ComputerVisionAIHub
Custom-trained YOLO models, free to download and run.
Custom-trained YOLO models, free to download and run.
Pulls the image if it's not cached, starts the app, and serves a drag-and-drop UI on localhost:7860. Requires Docker.
docker run -p 7860:7860 --entrypoint python docker.io/lukasiktar/computervisionaihub:latest app.py
Heads up: every model here is trained on a specific dataset, so how well it performs on your own images depends on how closely they match that data — don't expect it to generalize to everything out of the box. Tried a model on your own footage? Send us your results and what you need — it helps us improve the dataset, or train a version suited to your use case.
Everything a fresh Ubuntu machine needs before the commands above will work. Already have Docker (and a GPU driver, if you want one)? You can skip this.
Lets you pull and run the container images on this page. Official instructions (all distros): docs.docker.com/engine/install/ubuntu
sudo apt-get update
sudo apt-get install -y ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc
echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
sudo usermod -aG docker $USER
The last line lets you run docker without sudo — log out and back in (or run newgrp docker) for it to take effect.
The Container Toolkit (next step) needs the GPU driver already installed on the host. The right package depends on your exact GPU and Ubuntu version, so the quickest safe start is Ubuntu's own driver detector:
sudo ubuntu-drivers autoinstall
Reboot afterwards, then check it worked with nvidia-smi. For a specific GPU/driver version, a manual CUDA Toolkit install, or non-Ubuntu setups, use the official resources instead:
Only needed if you have an NVIDIA GPU and want faster inference. Requires the driver from step 2. Official instructions: NVIDIA Container Toolkit install guide
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
Once installed, add --gpus all to any docker run command on this page to use the GPU.