├── Dockerfile
├── LICENSE
├── README.md
├── example
├── 0.benchmark
│ ├── README.md
│ ├── benchmark.sh
│ ├── evaluate_refcoco.py
│ └── test_dataset_acc.py
└── 1.infer
│ ├── asserts
│ └── example.jpg
│ └── main.py
├── requirements.txt
└── updownfunk_mask.gif
/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM pytorch/pytorch:2.4.1-cuda11.8-cudnn9-devel
2 |
3 | # Set environment variables
4 | ENV PYTHONDONTWRITEBYTECODE=1
5 | ENV PYTHONUNBUFFERED=1
6 | ENV DEBIAN_FRONTEND=noninteractive
7 |
8 | # Set working directory
9 | WORKDIR /app
10 |
11 | # Install basic packages
12 | RUN apt-get update && \
13 | apt-get install -y --no-install-recommends \
14 | git \
15 | wget \
16 | && apt-get clean \
17 | && rm -rf /var/lib/apt/lists/*
18 |
19 | # Copy requirements.txt
20 | COPY requirements.txt .
21 |
22 | # Install the packages
23 | RUN pip install --no-cache-dir --upgrade pip && \
24 | pip install --no-cache-dir -r requirements.txt
25 |
26 | # Install the packages using Tencent Cloud mirror
27 | # RUN pip install --no-cache-dir -i https://mirrors.cloud.tencent.com/pypi/simple/ --trusted-host mirrors.cloud.tencent.com --upgrade pip && \
28 | # pip install --no-cache-dir -i https://mirrors.cloud.tencent.com/pypi/simple/ --trusted-host mirrors.cloud.tencent.com -r requirements.txt
29 |
30 |
31 | # Set an entrypoint that drops you into a shell
32 | ENTRYPOINT ["/bin/bash"]
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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/README.md:
--------------------------------------------------------------------------------
1 |
2 |
A Segment Anything Model with MLCD Integration
3 |
4 |
5 |

6 |

7 |
8 |
9 |
10 |
11 | ## RefCOCO Segmentation Evaluation Results:
12 |
13 |
14 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog?p=multi-label-cluster-discrimination-for-visual)
15 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-5?p=multi-label-cluster-discrimination-for-visual)
16 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-3?p=multi-label-cluster-discrimination-for-visual)
17 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog-1?p=multi-label-cluster-discrimination-for-visual)
18 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-8?p=multi-label-cluster-discrimination-for-visual)
19 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-4?p=multi-label-cluster-discrimination-for-visual)
20 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-9?p=multi-label-cluster-discrimination-for-visual)
21 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=multi-label-cluster-discrimination-for-visual)
22 | [](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=multi-label-cluster-discrimination-for-visual)
23 |
24 |
25 |
26 |
27 | | Dataset | Split | MLCD-seg-7B | EVF-SAM | GLaMM | VisionLLM v2| LISA |
28 | | :-- | :-: | :-: | :-: | :-: | :-: | :-: |
29 | | RefCOCO | val | **83.6** | 82.4 | 79.5 | 79.2 | 74.9 |
30 | | RefCOCO | testA | **85.3** | 84.2 | 83.2 | 82.3 | 79.1 |
31 | | RefCOCO | testB | **81.5** | 80.2 | 76.9 | 77.0 | 72.3 |
32 | | RefCOCO+ | val | **79.4** | 76.5 | 72.6 | 68.9 | 65.1 |
33 | | RefCOCO+ | testA | **82.9** | 80.0 | 78.7 | 75.8 | 70.8 |
34 | | RefCOCO+ | testB | **75.6** | 71.9 | 64.6 | 61.8 | 58.1 |
35 | | RefCOCOg | val | **79.7** | 78.2 | 74.2 | 73.3 | 67.9 |
36 | | RefCOCOg | test | **80.5** | 78.3 | 74.9 | 74.8 | 70.6 |
37 |
38 | ## Example:
39 |
40 |
41 |
42 |
43 |
44 | ## How to use:
45 |
46 | If you just want to use this code, please refer to this sample below
47 | ```python
48 | from transformers import AutoModel, AutoTokenizer
49 | from PIL import Image
50 |
51 |
52 | model_path = "DeepGlint-AI/MLCD-Seg" # or use your local path
53 | mlcd_seg = AutoModel.from_pretrained(
54 | model_path,
55 | torch_dtype=torch.float16,
56 | trust_remote_code=True
57 | ).cuda()
58 | tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
59 | # Assuming you have an image named test.jpg
60 | seg_img = Image.open("test.jpg").convert('RGB')
61 | seg_prompt = "Could you provide a segmentation mask for the right giraffe in this image?"
62 | pred_mask = model.seg(seg_img, seg_prompt, tokenizer, force_seg=False)
63 |
64 | ```
65 |
66 | If you want to use this code in video, please refer to this sample below
67 | ```python
68 | from transformers import AutoModel, AutoTokenizer
69 | from PIL import Image
70 | import torch
71 | from torchvision import transforms
72 | import subprocess
73 | import os
74 |
75 | # video path
76 | video_path = "updownfunk.mp4"
77 | input_dir = "frames"
78 | output_dir = "mask_frames"
79 | os.makedirs(input_dir, exist_ok=True)
80 | os.makedirs(output_dir, exist_ok=True)
81 |
82 | # assert you have ffmpeg installed, mp4 -> jpg
83 | cmd = [
84 | "ffmpeg",
85 | "-i", video_path,
86 | "-vf", "fps=30", # 30FPS
87 | "-qscale:v", "1",
88 | os.path.join(input_dir, "frame_%04d.jpg")
89 | ]
90 | subprocess.run(cmd)
91 |
92 | # model path
93 |
94 | model_path = "/root/MLCD-Seg/" # or use your local path
95 | mlcd_seg = AutoModel.from_pretrained(
96 | model_path,
97 | torch_dtype=torch.float16,
98 | trust_remote_code=True
99 | ).cuda()
100 | tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
101 |
102 |
103 | # read jpgs
104 | image_files = sorted([f for f in os.listdir(input_dir) if f.endswith(('.jpg', '.png', '.jpeg'))])
105 |
106 | for idx, filename in enumerate(image_files, start=1):
107 |
108 | src_path = os.path.join(input_dir, filename)
109 | seg_img = Image.open(src_path).convert('RGB')
110 |
111 | seg_prompt = "This