docs: prepare concise public model card

Remove links to private companion repositories, keep only the project and code links, condense results, and replace machine-local training paths with portable inference metadata.

README.md

@@ -8,90 +8,75 @@ language:
 tags:
   - remote-sensing
   - visual-grounding
+  - horizontal-bounding-box
   - oriented-bounding-box
   - reinforcement-learning
   - qwen3-vl
 ---
 
-**English** | [简体中文](#geobox-r1-中文)
+<div align="center">
 
 # GeoBox-R1
 
-Unified box-level remote sensing visual grounding — one model producing both horizontal (HBB)
-and oriented (OBB) bounding boxes.
+**Curriculum-Guided SFT and Geometric RL for Unified Box-Level Remote Sensing Visual Grounding**
 
-> **GeoBox-R1: Curriculum-Guided SFT and Geometric RL for Unified Box-Level Remote Sensing Visual Grounding**
-> Chenxi Lan\*, Yuchen Wu\*, Minghang Zhou, Tianyu Li, Zhihao Qiu, Guoqing Wang
-> Under review at AAAI 2027. (\* equal contribution)
->
-> [Project page](https://yuchenwu73.github.io/GeoBox-R1/) ·
-> [Code](https://github.com/yuchenwu73/GeoBox-R1) ·
-> [Training data](https://huggingface.co/datasets/yuchenwu73/GeoBox-R1-Data) ·
-> [Stage-1 SFT checkpoint](https://huggingface.co/yuchenwu73/GeoBox-R1-SFT)
+Chenxi Lan\*, Yuchen Wu\*, Minghang Zhou, Tianyu Li, Zhihao Qiu, Guoqing Wang<sup>†</sup>
 
-This repository holds the **final model** — merged weights after Stage-1 SFT and Stage-2 GDPO.
-For the first stage alone, use [`GeoBox-R1-SFT`](https://huggingface.co/yuchenwu73/GeoBox-R1-SFT).
+<sup>\*</sup>Equal contribution &nbsp;&nbsp; <sup>†</sup>Corresponding author
 
-## Training
+*Under review at AAAI 2027*
 
-Built on **Qwen3-VL-4B-Instruct** in two stages:
+[Project page](https://yuchenwu73.github.io/geobox-r1/) · [Code](https://github.com/yuchenwu73/GeoBox-R1)
 
-1. **Curriculum-guided SFT** — training data ordered easy-to-hard: HBB → OBB → HBB-to-OBB CoT.
-   LoRA (rank 16, alpha 32), vision encoder and merger frozen, lr `1e-4`, 1 epoch, 2× RTX 4090.
-2. **Geometric RL (GDPO)** — refines OBB prediction on top of the SFT checkpoint with two
-   rule-based geometric rewards: Rotated IoU and an adaptive Wasserstein distance (λ = 0.5 each).
-   G = 8 rollouts, β = 0.02, τ_c = 8, lr `5e-6`, 1 epoch, 3× A100 40G (one vLLM rollout server
-   and two GDPO workers).
+</div>
 
-## Results
-
-Best macro averages on all three metrics, across 7 HBB and 3 OBB evaluation sets.
+## Overview
 
-### HBB (7 evaluation sets, Acc@0.5)
+GeoBox-R1 is a 4B vision-language model for unified remote-sensing visual grounding. Given an
+aerial or satellite image and a referring expression, the same model can produce either a
+horizontal bounding box (HBB) or an oriented bounding box (OBB).
 
