DeepGlint-AI/MLCD-Seg

RefCOCO Segmentation Evaluation:

Dataset	Split	MLCD-seg-7B	EVF-SAM	GLaMM	VisionLLM v2	LISA
RefCOCO	val	83.6	82.4	79.5	79.2	74.9
RefCOCO	testA	85.3	84.2	83.2	82.3	79.1
RefCOCO	testB	81.5	80.2	76.9	77.0	72.3
RefCOCO+	val	79.4	76.5	72.6	68.9	65.1
RefCOCO+	testA	82.9	80.0	78.7	75.8	70.8
RefCOCO+	testB	75.6	71.9	64.6	61.8	58.1
RefCOCOg	val	79.7	78.2	74.2	73.3	67.9
RefCOCOg	test	80.5	78.3	74.9	74.8	70.6

Evaluation

If you just want to use this code, please refer to this sample below

from transformers import AutoModel, AutoTokenizer
from PIL import Image


model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path
mlcd_seg = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    trust_remote_code=True
).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# Assuming you have an image named test.jpg
seg_img = Image.open("test.jpg").convert('RGB')
seg_prompt = "Could you provide a segmentation mask for the right giraffe in this image?"
pred_mask = model.seg(seg_img, seg_prompt, tokenizer, force_seg=False)

If you want to use this code measurement dataset (e.g. refcoco), then you need to use the following method

from transformers import AutoModel, AutoTokenizer
from PIL import Image


model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path
mlcd_seg = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    trust_remote_code=True
).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# Assuming you have an image named test.jpg
seg_img = Image.open("test.jpg").convert('RGB')
seg_prompt = "Could you provide a segmentation mask for the right giraffe in this image?"
pred_mask = model.seg(seg_img, seg_prompt, tokenizer, force_seg=True)

If you want to use this code in video, please refer to this sample below

from transformers import AutoModel, AutoTokenizer
from PIL import Image
import torch
from torchvision import transforms
import subprocess
import os

# video path
video_path = "updownfunk.mp4"
input_dir = "frames"
output_dir = "mask_frames"
os.makedirs(input_dir, exist_ok=True)
os.makedirs(output_dir, exist_ok=True)

# assert you have ffmpeg installed, mp4 -> jpg
cmd = [
    "ffmpeg",
    "-i", video_path,
    "-vf", "fps=30",    # 30FPS
    "-qscale:v", "1",  
    os.path.join(input_dir, "frame_%04d.jpg") 
]
subprocess.run(cmd)

# model path
model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path
mlcd_seg = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    trust_remote_code=True
).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)

# read jpgs
image_files = sorted([f for f in os.listdir(input_dir) if f.endswith(('.jpg', '.png', '.jpeg'))])

for idx, filename in enumerate(image_files, start=1):

    src_path = os.path.join(input_dir, filename)
    seg_img = Image.open(src_path).convert('RGB')

    seg_prompt = "This <video> depicts a group of people dancing.\nCould you provide a segmentation mask for the man in pink suit?"
    pred_mask = mlcd_seg.predict_forward(seg_img, seg_prompt, tokenizer, force_seg=True)

    # Mask visualization
    pred_mask = pred_mask.squeeze(0).cpu()
    pred_mask = (pred_mask > 0.5).float()
    img_tensor = transforms.ToTensor()(seg_img)
    alpha = 0.2  # 20% transparency
    red_mask = torch.tensor([0.0, 1.0, 0.0]).view(3, 1, 1).to(img_tensor.device)  # green mask
    black_bg = torch.zeros_like(img_tensor)  # black background
    masked_area = red_mask * alpha + img_tensor * (1 - alpha)
    background = black_bg * alpha + img_tensor * (1 - alpha)
    combined = torch.where(pred_mask.unsqueeze(0).bool(), masked_area, background)
    combined = combined.cpu()  # [3, H, W], CPU

    # Save masked jpgs
    new_name = f"{idx:04d}{os.path.splitext(filename)[1]}"
    dst_path = os.path.join(output_dir, new_name)
    transforms.ToPILImage()(combined.clamp(0, 1)).save(dst_path)

cmd = [
    "ffmpeg",
    "-y",  
    "-framerate", str(30),  # fps
    "-i", os.path.join(output_dir, "%04d.jpg"), 
    "-c:v", "libx264",
    "-crf", str(23), 
    "-pix_fmt", "yuv420p", 
    "-vf", "fps=" + str(23), 
    "updownfunk_mask.mp4"  # output video
]
# jpgs -> mp4    
subprocess.run(cmd, check=True)

Example

Citations

@misc{mlcdseg_wukun,
  author = {Wu, Kun and Xie, Yin and Zhou, Xinyu and An, Xiang, and Deng, Jiankang, and Jie, Yu},
  title = {MLCD-Seg},
  year = {2025},
  url = {https://github.com/deepglint/unicom/tree/main/downstream},
}