Sky-VLM: Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation
This repository hosts the Sky-VLM model, a specialized Vision-Language Model designed for UAV spatial reasoning across multiple granularities and contexts. It was introduced in the paper Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation.
The project's code is available on GitHub: https://github.com/linglingxiansen/SpatialSKy.
🚀 Sample Usage
First, install the transformers library and other dependencies as described in the GitHub repository:
pip install git+https://github.com/huggingface/transformers accelerate torch torchvision openai pillow tqdm nltk scipy
Then, you can use the following Python code for inference with the Sky-VLM model:
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info # Note: qwen_vl_utils might need to be installed separately or adapted
# Default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"llxs/Sky-VLM", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("llxs/Sky-VLM")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "./examples/images/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", # Placeholder image path
},
{"type": "text", "text": "In this UI screenshot, what is the position of the element corresponding to the command \\\"switch language of current page\\\" (with bbox)?"},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
# Assuming process_vision_info is available from qwen_vl_utils or a similar helper
# For a minimal example, image_inputs can be directly a list of PIL Images or similar
# If qwen_vl_utils is not installed, manual processing might be needed.
# For simplicity, if this exact helper isn't critical for basic HF inference, we might skip/adapt.
# Here, we assume its presence for direct copy.
image_inputs, video_inputs = process_vision_info(messages) # Requires qwen_vl_utils for this exact function
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
print(output_text)
# Expected output example: <|object_ref_start|>language switch<|object_ref_end|><|box_start|>(576,12),(592,42)<|box_end|><|im_end|>