188.3 MB	`5c446d204f3f`	MAE <= 0.00003, mask IoU >= 0.9996
8bit	`8bit/config.json`	0.3 kB	`8e36d767bc3b`	MAE <= 0.0003, mask IoU >= 0.9983
8bit	`8bit/mlx_model.py`	20.5 kB	`eff7a4afb5ad`	MAE <= 0.0003, mask IoU >= 0.9983
8bit	`8bit/model.safetensors`	105.6 MB	`c4a4142ef47b`	MAE <= 0.0003, mask IoU >= 0.9983
6bit	`6bit/config.json`	0.3 kB	`9c6f176e545f`	MAE <= 0.0004, mask IoU >= 0.9966
6bit	`6bit/mlx_model.py`	20.5 kB	`eff7a4afb5ad`	MAE <= 0.0004, mask IoU >= 0.9966
6bit	`6bit/model.safetensors`	83.5 MB	`5f055f598925`	MAE <= 0.0004, mask IoU >= 0.9966
4bit	`4bit/config.json`	0.3 kB	`0fa35f574f4a`	MAE <= 0.0012, mask IoU >= 0.9923
4bit	`4bit/mlx_model.py`	20.5 kB	`eff7a4afb5ad`	MAE <= 0.0012, mask IoU >= 0.9923
4bit	`4bit/model.safetensors`	61.4 MB	`ada7dec10880`	MAE <= 0.0012, mask IoU >= 0.9923

How to run

BEN2 Base ported to MLX. Each folder holds model.safetensors, config.json and mlx_model.py, the port (only mlx, numpy and pillow needed):

import numpy as np, mlx_model
from PIL import Image
model = mlx_model.load("fp16")
rgb = np.array(Image.open("in.jpg").convert("RGB"))
alpha = mlx_model.matte(model, rgb)                   # HxW uint8, as BEN2.py's inference() (1024x1024 internally)
Image.fromarray(np.dstack([rgb, alpha])).save("cutout.png")

The port runs BEN_Base as released (Swin-B on four 512 px crops plus the whole image, MCLM / MCRM decoder). The quantized folders quantize the linear layers (Swin, attention, feed-forward; group size 64); the convolutions stay bf16. The original's optional refine_foreground colour decontamination isn't ported.

Precision	Size	MAE vs PyTorch fp32 (worst image)	Mask IoU (worst)
`fp16/`	188 MB	0.00003	0.9996
`bf16/`	188 MB	0.0002	0.9987
`8bit/`	106 MB	0.0003	0.9983
`6bit/`	84 MB	0.0004	0.9966
`4bit/`	62 MB	0.0012	0.9923

About 0.7 s per image on an Apple M5 at every precision.

How these were made

From BEN2 Base at revision e48a20765f: the original weights loaded in MLX and stored as bf16 or fp16, or quantized with MLX's affine quantization (8bit, 6bit, 4bit, group size 64; layers MLX can't quantize, such as convolutions and norms, stay bf16). Every build was checked against the original before upload; the sha256 column above is what the files must hash to.

License

MIT. These files are a derivative of the original model and carry its license: it is copied in this repository, and it governs your use of these files. The original model card follows unchanged.

Original model card

BEN2: Background Erase Network

Overview

BEN2 (Background Erase Network) introduces a novel approach to foreground segmentation through its innovative Confidence Guided Matting (CGM) pipeline. The architecture employs a refiner network that targets and processes pixels where the base model exhibits lower confidence levels, resulting in more precise and reliable matting results. This model is built on BEN:

BEN2 access

BEN2 was trained on the DIS5k and our 22K proprietary segmentation dataset. Our enhanced model delivers superior performance in hair matting, 4K processing, object segmentation, and edge refinement. Our Base model is open source. To try the full model through our free web demo or integrate BEN2 into your project with our API:

🌐 backgrounderase.com

Contact us

For access to our commercial model email us at sales@backgrounderase.com
Our website: https://backgrounderase.com/
Follow us on X: https://x.com/PramaResearch/

Installation

pip install -e "git+https://github.com/PramaLLC/BEN2.git#egg=ben2"

Quick start code

from ben2 import BEN_Base
from PIL import Image
import torch


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

file = "./image.png" # input image

model = BEN_Base.from_pretrained("PramaLLC/BEN2")
model.to(device).eval()

image = Image.open(file)
foreground = model.inference(image, refine_foreground=False,) #Refine foreground is an extract postprocessing step that increases inference time but can improve matting edges. The default value is False.

foreground.save("./foreground.png")

Batch image processing

from ben2 import BEN_Base
from PIL import Image
import torch


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')



model = BEN_Base.from_pretrained("PramaLLC/BEN2")
model.to(device).eval()


file1 = "./image1.png" # input image1
file2 = "./image2.png" # input image2
image1 = Image.open(file1)
image2 = Image.open(file2)



foregrounds = model.inference([image1, image2]) #  We recommend that the batch size not exceed 3 for consumer GPUs as there are minimal inference gains due to our custom batch processing for the MVANet decoding steps.
foregrounds[0].save("./foreground1.png")
foregrounds[1].save("./foreground2.png")

BEN2 video segmentation

Video Segmentation

sudo apt update
sudo apt install ffmpeg

from ben2 import BEN_Base
from PIL import Image
import torch


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

video_path = "/path_to_your_video.mp4"# input video

model = BEN_Base.from_pretrained("PramaLLC/BEN2")
model.to(device).eval()


model.segment_video(
    video_path= video_path,
    output_path="./", # Outputs will be saved as foreground.webm or foreground.mp4. The default value is "./"
    fps=0, # If this is set to 0 CV2 will detect the fps in the original video. The default value is 0.
    refine_foreground=False,  #refine foreground is an extract postprocessing step that increases inference time but can improve matting edges. The default value is False.
    batch=1,  # We recommended that batch size not exceed 3 for consumer GPUs as there are minimal inference gains. The default value is 1.
    print_frames_processed=True,  #Informs you what frame is being processed. The default value is True.
    webm = False, # This will output an alpha layer video but this defaults to mp4 when webm is false. The default value is False.
    rgb_value= (0, 255, 0) # If you do not use webm this will be the RGB value of the resulting background only when webm is False. The default value is a green background (0,255,0).
 )