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Φ Pheye - a family of efficient small vision-language models

  • These models train a fraction of the number of parameters other models of similar sizes train
  • Are more efficient in how they incorporate vision into language tasks (dense cross-attention > LLaVA style architecture)
  • Are more efficient in how the process high resolution input images
  • Use less data to train yet achieve competitive results (if you want to redo this recipe using more data results will be even better)


Model Resolution Trainable Params Data VQAv2 Nocaps TextVQA TextCaps
MoE-LLaVA-1.6B×4 336 2.9B 5.9M 76.0 - 47.8 -
MoE-LLaVA-2.7B×4 336 5.3B 5.9M 77.1 - 50.2 -
moondream1 384 1.86B 3.9M 74.7 - 35.6
moondream2 384 1.86B - 77.7 92.5 49.7 120.2
Pheye-x4 🤗 448 295M 2.9M 75.2 110.1 45.9 106.4
Pheye-x4 🤗 672 295M 2.9M 75.5 110.8 49.2 111.9
Pheye-x2 🤗 448 578M 2.9M 76.0 111.8 47.3 108.9
Pheye-x2 🤗 672 578M 2.9M 76.4 110.5 50.5 115.9


Image Example
How much do these popcorn packets weigh?
These popcorn packets weigh 90g each.

What brand are these popcorn packets?
These popcorn packets are from the brand "Magic Pop".
Can I pet that dog?
No, you cannot pet the dog in the image. It is a black bear, not a dog.
Why is the person wearing protective gear?
The person in the image is wearing protective gear, such as a beekeeper's suit and a helmet, to ensure their safety while working with bees. Beekeeping involves handling bees and their hives, which can be potentially dangerous due to the risk of bee stings. The protective gear helps to minimize the risk of bee stings and other bee-related injuries, allowing the beekeeper to carry out their work safely and effectively.

* Generated by Pheye-x2-672


To generate a sample response from a prompt use generate.py. Use a Python version >= 3.11. Start by cloning the repo and create a virtual environment with the necessary packages:

git clone https://github.com/miguelscarv/pheye.git
cd pheye
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

Then run generate.py:

python3 generate.py --image_path images/dog_flower.jpg --prompt "What is the dog holding in it's mouth?" --device cuda


This implementation was inspired by OpenFlamingo's repository.

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