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raja0007
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Model Details
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raja0007
Model Tree
OpenGVLab/InternVL3_5-1B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on the official held-out test split of 2,000 multi-sensor image pairs across 3 operational sensor modes:
| Metric / Category | Score |
|---|---|
| Soft Token F1 Score | 56.19% (Project Record) |
| Exact Match (EM) | 48.00% (24 / 50) |
| Land Cover Classification | 83.33% (5 / 6) |
| Feature Counting | 80.00% (4 / 5) |
| Presence / Absence Verification | 80.00% (4 / 5) |
| Area Coverage Estimation | 66.67% (4 / 6) |
| Bounding Box Syntax Valid Rate | 100.00% |
| Repetition Loop Rate | 0.00% |
| EOS Termination Rate | 100.00% |
You don't need to write model loading code! Simply download inference.py directly from this repository:
bash
# Download the standalone inference enginecurl -O https://huggingface.co/raja0007/satquery-satellite-vlm/raw/main/inference.py # Query any optical satellite image:python inference.py --image satellite_rgb.png --modality optical --question "What land cover is visible?" # Query SAR radar (Sentinel-1):python inference.py --image radar.png --modality sar --question "Is active flood inundation present?" # Multimodal SAR + Optical analysis:python inference.py --sar sar.png --optical optical.png --question "Is there flooding along the river?" # Interactive chat loop:python inference.py --interactivepython
from inference import SatQueryModel model = SatQueryModel()answer = model.query(image="flood_scene.png", question="Is there active water inundation?", modality="optical")print("SatQuery:", answer)bash
pip install torch torchvision transformers peft bitsandbytes tifffile pillowpython
import torchfrom transformers import AutoModel, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelfrom torchvision import transformsfrom PIL import Image # 1. Load Base Architecture (4-bit NF4 quantized for low VRAM)base_repo = "OpenGVLab/InternVL3_5-1B-Instruct"adapter_repo = "raja0007/satquery-satellite-vlm" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,) tokenizer = AutoTokenizer.from_pretrained(base_repo, trust_remote_code=True)base_model = AutoModel.from_pretrained( base_repo, quantization_config=bnb_config, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto",) # Connect fine-tuned satellite adaptermodel = PeftModel.from_pretrained(base_model, adapter_repo)model.eval() # Vision dtypevision_dtype = next(model.base_model.model.vision_model.parameters()).dtypepython
# Transform for input imagestransform = transforms.Compose([ transforms.Resize((448, 448)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),]) # Load your Sentinel-1 SAR and Sentinel-2 Optical images (RGB PIL Images)sar_image = Image.open("path_to_sar_image.png").convert("RGB")opt_image = Image.open("path_to_optical_image.png").convert("RGB") t_s1 = transform(sar_image).unsqueeze(0).to(model.device, dtype=vision_dtype)t_s2 = transform(opt_image).unsqueeze(0).to(model.device, dtype=vision_dtype)pixel_values = torch.cat([t_s1, t_s2], dim=0) question = "Is there floodwater or inundation present in this scene?" # Structured multimodal promptprompt = ( f"<MODALITY: Sentinel-1 SAR Radar>\n<image>\n" f"<MODALITY: Sentinel-2 Optical>\n<image>\n" f"Question: {question}\nAnswer:") with torch.no_grad(): response = model.chat( tokenizer=tokenizer, pixel_values=pixel_values, question=prompt, num_patches_list=[1, 1], generation_config=dict(max_new_tokens=64, do_sample=False, temperature=0.0), ) print("SatQuery Answer:", response)OpenGVLab/InternVL3_5-1B-Instructq_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_projFine-tuned by Raja as part of the SatQuery AI initiative for multi-modal Earth Observation reasoning.
Based on the InternVL-3.5 architecture by OpenGVLab.