Improve model card for MLLMSeg: Add metadata, abstract, and usage example (#1)

- Improve model card for MLLMSeg: Add metadata, abstract, and usage example (5bd60312454c0e21f3264af59a5e4468f60991b3)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,3 +1,176 @@
----
-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_8B_RES` model 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).
+
+Reference Expression Segmentation (RES) aims to segment image regions specified by referring expressions. While Multimodal Large Language Models (MLLMs) excel in semantic understanding, their token-generation paradigm often struggles with pixel-level dense prediction. MLLMSeg addresses this by fully exploiting the inherent visual detail features encoded in the MLLM vision encoder without introducing an extra visual encoder. It proposes a detail-enhanced and semantic-consistent feature fusion module (DSFF) and establishes a light-weight mask decoder (only 34M network parameters) to optimally leverage detailed spatial features and semantic features for precise mask prediction. Extensive experiments demonstrate that MLLMSeg generally surpasses both SAM-based and SAM-free competitors, striking a better balance between performance and cost.
+
+Code: https://github.com/jcwang0602/MLLMSeg
+
+<p align="center">
+  <img src="https://github.com/jcwang0602/MLLMSeg/raw/main/assets/method.png" width="800">
+</p>
+
+## Usage
+
+You can use this model with the `transformers` library. Below is an example demonstrating how to load and use the `MLLMSeg_InternVL2_5_8B_RES` model for inference.
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+import requests
+from io import BytesIO
+
+# Define image preprocessing utility functions
+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
+
+    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])
+
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    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]
+
+    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_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):
+    if image_file.startswith(('http://', 'https://')):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        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 model and tokenizer
+model_path = "jcwang0602/MLLMSeg_InternVL2_5_8B_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)
+
+# Load an example image (replace with your image path or URL)
+image_path = "https://github.com/jcwang0602/MLLMSeg/raw/main/assets/res_0.png" # Example image from the repo
+pixel_values = load_image(image_path, max_num=6).to(torch.bfloat16).cuda()
+
+# Define the referring expression
+question = "Please segment the person in the screenshot."
+
+# Set generation configuration
+generation_config = dict(max_new_tokens=1024, do_sample=False, temperature=0.0)
+
+# Generate response and segmentation mask
+# The output_segmentation_mask=True parameter is crucial for getting the mask directly.
+response, history, pred_mask = model.chat(
+    tokenizer, pixel_values, question, generation_config, history=None, return_history=True, output_segmentation_mask=True
+)
+
+print(f'User: {question}\
+Assistant: {response}')
+# `pred_mask` will contain the predicted segmentation mask. It's a torch.Tensor.
+# You can save or visualize it. For example, to save it as an image:
+# from torchvision.utils import save_image
+# save_image(pred_mask.float(), "segmentation_mask.png")
+```
+
+## 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}, 
+}
+```