| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| base_model: Qwen/Qwen3-VL-8B-Instruct |
| base_model_relation: finetune |
| tags: |
| - qwen3_vl |
| - vision-language |
| - remote-sensing |
| - visual-question-answering |
| - geospatial |
| - uhr-bat2 |
| model-index: |
| - name: UHR-BAT2 |
| results: |
| - task: |
| type: visual-question-answering |
| dataset: |
| type: RSHR-Bench |
| name: RSHR-Bench (all 1932 QA records) |
| metrics: |
| - type: accuracy |
| name: Accuracy (%) |
| value: 50.72463768115942 |
| - task: |
| type: visual-question-answering |
| dataset: |
| type: XLRS-Bench-lite |
| name: XLRS-Bench-lite (strict option-set matching) |
| metrics: |
| - type: accuracy |
| name: Accuracy (%) |
| value: 52.27272727272727 |
| - task: |
| type: visual-question-answering |
| dataset: |
| type: MME-RealWorld |
| name: MME-RealWorld (full Remote Sensing subset) |
| metrics: |
| - type: accuracy |
| name: Accuracy (%) |
| value: 56.79507758159444 |
| --- |
| |
| # UHR-BAT2 |
|
|
| [English](README.md) | [简体中文](README_zh.md) |
|
|
| [Hugging Face](https://huggingface.co/RL-MIND/UHR-BAT2) · [ModelScope](https://www.modelscope.cn/models/KAIWANG/UHR-BAT2) · [Evaluation details](EVALUATION.md) |
|
|
| **A Qwen3-VL-8B model fine-tuned for remote-sensing visual question answering.** |
|
|
| UHR-BAT2 is a full-weight fine-tune of [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct), trained on a selected 10,000-example GeoLLaVA manifest. It keeps the native Qwen3-VL architecture and can be loaded with Transformers. The released checkpoint is `qwen3vl-8b-geollava-ft3-selected-10k`. |
|
|
| ## Main results |
|
|
| These are complete, unpruned model evaluations, audited against existing prediction records on **2026-10-13**. The reported results use the corrected option-scoring protocol. |
|
|
| | Dataset | Accuracy | Correct / Total | Metric | |
| |---|---:|---:|---| |
| | **RSHR-Bench** | **50.72%** | 980 / 1,932 | All-QA option accuracy | |
| | **XLRS-Bench-lite** | **52.27%** | 1,610 / 3,080 | Strict option-set accuracy | |
| | **MME-RealWorld Remote Sensing** | **56.80%** | 2,123 / 3,738 | Full remote-sensing subset accuracy | |
|
|
| RSHR's single-turn subset is **45.66% (726/1,590)**. XLRS includes **52.45% (1,563/2,980)** on single-choice questions and **47.00% (47/100)** on multi-select tasks. MME uses the full 3,738-record remote-sensing subset, not its 150-record Lite subset. |
|
|
| The evaluated image budget is `min_pixels=784`, `max_pixels=1,310,720` per image. Images are resized within this budget. Single-choice evaluation uses FP32 candidate-option logits; XLRS multi-select evaluation uses constrained decoding and exact option-set matching. Ordinary free generation is not the same scoring procedure. See [EVALUATION.md](EVALUATION.md) and [evaluation_results.json](evaluation_results.json) for categories, denominators and protocol details. |
|
|
| ## Model and training |
|
|
| | Item | Value | |
| |---|---| |
| | Base model | Qwen/Qwen3-VL-8B-Instruct | |
| | Architecture | Qwen3VLForConditionalGeneration | |
| | Training data | GeoLLaVA, selected 10K examples | |
| | Fine-tuning | Frozen vision encoder; train merger and LLM | |
| | Checkpoint format | Full BF16 safetensors, 4 shards | |
| | Training completion | 1 epoch, 625 optimizer steps | |
| | Launch configuration | Adafactor; learning rate 1e-5; global batch 16; maximum sequence length 8,192 | |
| | Release scope | Dense checkpoint and inference example | |
|
|
