[
  {
    "Accuracy (%)": 76.4,
    "code_links": [
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research/tree/master/HMQ"
      },
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research"
      }
    ],
    "date": "2020-07-20",
    "date2": 20200720,
    "model": "EfficientNet-B0-W8A8",
    "paper": {
      "title": "HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs",
      "url": "https://cknow.io/lib/0ebe6d41d9975ffd"
    },
    "paper_data_uoa": "0ebe6d41d9975ffd"
  },
  {
    "Accuracy (%)": 76,
    "code_links": [
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research/tree/master/HMQ"
      },
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research"
      }
    ],
    "date": "2020-07-20",
    "date2": 20200720,
    "model": "EfficientNet-B0-W4A4",
    "paper": {
      "title": "HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs",
      "url": "https://cknow.io/lib/0ebe6d41d9975ffd"
    },
    "paper_data_uoa": "0ebe6d41d9975ffd"
  },
  {
    "Accuracy (%)": 75.45,
    "code_links": [
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research/tree/master/HMQ"
      },
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research"
      }
    ],
    "date": "2020-07-20",
    "date2": 20200720,
    "model": "ResNet50-W3A4",
    "paper": {
      "title": "HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs",
      "url": "https://cknow.io/lib/0ebe6d41d9975ffd"
    },
    "paper_data_uoa": "0ebe6d41d9975ffd"
  },
  {
    "Accuracy (%)": 73.8,
    "code_links": [
      {
        "title": "yashbhalgat/QualcommAI-MicroNet-submission-MixNet",
        "url": "https://github.com/yashbhalgat/QualcommAI-MicroNet-submission-MixNet"
      }
    ],
    "date": "2020-04-20",
    "date2": 20200420,
    "model": "EfficientNet-W4A4",
    "paper": {
      "title": "LSQ+: Improving low-bit quantization through learnable offsets and better initialization",
      "url": "https://cknow.io/lib/aa19870b4963c845"
    },
    "paper_data_uoa": "aa19870b4963c845"
  },
  {
    "Accuracy (%)": 71.7,
    "code_links": [
      {
        "title": "yashbhalgat/QualcommAI-MicroNet-submission-MixNet",
        "url": "https://github.com/yashbhalgat/QualcommAI-MicroNet-submission-MixNet"
      }
    ],
    "date": "2020-04-20",
    "date2": 20200420,
    "model": "MixNet-W4A4",
    "paper": {
      "title": "LSQ+: Improving low-bit quantization through learnable offsets and better initialization",
      "url": "https://cknow.io/lib/aa19870b4963c845"
    },
    "paper_data_uoa": "aa19870b4963c845"
  },
  {
    "Accuracy (%)": 70.9,
    "code_links": [
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research/tree/master/HMQ"
      },
      {
        "title": "sony-si/ai-research",
        "url": "https://github.com/sony-si/ai-research"
      }
    ],
    "date": "2020-07-20",
    "date2": 20200720,
    "model": "MobileNetV2",
    "paper": {
      "title": "HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs",
      "url": "https://cknow.io/lib/0ebe6d41d9975ffd"
    },
    "paper_data_uoa": "0ebe6d41d9975ffd"
  }
]