-| Model | Params | DIOR-Test | DIOR-Val | RSVG-Test | RSVG-Val | GeoChat* | VRSBench* | AVVG | **Avg.** |
-| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| Qwen3-VL | 8B | 54.14 | 53.68 | 33.82 | 34.80 | 50.73 | 53.55 | 25.22 | 43.70 |
-| GeoGround | 7B | **77.70** | **77.13** | 26.65 | 27.81 | **69.76** | 65.76 | 21.60 | 52.35 |
-| InternVL3 (SFT) | 8B | 74.83 | 75.06 | 46.05 | 44.05 | 59.23 | 58.05 | 28.41 | 55.10 |
-| GeoBox-R1 (SFT) | 4B | 74.91 | 74.21 | 48.57 | 46.29 | 61.39 | 64.45 | 30.33 | 57.17 |
-| **GeoBox-R1** | **4B** | 76.61 | 75.11 | **51.26** | **48.38** | 61.13 | **66.84** | **32.14** | **58.78** |
+The model starts from Qwen3-VL-4B-Instruct and is trained in two stages:
 
-Acc@0.7 and mIoU macro averages are **42.22** and **50.39**, also the best.
+1. **Curriculum-guided SFT** orders examples from HBB grounding to OBB grounding and then
+   HBB-to-OBB chain-of-thought reasoning.
+2. **Geometric RL (GDPO)** improves geometric precision with rotated-IoU and adaptive
+   Wasserstein rewards, without a learned reward model.
 
-### OBB (3 evaluation sets, Acc@0.5 / Acc@0.7 / mRIoU)
+## Results
 
-| Model | Params | GeoChat* | VRSBench* | AVVG | **Avg.** |
-| --- | --- | --- | --- | --- | --- |
-| InternVL3 (SFT) | 8B | 49.79 / 23.10 / 41.66 | 42.01 / 20.65 / 40.68 | 15.43 / 7.40 / 15.39 | 35.74 / 17.05 / 32.58 |
-| GeoGround | 7B | 58.72 / 25.49 / 46.89 | 53.26 / 29.82 / 48.35 | 13.89 / 4.10 / 15.64 | 41.96 / 19.81 / 36.96 |
-| GeoBox-R1 (SFT) | 4B | 55.96 / 30.45 / 45.45 | 51.14 / 27.69 / 45.91 | 22.23 / 15.07 / 19.20 | 43.11 / 24.40 / 36.85 |
-| **GeoBox-R1** | **4B** | **60.56 / 35.19 / 48.92** | **56.61 / 30.55 / 49.43** | **24.79 / 16.89 / 21.18** | **47.32 / 27.55 / 39.85** |
+Macro averages are shown below. Full comparisons, per-dataset results, and the evaluation
+protocol are available on the [project page](https://yuchenwu73.github.io/geobox-r1/).
 
-At 4B parameters this beats the 7B–8B state of the art by **3.68/4.89/2.61** points on HBB and
-**5.36/7.74/2.89** on OBB, with the largest gains at the stricter Acc@0.7 threshold.
+| Task | Evaluation sets | Acc@0.5 | Acc@0.7 | mIoU / mRIoU |
+| --- | :-: | :-: | :-: | :-: |
+| HBB | 7 | **58.78** | **42.22** | **50.39** |
+| OBB | 3 | **47.32** | **27.55** | **39.85** |
 
-**OBB-only RL does not cost HBB accuracy.** GDPO trains on OBB samples alone, yet the HBB macro
-average rises from 57.17/40.33/48.82 to 58.78/42.22/50.39 — Acc@0.5 improves on 6 of 7 sets,
-and Acc@0.7 and mIoU improve on all 7.
+Among the evaluated baselines, GeoBox-R1 achieves the best macro averages while using 4B
+parameters. GDPO is trained only on OBB samples, but it also improves HBB performance over the
+SFT stage.
 
