UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface
Paper • 2503.01342 • Published • 8
How to use kanashi6/UFO with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="kanashi6/UFO") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kanashi6/UFO", dtype="auto")How to use kanashi6/UFO with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kanashi6/UFO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "kanashi6/UFO",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/kanashi6/UFO
How to use kanashi6/UFO with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "kanashi6/UFO" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "kanashi6/UFO",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "kanashi6/UFO" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "kanashi6/UFO",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use kanashi6/UFO with Docker Model Runner:
docker model run hf.co/kanashi6/UFO
This repository contains the model presented in the paper UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface.
UFO unifies object-level detection, pixel-level segmentation, and image-level vision-language tasks into a single model by transforming all perception targets into the language space. It introduces a novel embedding retrieval approach that relies solely on the language interface to support segmentation tasks.
For more details, please refer to the original paper and the GitHub repository: