Improve dataset card: Add task categories and tags, update sample usage, refine metadata

This PR improves the dataset card for the PaDT project by:
- Removing `base_model` and `pipeline_tag` from the metadata, as these are typically used for model cards or Spaces, not datasets.
- Adding `task_categories`: `object-detection`, `image-segmentation`, `image-to-text` to accurately reflect the domains the dataset supports, enhancing discoverability.
- Adding comprehensive `tags` such as `mllm`, `multimodal`, `vision-language-model`, `visual-grounding`, `referring-expression-comprehension`, `referring-image-captioning`, and `computer-vision` for improved searchability.
- Adding the official Hugging Face paper link to the paper reference section.
- Updating the `PROMPT` string in the "Quick Start" sample usage section with a more specific example from the Github README, better illustrating the dataset's use in concrete tasks.

These changes make the dataset card more informative, accurate, and easier to navigate on the Hugging Face Hub.

README.md

@@ -1,19 +1,27 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
-base_model:
-  - Qwen/Qwen2.5-VL-3B-Instruct
-  - Qwen/Qwen2.5-VL-7B-Instruct
-pipeline_tag:  image-text-to-text
+license: apache-2.0
+task_categories:
+- object-detection
+- image-segmentation
+- image-to-text
+tags:
+- mllm
+- multimodal
+- vision-language-model
+- visual-grounding
+- referring-expression-comprehension
+- referring-image-captioning
+- computer-vision
 ---
 
 <div align='center'><h1>Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs</h1></div>
 
 <font size=4><div align='center'>[[🔗 Released Code](https://github.com/Gorilla-Lab-SCUT/PaDT)]
  [[🤗 Datasets](https://huggingface.co/collections/PaDT-MLLM/padt-dataset-68e400440ffb8c8f95e5ee20)] [[🤗 Checkpoints](https://huggingface.co/collections/PaDT-MLLM/padt-68e3f5c22e8ecbd6d0d13d43)]</div></font>
- <font size=4><div align='center'>[[📄 Tech Report](https://arxiv.org/abs/2510.01954)]</div></font>
+ <font size=4><div align='center'>[[📄 Tech Report](https://arxiv.org/abs/2510.01954)] [[🤗 Paper](https://huggingface.co/papers/2510.01954)]</div></font>
 
 <div align="center">
 <img src="./assets/Pipeline.webp" width="900"/>
@@ -84,7 +92,7 @@ processor = VisonTextProcessingClass(processor, model.config.vision_config.spati
 processor.prepare(model.model.embed_tokens.weight.shape[0])
 
 # question prompt
-PROMPT = "Please describe this image."
+PROMPT = """Please carefully check the image and detect the object this sentence describes: "The car is on the left side of the horse"."""
 
 # construct conversation
 message = [
@@ -122,7 +130,8 @@ with torch.inference_mode():
     # extract Visual Reference Tokens within the sequence
     completions, feats, labels, vrts, vrts_feats = parseVRTintoCompletion(processor, completion_ids, generate_returned_result['hidden_states'], torch.Tensor([False]))
 
-    print("\ngenerate result:", completions[0])
+    print("
+generate result:", completions[0])
 
     # decode low-level visual task results
     low_res_image_embeds = generate_returned_result.past_image_embeds
@@ -130,7 +139,10 @@ with torch.inference_mode():
     visual_pe = generate_returned_result.past_visual_pe
     decoded_list = model.vl_decode(feats, low_res_image_embeds, high_res_image_embeds, prompt_inputs['image_grid_thw'], visual_pe)
 
-    print(f"\npred_bboxes: {decoded_list['pred_boxes']},\npred_scores: {decoded_list['pred_score'].sigmoid()}\n")
+    print(f"
+pred_bboxes: {decoded_list['pred_boxes']},
+pred_scores: {decoded_list['pred_score'].sigmoid()}
+")
 ```
 
 ## Models
@@ -174,6 +186,64 @@ Here are some randomly selected test examples showcasing PaDT’s excellent perf
 <img src="./assets/TAM.webp" width="900"/>
 </div>
 
+## Training Instruction
+
+Download Datasets:
+
+- [COCO](https://cocodataset.org/#home)
+
+- RefCOCO/+/g
+    ```bash
+    wget https://web.archive.org/web/20220413011718/https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
+    wget https://web.archive.org/web/20220413011656/https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
+    wget https://web.archive.org/web/20220413012904/https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip
+    ```
+
+Unpack these datasets and place them under the following directory:
+
+```
+PaDT/
+ ├── dataset/
+ │    ├── coco/
+ │    │     ├── annotations/
+ │    │     ├── train2014/
+ │    │     ├── train2017/
+ │    │     ├── val2014/
+ │    │     └── val2017/
+ │    └── RefCOCO/
+ │          ├── refcoco/
+ │          ├── refcoco+/
+ │          └── refcocog/
+```
+
+Preprocess the datasets:
+- 1. Preprocess via our scripts. (Please first update the dataset path configuration in the preprocessing scripts)
+    ```bash
+    cd src/preprocess
+    python process_coco.py
+    python process_refcoco.py
+    ```
+- 2. We also released the preprocessed datasets which are ready to use for training in huggingface.
+
+    | Dataset | Dataset Path | Task Type |
+    | - | - | -|
+    | COCO | [PaDT-MLLM/COCO](https://huggingface.co/datasets/PaDT-MLLM/COCO) | Open Vocabulary Detection |
+    | RefCOCO | [PaDT-MLLM/RefCOCO](https://huggingface.co/datasets/PaDT-MLLM/RefCOCO) | Referring Expression Comprehension/Segmentation |
+    | RIC | [PaDT-MLLM/ReferringImageCaptioning](https://huggingface.co/datasets/PaDT-MLLM/ReferringImageCaptioning) | Referring Image Captioning |
+
+
+The training scripts in `run_scripts` are ready to execute.
+
+For example: Train the PaDT-Pro 3B model on a single node with 8×96 GB GPUs.
+
+```bash
+bash ./run_scripts/padt_pro_3b_sft.sh
+```
+
+## Evaluation
+
+We provide a simple inference example in `eval/test_demo.py`. More evaluation scripts will be added soon.
+
 ## License Agreement
 
 PaDT is licensed under Apache 2.0.
@@ -192,4 +262,4 @@ We kindly encourage citation of our work if you find it useful.
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2510.01954}, 
 }
-```
+```