 ## Usage
 
-The prompts below are **byte-for-byte identical to the ones used in training and evaluation**,
-including the spaces inside the coordinate lists. Changing the spacing changes tokenization.
+Install a recent Transformers release together with PyTorch, Pillow, and Accelerate, then run:
 
 ````python
-from transformers import AutoModelForImageTextToText, AutoProcessor
 from PIL import Image
+from transformers import AutoModelForImageTextToText, AutoProcessor
 
 model_id = "yuchenwu73/GeoBox-R1"
-model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")
+
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    dtype="auto",
+    device_map="auto",
+)
 processor = AutoProcessor.from_pretrained(model_id)
 
-image = Image.open("scene.png")
-expression = "the brown suv on the right"
+image = Image.open("scene.png").convert("RGB")
+expression = "the brown SUV on the right"
 
-# Oriented box (OBB)
 prompt = f"""Locate the instance that matches the description: [{expression}]. Report oriented bbox coordinates in following JSON format:
 ```json
 [
@@ -99,14 +84,28 @@ prompt = f"""Locate the instance that matches the description: [{expression}]. R
 ]
 ```"""
 
-messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt}]}]
-text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": prompt},
+        ],
+    }
+]
+text = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
 inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
-out = model.generate(**inputs, max_new_tokens=256)
-print(processor.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+
+generated = model.generate(**inputs, max_new_tokens=256)
+generated = generated[:, inputs.input_ids.shape[1]:]
+print(processor.batch_decode(generated, skip_special_tokens=True)[0])
 ````
 
