[
  {
    "mAP @0.5:0.95": 84.4,
    "mAP@0.50": 95.5,
    "code_links": [
      {
        "title": "skokec/detectron-traffic-signs",
        "url": "https://github.com/skokec/detectron-traffic-signs"
      }
    ],
    "date": "2019-04-01",
    "date2": 20190401,
    "model": "Mask R-CNN with adaptations for traffic sings and augmentations (ResNet50)",
    "paper": {
      "title": "Deep Learning for Large-Scale Traffic-Sign Detection and Recognition",
      "url": "https://cknow.io/lib/fb94017be61ba9a1"
    },
    "paper_data_uoa": "fb94017be61ba9a1"
  },
  {
    "mAP @0.5:0.95": 82,
    "mAP@0.50": 95.2,
    "code_links": [
      {
        "title": "skokec/detectron-traffic-signs",
        "url": "https://github.com/skokec/detectron-traffic-signs"
      }
    ],
    "date": "2019-04-01",
    "date2": 20190401,
    "model": "Mask R-CNN with adaptations for traffic sings  (ResNet50)",
    "paper": {
      "title": "Deep Learning for Large-Scale Traffic-Sign Detection and Recognition",
      "url": "https://cknow.io/lib/fb94017be61ba9a1"
    },
    "paper_data_uoa": "fb94017be61ba9a1"
  },
  {
    "mAP @0.5:0.95": 82.3,
    "mAP@0.50": 93,
    "code_links": [
      {
        "title": "skokec/detectron-traffic-signs",
        "url": "https://github.com/skokec/detectron-traffic-signs"
      }
    ],
    "date": "2019-04-01",
    "date2": 20190401,
    "model": "Mask R-CNN (ResNet50)",
    "paper": {
      "title": "Deep Learning for Large-Scale Traffic-Sign Detection and Recognition",
      "url": "https://cknow.io/lib/fb94017be61ba9a1"
    },
    "paper_data_uoa": "fb94017be61ba9a1"
  },
  {
    "mAP @0.5:0.95": 80.4,
    "mAP@0.50": 92.4,
    "code_links": [
      {
        "title": "skokec/detectron-traffic-signs",
        "url": "https://github.com/skokec/detectron-traffic-signs"
      }
    ],
    "date": "2019-04-01",
    "date2": 20190401,
    "model": "Faster R-CNN",
    "paper": {
      "title": "Deep Learning for Large-Scale Traffic-Sign Detection and Recognition",
      "url": "https://cknow.io/lib/fb94017be61ba9a1"
    },
    "paper_data_uoa": "fb94017be61ba9a1"
  }
]