Add pipeline tag and include Github README content

This PR adds the `pipeline_tag: image-text-to-text` to the model card metadata. It also includes the Github README content into the model card, improving the documentation and discoverability of the model.

README.md

@@ -1,19 +1,173 @@
 ---
+base_model:
+- Qwen/Qwen2-VL-7B-Instruct
+datasets:
+- JosephZ/vg150_train_sgg_prompt
 library_name: transformers
+license: apache-2.0
+metrics:
+- recall
 tags:
 - image
 - scene-graph
 - scene-graph-generation
-license: apache-2.0
-datasets:
-- JosephZ/vg150_train_sgg_prompt
-metrics:
-- recall
-base_model:
-- Qwen/Qwen2-VL-7B-Instruct
+pipeline_tag: image-text-to-text
 ---
 
 # Model Description
 
 <!-- Provide a quick summary of what the model is/does. -->
-An end-to-end multimodal LLM for Scene Graph Generation (SGG), which was introduced in [Compile Scene Graphs with Reinforcement Learning](https://huggingface.co/papers/2504.13617
+An end-to-end multimodal LLM for Scene Graph Generation (SGG), which was introduced in [Compile Scene Graphs with Reinforcement Learning](https://huggingface.co/papers/2504.13617)
+
+# R1-SGG: Compile Scene Graphs with Reinforcement Learning
+
+## **Structured Visual Reasoning with Multimodal LLMs and Reinforcement Learning**  
+[![Paper](https://img.shields.io/badge/arXiv-2504.13617-b31b1b.svg)](https://arxiv.org/abs/2504.13617)  [![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](LICENSE) [![Hugging Face](https://img.shields.io/badge/HuggingFace-Demo-orange?logo=huggingface)](https://huggingface.co/spaces/JosephZ/R1-SGG) 
+---
+
+## 🚀 Update
+- ✅ ![Hugging Face](https://img.shields.io/badge/HuggingFace-Model-orange?logo=huggingface)[R1-SGG-7B](https://huggingface.co/JosephZ/R1-SGG-7B), [R1-SGG-Zero-7B](https://huggingface.co/JosephZ/R1-SGG-Zero-7B)
+- ✅ Support [PSG](https://github.com/Jingkang50/OpenPSG) dataset (bbox format only, not Panoptic)
+- ✅ Updated loss implementation
+- ✅ Always use `custom_per_device_train_batch_size` instead of `per_device_train_batch_size` for faster sampling under gradient accumulation
+- ⚠️ Current loss implementation might still be affected by gradient accumulation: [trl issue #3021](https://github.com/huggingface/trl/issues/3021)
+
+---
+
+## 🛠️ Setup Environment
+```bash
+bash install.sh
+```
+Main dependencies:
+```bash
+- torch == 2.5.0 or 2.5.1 (cu124, optional)
+- transformers (supports Qwen2VL, Qwen2.5VL)
+- trl
+- vLLM
+```
+
+---
+
+## 📚 Dataset
+Load preprocessed datasets via:
+```python
+from datasets import load_dataset
+
+db_train = load_dataset("JosephZ/vg150_train_sgg_prompt")["train"]
+db_val = load_dataset("JosephZ/vg150_val_sgg_prompt")["train"]
+```
+or for PSG:
+```python
+db_train = load_dataset("JosephZ/psg_train_sg")["train"]  # keys: image_id, image, objects, relationships
+db_val = load_dataset("JosephZ/psg_test_sg")["train"]
+```
+We transformed VG150 into HuggingFace Datasets format with keys:
+- `image_id`
+- `image`
+- `prompt_open`
+- `prompt_close`
+- `objects`
+- `relationships`
+
+---
+
+## 🔥 Supported Models
+- [x] Qwen/Qwen2-VL-2B-Instruct
+- [x] Qwen/Qwen2-VL-7B-Instruct
+- [x] Qwen/Qwen2.5-VL-3B-Instruct
+- [x] Qwen/Qwen2.5-VL-7B-Instruct
+
+---
+
+## 🏋️‍♂️ Training
+
+### Training with Supervised Fine-Tuning (SFT)
+
+For **SLURM users**:
+```bash
+sbatch scripts/sft/7B_sgg.sh 
+```
+
+For **local machines**:
+```bash
+bash scripts/sft_local/7B_sgg.sh
+```
+⏱️ Approximate training time:
+- 2B models: ~4 hours (4×A100 SXM4 GPUs)
+- 7B models: ~10 hours (4×A100 SXM4 GPUs)
+
+---
+
+### Training with Reinforcement Learning (GRPO)
+** Update (11/05/2025): to use "Hard Recall"**:
+```
+--reward_funcs format_reward edge_hard_reward 
+```
+
+For **A100 GPUs**:
+```bash
+sbatch scripts/grpo/train_a100_2B.sh
+```
+(12 hours on 16×A100 GPUs)
+
+For **GH200 GPUs**:
+```bash
+sbatch scripts/grpo/train_gh200.sh
+```
+(16 hours on 16×GH200 GPUs)
+
+For clusters with many RTX_3090/4090 GPUs:
+```bash
+sbatch scripts/grpo/train_fused.sh
+```
+- Training 7B models on 24GB cards is possible with Zero3, but slow due to communication bottlenecks.
+- (Fun fact: training with 120×RTX_4090 is crazy but severely limited by communication latency.)
+
+💡 **Recommended learning rate**: `6e-7`.
+
+---
+
+## 🧪 Inference and Evaluation
+
+### Inference with SFT-trained models:
+```bash
+bash scripts/inference/run_sgg_inference.sh $DATASET $MODEL_NAME $OUTPUT_DIR
+```
+For models trained **with predefined categories**, add `true`:
+```bash
+bash scripts/inference/run_sgg_inference.sh $DATASET $MODEL_NAME $OUTPUT_DIR true
+```
+
+### Inference with GRPO-trained models:
+```bash
+bash scripts/inference/run_sgg_inference.sh $DATASET $MODEL_NAME $OUTPUT_DIR false/true true
+```
+
+### Evaluation:
+```bash
+DATASET_TYPE=vg # or psg
+python src/sgg_gather_preds.py $DATASET_TYPE $OUTPUT_DIR sgg_pred_results.json
+python src/vg150_eval.py $DATASET sgg_pred_results.json
+```
+
+---
+
+## 🤝 Acknowledgement
+The `GRPOTrainer` used in this project is based on [trl's GRPOTrainer](https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py), extended to support multimodal inputs.
+
+---
+
+## 📖 Citation
+If you find this work helpful, please cite:
+```bibtex
+@article{chen2025compile,
+  title={Compile Scene Graphs with Reinforcement Learning},
+  author={Chen, Zuyao and Wu, Jinlin and Lei, Zhen and Pollefeys, Marc and Chen, Chang Wen},
+  journal={arXiv preprint arXiv:2504.13617},
+  year={2025}
+}
+```
+
+---
+
+# ✨ Happy Compiling!