-For horizontal boxes, use the same call with:
+For HBB grounding, replace the prompt with:
 
 ````python
 prompt = f"""Locate the instance that matches the description: [{expression}]. Report horizontal bbox coordinates in following JSON format:
@@ -117,64 +116,36 @@ prompt = f"""Locate the instance that matches the description: [{expression}]. R
 ```"""
 ````
 
-Coordinates are quantized to `[0, 1000]`; scale by image width and height to recover pixels.
-
-The model can also be served with [ms-swift](https://github.com/modelscope/ms-swift), the
-framework used for training.
-
-## Citation
-
-```bibtex
-@inproceedings{geoboxr1,
-  title     = {GeoBox-R1: Curriculum-Guided SFT and Geometric RL for
-               Unified Box-Level Remote Sensing Visual Grounding},
-  author    = {Lan, Chenxi and Wu, Yuchen and Zhou, Minghang and
-               Li, Tianyu and Qiu, Zhihao and Wang, Guoqing},
-  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
-  year      = {2027},
-  note      = {Under review}
-}
-```
-
----
-
-# GeoBox-R1 中文
-
-[English](#geobox-r1) | **简体中文**
-
-统一边界框级遥感视觉定位模型，同时输出水平框（HBB）与旋转框（OBB）。
-
-本仓库是**最终模型**（Stage-1 SFT + Stage-2 GDPO 后的合并权重）。
-只需要第一阶段结果请用 [`GeoBox-R1-SFT`](https://huggingface.co/yuchenwu73/GeoBox-R1-SFT)。
+Coordinates are quantized to `[0, 1000]`. Multiply x coordinates by the image width divided by
+1000, and y coordinates by the image height divided by 1000, to recover pixel coordinates.
 
-## 训练流程
+The repository also provides evaluation scripts, an interactive demo, and the complete training
+pipeline: [github.com/yuchenwu73/GeoBox-R1](https://github.com/yuchenwu73/GeoBox-R1).
 
-基座 **Qwen3-VL-4B-Instruct**，两阶段：
+## Limitations
 
-1. **课程式 SFT** — 训练数据按 HBB → OBB → HBB-to-OBB CoT 由易到难排列。
-   LoRA（rank 16，alpha 32），冻结视觉编码器与 merger，lr `1e-4`，1 epoch，2× RTX 4090。
-2. **几何强化学习（GDPO）** — 在 SFT 检查点上用两个基于规则的几何奖励细化 OBB：
-   Rotated IoU 与自适应 Wasserstein 距离（λ 各 0.5）。
-   G=8 rollouts，β=0.02，τ_c=8，lr `5e-6`，1 epoch，3× A100 40G（1 个 vLLM rollout 服务 + 2 个 GDPO worker）。
+- The model is designed for single-object visual grounding in remote-sensing imagery; it is not
+  a general-purpose detector.
+- Predictions are generated as text and may occasionally be malformed or refer to the wrong
+  object, especially for ambiguous expressions or very small targets.
+- Reported results follow the benchmark splits and protocols described on the project page and
+  may not transfer directly to other imagery domains.
 
-## 结果
+## License
 
-7 个 HBB 与 3 个 OBB 评测集，三项指标的宏平均均取得最佳：
+The model weights are released under the **CC BY-NC 4.0** license. Users must also comply with
+the licenses and terms of the underlying Qwen3-VL model and any input datasets they use.
 
-| 任务 | Acc@0.5 | Acc@0.7 | mIoU / mRIoU |
-| --- | --- | --- | --- |
-| HBB（7 个集） | **58.78** | **42.22** | **50.39** |
-| OBB（3 个集） | **47.32** | **27.55** | **39.85** |
-
-以 4B 参数超过 7B–8B 的现有最优模型：HBB 领先 **3.68/4.89/2.61** 点，
-OBB 领先 **5.36/7.74/2.89** 点，在更严格的 Acc@0.7 上增益最大。
-逐数据集的完整结果见上方英文表格或[项目主页](https://yuchenwu73.github.io/GeoBox-R1/)。
-
-**OBB-only RL 不牺牲 HBB**：GDPO 只用 OBB 样本训练，HBB 宏平均反而从 57.17/40.33/48.82
-升到 58.78/42.22/50.39 —— Acc@0.5 在 7 个集合中的 6 个上升，Acc@0.7 与 mIoU 全部 7 个上升。
-
-## 使用
+## Citation
 
-代码见上方英文 [Usage](#usage) 一节。提示词与训练、评测时**逐字节一致**，
-包括坐标列表里的空格 —— 改动空格会改变分词结果。坐标量化到 `[0, 1000]`，
-按图像宽高缩放即可还原到像素。
+```bibtex
+@misc{geoboxr1,
+  title  = {GeoBox-R1: Curriculum-Guided SFT and Geometric RL for
+            Unified Box-Level Remote Sensing Visual Grounding},
+  author = {Lan, Chenxi and Wu, Yuchen and Zhou, Minghang and
+            Li, Tianyu and Qiu, Zhihao and Wang, Guoqing},
+  year   = {2026},
+  url    = {https://yuchenwu73.github.io/geobox-r1/},
+  note   = {Preprint}
+}
+```

args.json

@@ -1,535 +1,9 @@
 {
-  "output_dir": "/data2/longfeiqi/sutando/MLLM4RSVG/output/GRPO/v64-20260329-180422",
-  "overwrite_output_dir": false,
-  "do_train": false,
-  "do_eval": false,
-  "do_predict": false,
-  "eval_strategy": "no",
