Improve model card for MLLMSeg_InternVL2_5_1B_RES

This PR significantly enhances the model card for the `MLLMSeg_InternVL2_5_1B_RES` model by:

- Adding the `pipeline_tag: image-segmentation`, which makes the model discoverable on the Hugging Face Hub under this pipeline (https://huggingface.co/models?pipeline_tag=image-segmentation).
- Specifying `library_name: transformers`, enabling the "How to use" widget on the model page for easier integration.
- Linking directly to the paper: [Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder](https://huggingface.co/papers/2508.04107).
- Including a direct link to the official GitHub repository for access to the code.
- Adding a comprehensive "How to Use" section with a runnable Python code snippet for inference, along with important notes regarding input/output processing.
- Incorporating performance metrics and visualization examples from the original GitHub repository to provide a clearer understanding of the model's capabilities.

This update aims to greatly improve the model's visibility and usability for the community.

README.md

@@ -1,3 +1,181 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-segmentation
+library_name: transformers
+---
+
+# MLLMSeg: Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder
+
+This repository contains the `MLLMSeg_InternVL2_5_1B_RES` model, which was presented in the paper [Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder](https://huggingface.co/papers/2508.04107).
+
+**MLLMSeg** aims to segment image regions specified by referring expressions. While Multimodal Large Language Models (MLLMs) are proficient in semantic understanding, their token-generation approach often struggles with pixel-level dense prediction tasks like segmentation. To address this, MLLMSeg proposes a novel framework that fully leverages the inherent visual detail features encoded in the MLLM's vision encoder, eliminating the need for an extra visual encoder. It further introduces a detail-enhanced and semantic-consistent feature fusion module (DSFF) to integrate visual details with semantic features from the Large Language Model (LLM). Finally, a lightweight mask decoder (with only 34M parameters) is established to optimize the use of these features for precise mask prediction. This approach strikes a better balance between performance and computational cost compared to existing SAM-based and SAM-free methods.
+
+The official code is available on GitHub: [https://github.com/jcwang0602/MLLMSeg](https://github.com/jcwang0602/MLLMSeg)
+
+## Model Architecture
+<p align="center">
+  <img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/method.png" width="800">
+</p>
+
+## Quick Start / How to Use
+
+This section provides instructions on how to use our pre-trained model for inference. Our models accept images of any size as input. The model outputs are normalized to relative coordinates within a 0-1000 range (e.g., a bounding box defined by top-left and bottom-right coordinates). For visualization, you will need to convert these relative coordinates back to the original image dimensions.
+
+### Installation
+
+First, install the `transformers` library and other necessary dependencies. Note that `flash-attn` requires a GPU for installation.
+
+```bash
+conda create -n mllmseg python==3.10.18 -y
+conda activate mllmseg
+pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu118 # Adjust for your CUDA version
+pip install -r requirements.txt # Assuming requirements.txt from the cloned repo
+pip install flash-attn==2.3.6 --no-build-isolation # Note: requires GPU to install
+```
+
+### Inference Code Example
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+# Load the model and tokenizer
+# Note: trust_remote_code=True is required for this model architecture
+model_path = 'jcwang0602/MLLMSeg_InternVL2_5_1B_RES' 
+model = AutoModel.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
+
+# Example image (replace with your image path)
+# You can find example images in the GitHub repository of MLLMSeg, e.g., in the 'examples/images' directory.
+image_path = './path/to/your/image.png' 
+pixel_values = load_image(image_path, max_num=6).to(torch.bfloat16).cuda()
+generation_config = dict(max_new_tokens=1024, do_sample=True)
+
+# Example query for referring expression segmentation
+question = "Please segment the person in the image." # Replace with your specific referring expression
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+print(f'User: {question}
+Assistant: {response}')
+
+# The 'response' will contain the segmentation mask coordinates in a specific format (normalized 0-1000).
+# You will need to parse these coordinates and visualize the mask as per the paper's methodology or example scripts.
+```
+
+## Performance Metrics
+
+### Referring Expression Segmentation
+<img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/tab_res.png" width="800">
+
+### Referring Expression Comprehension
+<img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/tab_rec.png" width="800">
+
+### Generalized Referring Expression Segmentation
+<img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/tab_gres.png" width="800">
+
+## Visualization
+
+### Referring Expression Segmentation
+<img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/res.png" width="800">
+
+### Referring Expression Comprehension
+<img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/rec.png" width="800">
+
+### Generalized Referring Expression Segmentation
+<img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/gres.png" width="800">
+
+## Citation
+If our work is useful for your research, please consider citing:
+
+```bibtex
+@misc{wang2025unlockingpotentialmllmsreferring,
+      title={Unlocking the Potential of MLLMs in Referring Expression Segmentation via a Light-weight Mask Decoder}, 
+      author={Jingchao Wang and Zhijian Wu and Dingjiang Huang and Yefeng Zheng and Hong Wang},
+      year={2025},
+      eprint={2508.04107},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2508.04107}, 
+}
+```