Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.
$ docker login nvcr.io Username: $oauthtoken Password: <PASTE_API_KEY_HERE>
Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.
export NGC_API_KEY=<PASTE_API_KEY_HERE> export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" docker run -it --rm \ --gpus all \ --shm-size=16GB \ -e NGC_API_KEY=$NGC_API_KEY \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \ -u $(id -u) \ -p 8000:8000 \ nvcr.io/nim/nvidia/nv-yolox-page-elements-v1:latest
You can now make a local API call using this curl command:
HOSTNAME="localhost" SERVICE_PORT=8000 curl -X "POST" \ "http://${HOSTNAME}:${SERVICE_PORT}/v1/infer" \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "input": [ { "type": "image_url", "url": "data:image/png;base64,<BASE64_ENCODED_IMAGE>" }, { "type": "image_url", "url": "data:image/png;base64,<BASE64_ENCODED_IMAGE>" } ] }'
For more details on getting started with this NIM, visit the NVIDIA NIM Docs.