-  "prediction_loss_only": false,
-  "per_device_train_batch_size": 8,
-  "per_device_eval_batch_size": 1,
-  "per_gpu_train_batch_size": null,
-  "per_gpu_eval_batch_size": null,
-  "gradient_accumulation_steps": 4,
-  "eval_accumulation_steps": null,
-  "eval_delay": 0,
-  "torch_empty_cache_steps": null,
-  "learning_rate": 5e-06,
-  "weight_decay": 0.1,
-  "adam_beta1": 0.9,
-  "adam_beta2": 0.95,
-  "adam_epsilon": 1e-08,
-  "max_grad_norm": 1.0,
-  "num_train_epochs": 1.0,
-  "max_steps": -1,
-  "lr_scheduler_type": "cosine",
-  "lr_scheduler_kwargs": null,
-  "warmup_ratio": 0.05,
-  "warmup_steps": 0,
-  "log_level": "passive",
-  "log_level_replica": "warning",
-  "log_on_each_node": true,
-  "logging_dir": "/data2/longfeiqi/sutando/MLLM4RSVG/output/GRPO/v64-20260329-180422/runs",
-  "logging_strategy": "steps",
-  "logging_first_step": true,
-  "logging_steps": 1,
-  "logging_nan_inf_filter": true,
-  "save_strategy": "steps",
-  "save_steps": 100.0,
-  "save_total_limit": 3,
-  "save_safetensors": true,
-  "save_on_each_node": false,
-  "save_only_model": false,
-  "restore_callback_states_from_checkpoint": false,
-  "no_cuda": false,
-  "use_cpu": false,
-  "use_mps_device": false,
-  "seed": 42,
-  "data_seed": 42,
-  "jit_mode_eval": false,
-  "bf16": true,
-  "fp16": false,
-  "fp16_opt_level": "O1",
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-  "fp16_full_eval": false,
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-  "local_rank": 0,
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-  "eval_steps": 100.0,
-  "dataloader_num_workers": 4,
-  "dataloader_prefetch_factor": null,
-  "past_index": -1,
-  "run_name": "/data2/longfeiqi/sutando/MLLM4RSVG/output/GRPO/v64-20260329-180422",
-  "disable_tqdm": null,
-  "remove_unused_columns": false,
-  "label_names": null,
-  "load_best_model_at_end": false,
-  "metric_for_best_model": "loss",
-  "greater_is_better": false,
-  "ignore_data_skip": false,
-  "fsdp": [],
-  "fsdp_min_num_params": 0,
-  "fsdp_config": null,
-  "fsdp_transformer_layer_cls_to_wrap": null,
-  "accelerator_config": {
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-  },
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-  "deepspeed": {
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-      "allgather_bucket_size": 200000000.0,
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-      "reduce_bucket_size": 200000000.0,
-      "contiguous_gradients": true
-    },
-    "gradient_accumulation_steps": "auto",
-    "gradient_clipping": "auto",
-    "steps_per_print": 2000,
-    "train_batch_size": "auto",
-    "train_micro_batch_size_per_gpu": "auto",
-    "wall_clock_breakdown": false
-  },
-  "label_smoothing_factor": 0.0,
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-  "adafactor": false,
-  "group_by_length": false,
-  "length_column_name": "length",
-  "report_to": [
-    "swanlab"
-  ],
-  "project": "huggingface",
-  "trackio_space_id": "trackio",
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-  "ray_scope": "last",
-  "ddp_timeout": 18000000,
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-  "include_tokens_per_second": false,
-  "include_num_input_tokens_seen": false,
-  "neftune_noise_alpha": null,
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-  "eval_use_gather_object": false,
-  "average_tokens_across_devices": true,
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-  "tuner_backend": "peft",
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-  "enable_dft_loss": false,
-  "enable_channel_loss": false,
-  "check_model": true,
-  "acc_strategy": "token",
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-  "max_epochs": null,
-  "aligner_lr": null,
-  "vit_lr": null,
-  "use_logits_to_keep": null,
-  "ds3_gather_for_generation": true,
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-  "loss_type": "grpo",
-  "eval_metric": null,
-  "callbacks": [],
-  "early_stop_interval": null,
-  "eval_use_evalscope": false,
