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

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}, 
 }
-```
+```