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Deploy MMDetection

Deploy MMDetection using Serve MMDetection 3.0 app to serve models and can be used to deploy custom and pretrained models that you can use via Apply NN layer. Custom models will appear in the custom tab of the table only if you have any trained MMDetection models in your Team Files. You can train your own model using Train MMDetection 3.0 app. If you want to use pretrained models, simply select "Pretrained public models" tab in model selector.

image

Settings:

How to use:

  1. Add Deploy MMDetection layer
  2. Open agent settings and select agent and device
  3. Open models selector and select one of the available models
  4. Press SERVE
  5. Wait until model is deployed, you will see "Model deployed" message in the bottom of the layer card
  6. Connect this layer to Apply NN Inference layer's Deployed model (optional) socket
  7. If you want to deploy another model, press STOP and repeat steps 3, 4, 5 and 6

Settings:

  • Select agent - select agent and device that will be used for deployment:
    • Agent - select agent
    • Device - select CPU or GPU (faster) device if available
  • Select model - select custom or pretrained model
    • Model type - custom or pretrained
    • Task type - select task type from "object detection" or "instance segmentation"
    • Checkpoint - select checkpoint
  • Auto stop model session - automatically stop model session when pipeline is finished

JSON view

JSON view 
  {
    "action": "deploy_mmdetection",
    "src": [],
    "dst": "$deploy_mmdetection_2",
    "settings": {
      "agent_id": 359,
      "device": "cuda:0",
      "model_source": "Pretrained models",
      "task_type": "instance segmentation",
      "checkpoint_name": "mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.pth",
      "checkpoint_url": "https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth",
      "config_url": "configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py",
      "arch_type": "ConvNeXt",
      "stop_model_session": true,
      "session_id": 59493
    }
  }