-  "eval_dataset": [],
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-  "eval_limit": null,
-  "eval_generation_config": null,
-  "extra_eval_args": null,
-  "tuner_type": "lora",
-  "use_galore": false,
-  "galore_target_modules": null,
-  "galore_rank": 128,
-  "galore_update_proj_gap": 50,
-  "galore_scale": 1.0,
-  "galore_proj_type": "std",
-  "galore_optim_per_parameter": false,
-  "galore_with_embedding": false,
-  "galore_quantization": false,
-  "galore_proj_quant": false,
-  "galore_proj_bits": 4,
-  "galore_proj_group_size": 256,
-  "galore_cos_threshold": 0.4,
-  "galore_gamma_proj": 2,
-  "galore_queue_size": 5,
-  "lisa_activated_layers": 0,
-  "lisa_step_interval": 20,
-  "use_flash_ckpt": false,
-  "use_ray": false,
-  "ray_exp_name": null,
-  "device_groups": null,
-  "model": "../checkpoints/checkpoint-sft_no-joint_cot",
+  "swift_version": "4.0.0.dev0",
   "model_type": "qwen3_vl",
-  "model_revision": null,
-  "task_type": "causal_lm",
-  "torch_dtype": "bfloat16",
-  "attn_impl": "flash_attention_2",
-  "experts_impl": null,
-  "new_special_tokens": [],
-  "num_labels": null,
-  "problem_type": null,
-  "rope_scaling": null,
-  "device_map": null,
-  "max_memory": {},
-  "max_model_len": null,
-  "local_repo_path": null,
-  "init_strategy": null,
   "template": "qwen3_vl",
-  "system": null,
-  "max_length": 2048,
-  "truncation_strategy": "left",
-  "max_pixels": null,
-  "agent_template": null,
-  "norm_bbox": null,
-  "use_chat_template": true,
-  "padding_side": "right",
-  "padding_free": false,
-  "loss_scale": "last_round",
-  "sequence_parallel_size": 1,
-  "template_backend": "swift",
-  "response_prefix": null,
-  "enable_thinking": null,
-  "add_non_thinking_prefix": true,
-  "dataset": [
-    "../refGeo*/RL/rl_obb_train_20.0%.jsonl"
-  ],
-  "val_dataset": [],
-  "cached_dataset": [],
-  "cached_val_dataset": [],
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eval_dataset_args=None, eval_limit=None, eval_generation_config=None, extra_eval_args=None, tuner_type='lora', use_galore=False, galore_target_modules=None, galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False, vllm_pipeline_parallel_size=1, vllm_enable_expert_parallel=False, vllm_max_num_seqs=None, vllm_max_model_len=None, vllm_disable_custom_all_reduce=True, vllm_enforce_eager=False, vllm_limit_mm_per_prompt=None, vllm_max_lora_rank=16, vllm_enable_prefix_caching=True, vllm_use_async_engine=None, vllm_quantization=None, vllm_reasoning_parser=None, vllm_disable_cascade_attn=False, vllm_mm_processor_cache_gb=None, vllm_speculative_config=None, vllm_engine_kwargs={}, vllm_data_parallel_size=1, stop_words=[], vllm_enable_lora=False, lora_rank=16, vllm_server_group_port=[51226], enable_flattened_weight_sync=True, async_generate=False, structured_outputs_regex=None, sleep_level=0, move_model_batches=None, offload_optimizer=False, offload_model=False, cosine_min_len_value_wrong=-0.5, cosine_max_len_value_wrong=0.0, cosine_min_len_value_correct=1.0, cosine_max_len_value_correct=0.5, cosine_max_len=256, repetition_n_grams=3, repetition_max_penalty=-1.0, reward_model=None, reward_model_plugin=None, chord_sft_dataset=[], chord_sft_per_device_train_batch_size=None, chord_enable_phi_function=False, chord_mu_warmup_steps=None, chord_mu_decay_steps=None, chord_mu_peak=None, chord_mu_valley=None, multi_turn_scheduler=None, max_turns=None, completion_length_limit_scope='per_round', vllm_server_pass_dataset=False, dynamic_sample=False, max_resample_times=3, overlong_filter=False, soft_max_length=None, soft_cache_length=None, log_entropy=False, tau_pos=1.0, tau_neg=1.05, advantage_estimator='grpo', kl_in_reward=False, num_generations_eval=None, dataset_shuffle=True, rollout_importance_sampling_mode=None, rollout_importance_sampling_threshold=2.0, log_rollout_offpolicy_metrics=False, off_policy_sequence_mask_delta=None)"
+  "norm_bbox": "norm1000",
+  "task_type": "causal_lm",
+  "tuner_type": "full",
+  "torch_dtype": "bfloat16"
 }