{ "id": 290228, "title": "👍💥 Notebooks of Object detection", "url": "/competitions/tensorflow-great-barrier-reef/discussion/290228", "author_name": "", "post_date": "2021-11-23T16:41:08.442144600Z", "votes": 12, "comment_count": 2, "views": 0, "content": "

Global Wheat Detection

\n
    \n
  1. [Training] EfficientDet
    2. \n
  2. Pytorch Starter - FasterRCNN Train
    3. \n
  3. YoloV5 Pseudo Labeling
    4. \n
  4. GlobalWheatDetection EDA
    5. \n
  5. Competition metric details + script
    6. \n
  6. nd to End Object Detection with Transformers:DETR
    7. \n
\n

Open Images 2019 - Object Detection

\n
    \n
  1. Intro to TF Hub for Object Detection
    2. \n
  2. Benchmark 2019: Speed of image reading
    3. \n
  3. training fast rcnn using torchvision
    4. \n
  4. Baseline predictions using inception resnet v2
    5. \n
\n

VinBigData Chest X-ray Abnormalities Detection

\n
    \n
  1. Convert dicom to np.array - the correct way
    2. \n
  2. Visual In-Depth EDA – VinBigData Competition Data
    3. \n
  3. EDA - VinBigData Chest X-ray Abnormalities
    4. \n
  4. VinBigData-CXR-AD YOLOv5 14 Class [train]
    5. \n
  5. VinBigData detectron2 train
    6. \n
", "messages": [ { "id": "1593102", "postDate": "11/23/2021 16:41:08", "content": "

Global Wheat Detection

\n
    \n
  1. [Training] EfficientDet
    2. \n
  2. Pytorch Starter - FasterRCNN Train
    3. \n
  3. YoloV5 Pseudo Labeling
    4. \n
  4. GlobalWheatDetection EDA
    5. \n
  5. Competition metric details + script
    6. \n
  6. nd to End Object Detection with Transformers:DETR
    7. \n
\n

Open Images 2019 - Object Detection

\n
    \n
  1. Intro to TF Hub for Object Detection
    2. \n
  2. Benchmark 2019: Speed of image reading
    3. \n
  3. training fast rcnn using torchvision
    4. \n
  4. Baseline predictions using inception resnet v2
    5. \n
\n

VinBigData Chest X-ray Abnormalities Detection

\n
    \n
  1. Convert dicom to np.array - the correct way
    2. \n
  2. Visual In-Depth EDA – VinBigData Competition Data
    3. \n
  3. EDA - VinBigData Chest X-ray Abnormalities
    4. \n
  4. VinBigData-CXR-AD YOLOv5 14 Class [train]
    5. \n
  5. VinBigData detectron2 train
    6. \n
", "rawMarkdown": "**Global Wheat Detection**\n1. [Training] EfficientDet\n2. Pytorch Starter - FasterRCNN Train\n3. YoloV5 Pseudo Labeling \n4. GlobalWheatDetection EDA \n5. Competition metric details + script\n6. nd to End Object Detection with Transformers:DETR\n\n\n**Open Images 2019 - Object Detection**\n1. Intro to TF Hub for Object Detection\n2. Benchmark 2019: Speed of image reading\n3. training fast rcnn using torchvision\n4. Baseline predictions using inception resnet v2\n\n**VinBigData Chest X-ray Abnormalities Detection**\n1. Convert dicom to np.array - the correct way\n2. Visual In-Depth EDA – VinBigData Competition Data\n3. EDA - VinBigData Chest X-ray Abnormalities\n4. VinBigData-CXR-AD YOLOv5 14 Class [train]\n5. VinBigData detectron2 train", "votes": null }, { "id": "1593912", "postDate": "11/24/2021 11:34:34", "content": "

Good work…..

", "rawMarkdown": "Good work.....", "votes": null }, { "id": "1594412", "postDate": "11/24/2021 19:49:23", "content": "

Thanks :-)

", "rawMarkdown": "Thanks :-)", "votes": null } ], "comments": [ { "id": 1593912, "author_name": "kvgovindan", "author_url": "", "post_date": "11/24/2021 11:34:34", "content": "

Good work…..

", "votes": null, "replies": [ { "id": 1594412, "author_name": "gazu468", "author_url": "", "post_date": "11/24/2021 19:49:23", "content": "

Thanks :-)

", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1593102": "**Global Wheat Detection**\n1. [Training] EfficientDet\n2. Pytorch Starter - FasterRCNN Train\n3. YoloV5 Pseudo Labeling \n4. GlobalWheatDetection EDA \n5. Competition metric details + script\n6. nd to End Object Detection with Transformers:DETR\n\n\n**Open Images 2019 - Object Detection**\n1. Intro to TF Hub for Object Detection\n2. Benchmark 2019: Speed of image reading\n3. training fast rcnn using torchvision\n4. Baseline predictions using inception resnet v2\n\n**VinBigData Chest X-ray Abnormalities Detection**\n1. Convert dicom to np.array - the correct way\n2. Visual In-Depth EDA – VinBigData Competition Data\n3. EDA - VinBigData Chest X-ray Abnormalities\n4. VinBigData-CXR-AD YOLOv5 14 Class [train]\n5. VinBigData detectron2 train", "1593912": "Good work.....", "1594412": "Thanks :-)" }, "source": "meta" }