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nvidia

nemoretriever-graphic-elements-v1

Run Anywhere

Model for object detection, fine-tuned to detect charts, tables, and titles in documents.

Chart DetectionObject DetectionTable Detectiondata ingestionnemo retrieverrun-on-rtx
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API Reference
Accelerated by DGX Cloud
Deploying your application in production? Get started with a 90-day evaluation of NVIDIA AI Enterprise

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Generate API Key

Step 2
Pull and Run the NIM

$ 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/nemoretriever-graphic-elements-v1:latest

Step 3
Test the NIM

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.