fast-rcnn

⚠️ Notice: Limited Maintenance

This project is no longer actively maintained. While existing releases remain available, there are no planned updates, bug fixes, new features, or security patches. Users should be aware that vulnerabilities may not be addressed.

Object Detection using torchvision's pretrained fast-rcnn model.

• Download the pre-trained fast-rcnn object detection model's state_dict from the following URL :

https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth

wget https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth

• Create a model archive file and serve the fastrcnn model in TorchServe using below commands

  torch-model-archiver --model-name fastrcnn --version 1.0 --model-file examples/object_detector/fast-rcnn/model.py --serialized-file fasterrcnn_resnet50_fpn_coco-258fb6c6.pth --handler object_detector --extra-files examples/object_detector/index_to_name.json
  mkdir model_store
  mv fastrcnn.mar model_store/
  torchserve --start --model-store model_store --models fastrcnn=fastrcnn.mar --disable-token-auth  --enable-model-api
  curl http://127.0.0.1:8080/predictions/fastrcnn -T examples/object_detector/persons.jpg

• Note : The objects detected have scores greater than "0.5". This threshold value is set in object_detector handler.

• Output

[
  {
    "person": [
      167.4222869873047,
      57.03825378417969,
      301.305419921875,
      436.68682861328125
    ],
    "score": 0.9995299577713013
  },
  {
    "person": [
      89.61490631103516,
      64.8980484008789,
      191.40206909179688,
      446.6605224609375
    ],
    "score": 0.9995074272155762
  },
  {
    "person": [
      362.34539794921875,
      161.9876251220703,
      515.53662109375,
      385.2342834472656
    ],
    "score": 0.9977679252624512
  },
  {
    "handbag": [
      67.37423706054688,
      277.63787841796875,
      111.6810073852539,
      400.26470947265625
    ],
    "score": 0.9925485253334045
  },
  {
    "handbag": [
      228.7159423828125,
      145.87753295898438,
      303.5065612792969,
      231.10513305664062
    ],
    "score": 0.9921919703483582
  },
  {
    "handbag": [
      379.4246826171875,
      259.97760009765625,
      419.0148620605469,
      317.9510498046875
    ],
    "score": 0.9896279573440552
  },
  {
    "person": [
      517.9014282226562,
      149.55001831054688,
      636.5952758789062,
      365.5250549316406
    ],
    "score": 0.9828333854675293
  },
  {
    "bench": [
      268.9991760253906,
      217.24330139160156,
      423.9517822265625,
      390.4784851074219
    ],
    "score": 0.9581767916679382
  },
  {
    "person": [
      539.6831665039062,
      157.81715393066406,
      616.1688842773438,
      253.09609985351562
    ],
    "score": 0.8993930816650391
  },
  {
    "person": [
      477.1377868652344,
      147.9254913330078,
      611.0255126953125,
      297.9276428222656
    ],
    "score": 0.8726601600646973
  },
  {
    "bench": [
      286.6688537597656,
      216.35751342773438,
      550.4537353515625,
      383.19561767578125
    ],
    "score": 0.8438199162483215
  },
  {
    "person": [
      627.44677734375,
      177.19900512695312,
      640.0,
      247.35137939453125
    ],
    "score": 0.8364201188087463
  },
  {
    "bench": [
      88.39929962158203,
      226.47962951660156,
      560.918701171875,
      421.661865234375
    ],
    "score": 0.7469933032989502
  },
  {
    "handbag": [
      406.9602355957031,
      261.8284606933594,
      453.7619934082031,
      357.5364990234375
    ],
    "score": 0.7322059273719788
  },
  {
    "chair": [
      451.3659973144531,
      207.49049377441406,
      504.656982421875,
      287.66192626953125
    ],
    "score": 0.6674202084541321
  },
  {
    "chair": [
      454.3897399902344,
      207.96115112304688,
      487.7691955566406,
      270.31329345703125
    ],
    "score": 0.5939609408378601
  }
]