| The checkpoint's trainer state confirms one completed epoch and 625 steps. Other training settings above are recorded launcher defaults. This is the final training checkpoint; no validation-selected best checkpoint was recorded. The original [UHR-BAT](https://huggingface.co/RL-MIND/UHR-BAT) uses a different architecture; its custom compression modules and results are not part of this Qwen3-VL weight release. |
|
|
| ## Quick start |
|
|
| Use Python 3.10 or later and a CUDA-enabled PyTorch installation. A single image can be processed with the standard Qwen3-VL implementation; custom remote code is not required. GPU memory requirements depend on image size and output length. |
|
|
| ```bash |
| pip install -r requirements.txt |
| python inference.py --model RL-MIND/UHR-BAT2 \ |
| --image /path/to/remote_sensing_image.jpg \ |
| --question "Describe the land use and visible objects in this image." |
| ``` |
|
|
| ```python |
| import torch |
| from PIL import Image |
| from transformers import AutoProcessor, Qwen3VLForConditionalGeneration |
| |
| model_id = "RL-MIND/UHR-BAT2" |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = Qwen3VLForConditionalGeneration.from_pretrained( |
| model_id, dtype="auto", device_map="auto", attn_implementation="sdpa" |
| ).eval() |
| |
| image = Image.open("remote_sensing_image.jpg").convert("RGB") |
| messages = [{"role": "user", "content": [ |
| {"type": "image"}, |
| {"type": "text", "text": "Describe this remote-sensing image."}, |
| ]}] |
| prompt = processor.apply_chat_template( |
| messages, tokenize=False, add_generation_prompt=True |
| ) |
| inputs = processor(text=[prompt], images=[image], return_tensors="pt") |
| inputs.pop("token_type_ids", None) |
| inputs = inputs.to(model.device) |
| with torch.inference_mode(): |
| output = model.generate( |
| **inputs, max_new_tokens=128, do_sample=False, |
| temperature=None, top_p=None, top_k=None, use_cache=True, |
| ) |
| answer = output[:, inputs["input_ids"].shape[1]:] |
| print(processor.batch_decode(answer, skip_special_tokens=True)[0]) |
| ``` |
|
|
| For ModelScope, download the same package and pass the local directory to the example above: |
|
|
| ```bash |
| pip install modelscope |
| ``` |
|
|
| ```python |
| from modelscope import snapshot_download |
| model_id = snapshot_download("KAIWANG/UHR-BAT2") |
| # Use model_id with AutoProcessor and Qwen3VLForConditionalGeneration above. |
| ``` |
|
|
| The example demonstrates image-conditioned generation. It does not reproduce the option-scoring evaluation table by itself. |
|
|
| The release passed a native loading and short image-generation smoke test with Python 3.11.0, PyTorch 2.5.1+cu121, Transformers 4.57.0.dev0 and Accelerate 1.13.0. The test checks package loading and generation, not benchmark accuracy or inference speed. File hashes and tensor metadata are in [release_manifest.json](release_manifest.json). |
|
|
| ## Limitations |
|
|
| Counting and small-object localization remain challenging. These resized-input evaluations do not establish native full-resolution ultra-high-resolution performance. A matched evaluation of the unfine-tuned base model is not included, so this release makes no controlled fine-tuning improvement claim. A training-data audit found candidate scene-name/tile matches with RSHR; pixel-level and question-level overlap have not been resolved, so geographic-disjoint generalization is not established. |
|
|
| ## License and acknowledgments |
|
|
| Released under [Apache-2.0](LICENSE), following the base model's license. See [NOTICE](NOTICE) for the upstream attribution and modifications. Training and benchmark datasets are not redistributed; their own terms apply. We acknowledge the Qwen team, GeoLLaVA contributors and the benchmark authors. |
|
|
