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about data #68

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mrzhangzizhen123 opened this issue Jun 12, 2019 · 9 comments
Open

about data #68

mrzhangzizhen123 opened this issue Jun 12, 2019 · 9 comments

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@mrzhangzizhen123
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Can this code use other data?For example, medical image, please give some specific Suggestions?thank you.

@frankite
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I have same question ,have you solved it ? thanks !

@gireek
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gireek commented Jul 22, 2019

I have the same question. Please share how did you get keypoints on your own data.

@chaurasiat
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@mrzhangzizhen123 , I have same question..have you solved it?

@wanghao14
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Hi, I had participated a ICCV2019 workshop&challenge and used this code for the tiger pose estimation task. After making some minor modifications to the original code, I got the 2nd place in the final leaderboard. The modified code has been publiced and I hope this will help you apply the HRNet code to your own data.

@welleast
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Big congratulations to wanghao14!

@wanghao14
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@welleast Thanks for you encouragement. The result depends entirey on the robustness and state-of-the-art performance of your great work for pose estimation.

@ZP-Guo
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ZP-Guo commented Nov 5, 2019

How I can train HRNet with my own dataset as follow. Maybe some steps is not clear enough, but I think you can reference it. I will be pleased if I can help

  1. Change your own dataset format into the COCO's, and you need to get bbox of every human in
    your images.
  2. "mkdir" ./data/xxx/annotaions, /data/xxx/iamges, /data/xxx/person_detections_results and put your
    data into these "dir"s like ./data/coco
  3. Copy ./lib/dataset/coco.py to ./lib/dataset/xxx.py.
  4. Modify ./lib.dataset/xxx.py: def image_path_from_index(self,index) according to your format of
    images.
  5. Copy ./experiments/coco to ./experiments/xxx.
  6. For example, modify ./experiments/xxx/hrnet/w32_256x192_adam_lr1e-3.yaml.
    DATASET.DATASET:'xxx',
    DATASET.RROT:'./data/xxx',
    DATASET.TEST_SET:'val' (if you need)
    DATASET.TRAIN_SET:'train' (if you need)
    TEST.COCO_BBOX_FILE:'./data/xxx/person_detections_results/xxx_detections_person.json'.

@Gokulnath31
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What tool did you use to create your own dataset?

@Gokulnath31
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How I can train HRNet with my own dataset as follow. Maybe some steps is not clear enough, but I think you can reference it. I will be pleased if I can help

  1. Change your own dataset format into the COCO's, and you need to get bbox of every human in
    your images.
  2. "mkdir" ./data/xxx/annotaions, /data/xxx/iamges, /data/xxx/person_detections_results and put your
    data into these "dir"s like ./data/coco
  3. Copy ./lib/dataset/coco.py to ./lib/dataset/xxx.py.
  4. Modify ./lib.dataset/xxx.py: def image_path_from_index(self,index) according to your format of
    images.
  5. Copy ./experiments/coco to ./experiments/xxx.
  6. For example, modify ./experiments/xxx/hrnet/w32_256x192_adam_lr1e-3.yaml.
    DATASET.DATASET:'xxx',
    DATASET.RROT:'./data/xxx',
    DATASET.TEST_SET:'val' (if you need)
    DATASET.TRAIN_SET:'train' (if you need)
    TEST.COCO_BBOX_FILE:'./data/xxx/person_detections_results/xxx_detections_person.json'.

What tool did you use to create your own dataset?

Masum06 added a commit to Masum06/deep-high-resolution-net.pytorch that referenced this issue Aug 1, 2022
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