---
title: MLCD-Seg-7B
canonical_url: "https://www.modelscope.cn/models/deepglint/MLCD-Seg-7B"
md_url: "https://www.modelscope.cn/models/deepglint/MLCD-Seg-7B.md"
repository: deepglint/MLCD-Seg-7B
last_updated: 2026-01-19
license: apache-2.0
model_type:
  - qwen2
architectures:
  - LlavaQwenForCausalLM
base_model:
  - DeepGlint-AI/MLCD-Embodied-7B
base_model_relation: finetune
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task vlm"
  - "sglang 0.5.2"
downloads: 40
stars: 0
---

# MLCD-Seg-7B

> MLCD-Seg-7B - deepglint 在 ModelScope 开源的模型。RefCOCO Segmentation Evaluation:

deepglint/MLCD-Seg-7B 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可，基于 DeepGlint-AI/MLCD-Embodied-7B 构建，可用 deploy_task vlm、sglang 0.5.2 部署。

- **Repository**: deepglint/MLCD-Seg-7B
- **License**: apache-2.0
- **Base model**: DeepGlint-AI/MLCD-Embodied-7B
- **Inference backends**: deploy_task vlm, sglang 0.5.2
- **Downloads**: 40
- **Stars**: 0
- **Last updated**: 2026-01-19

Source: https://www.modelscope.cn/models/deepglint/MLCD-Seg-7B

---

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcocog)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-5)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-5?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-3)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-3?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcocog-1)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcocog-1?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-8)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-8?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-4)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-4?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco-9)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco-9?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=multi-label-cluster-discrimination-for-visual)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/multi-label-cluster-discrimination-for-visual/referring-expression-segmentation-on-refcoco)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refcoco?p=multi-label-cluster-discrimination-for-visual) 


## 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

Install the evaluation tool and execute the evaluation script:  

```bash
git clone https://github.com/deepglint/unicom/tree/main
cd downstream
bash ./eval/scripts/eval_refcoco.sh
```

## Citations
```
@misc{mlcdseg_wukun,
  author = {Wu, Kun and Xie, Yin and Zhou, Xinyu and An, Xiang, and Deng, Jiankang},
  title = {MLCD-seg-7B},
  year = {2024},
  url = {https://github.com/deepglint/unicom/tree/main/downstream},
}
```