train my own dataset , but AP is very low

[ing-NT]

Recently I run HRnet keypoint detection code, I didn't use any PRETRAINED MODEL (including ImageNet and COCO datasets), then train and test（use groundtruth bbox） my own datasets(My own dataset is already in coco format) ，AP only around 1%. However, using pretrained (coco trained in the paper), AP can reach about 88%. I don't know why, and do you know the reason?@leoxiaobin My configuration file is as follows： AUTO_RESUME: true CUDNN: BENCHMARK: true DETERMINISTIC: false ENABLED: true DATA_DIR: '' GPUS: (0,) OUTPUT_DIR: 'output' LOG_DIR: 'log' WORKERS: 24 PRINT_FREQ: 100 DATASET: COLOR_RGB: true DATASET: 'coco' DATA_FORMAT: jpg FLIP: true NUM_JOINTS_HALF_BODY: 8 PROB_HALF_BODY: 0.3 ROOT: 'data/coco/' ROT_FACTOR: 45 SCALE_FACTOR: 0.35 TEST_SET: 'val2017' TRAIN_SET: 'train2017' MODEL: INIT_WEIGHTS: true NAME: pose_hrnet NUM_JOINTS: 17 PRETRAINED: ' ' TARGET_TYPE: gaussian IMAGE_SIZE:

• 192
• 256 HEATMAP_SIZE:
• 48
• 64 SIGMA: 2 EXTRA: PRETRAINED_LAYERS:
  • 'conv1'
  • 'bn1'
  • 'conv2'
  • 'bn2'
  • 'layer1'
  • 'transition1'
  • 'stage2'
  • 'transition2'
  • 'stage3'
  • 'transition3'
  • 'stage4' FINAL_CONV_KERNEL: 1 STAGE2: NUM_MODULES: 1
    NUM_BRANCHES: 2 BLOCK: BASIC NUM_BLOCKS:
    • 4
    • 4 NUM_CHANNELS:
    • 32
    • 64 FUSE_METHOD: SUM STAGE3: NUM_MODULES: 4 NUM_BRANCHES: 3 BLOCK: BASIC NUM_BLOCKS:
    • 4
    • 4
    • 4 NUM_CHANNELS:
    • 32
    • 64
    • 128 FUSE_METHOD: SUM STAGE4: NUM_MODULES: 3 NUM_BRANCHES: 4 BLOCK: BASIC NUM_BLOCKS:
    • 4
    • 4
    • 4
    • 4 NUM_CHANNELS:
    • 32
    • 64
    • 128
    • 256 FUSE_METHOD: SUM LOSS: USE_TARGET_WEIGHT: true TRAIN: BATCH_SIZE_PER_GPU: 32 SHUFFLE: true BEGIN_EPOCH: 0 END_EPOCH: 210 OPTIMIZER: adam LR: 0.001 LR_FACTOR: 0.1 LR_STEP:
• 170
• 200 WD: 0.0001 GAMMA1: 0.99 GAMMA2: 0.0 MOMENTUM: 0.9 NESTEROV: false TEST: BATCH_SIZE_PER_GPU: 32 COCO_BBOX_FILE: 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json' BBOX_THRE: 1.0 IMAGE_THRE: 0.0 IN_VIS_THRE: 0.2 MODEL_FILE: '' NMS_THRE: 1.0 OKS_THRE: 0.9 USE_GT_BBOX: true FLIP_TEST: true POST_PROCESS: true SHIFT_HEATMAP: true DEBUG: DEBUG: true SAVE_BATCH_IMAGES_GT: true SAVE_BATCH_IMAGES_PRED: true SAVE_HEATMAPS_GT: true SAVE_HEATMAPS_PRED: true

[khandriod]

hi, how you used this architecture on a custom dataset?

Thanks

[ing-NT]

Make the same data set format as COCO2017 and put it in the same place as the code

------------------ 原始邮件 ------------------ 发件人: "leoxiaobin/deep-high-resolution-net.pytorch" @.>; 发送时间: 2021年12月23日(星期四) 上午10:41 @.>; @.**@.>; 主题: Re: [leoxiaobin/deep-high-resolution-net.pytorch] train my own dataset , but AP is very low (#264)

hi, how you used this architecture on a custom dataset?

Thanks

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[khandriod]

Thanks for your reply. I do not have labeled data. So, should I use pre-trained model weights for my dataset? and where are the results stored (images with pose skeleton)?

[ing-NT]

How can you train your own data without  the label?? train result stored:output/coco/pose_hrnet/w32_256x192_adam_lr1e-3/results

------------------ 原始邮件 ------------------ 发件人: "leoxiaobin/deep-high-resolution-net.pytorch" @.>; 发送时间: 2021年12月23日(星期四) 下午2:30 @.>; @.**@.>; 主题: Re: [leoxiaobin/deep-high-resolution-net.pytorch] train my own dataset , but AP is very low (#264)

Thanks for your reply. I do not have labeled data. So, should I use pre-trained model weights for my dataset? and where are the results stored (images with pose skeleton)?

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[khandriod]

I don't want to train from scratch. That's why I want to use a pre-trained model (transfer learning, if I am not wrong). Can I use pre-trained weight on a custom dataset?

Thanks

[ing-NT]

Of course you can

------------------ 原始邮件 ------------------ 发件人: "leoxiaobin/deep-high-resolution-net.pytorch" @.>; 发送时间: 2021年12月23日(星期四) 下午3:07 @.>; @.**@.>; 主题: Re: [leoxiaobin/deep-high-resolution-net.pytorch] train my own dataset , but AP is very low (#264)

I don't want to train from scratch. That's why I want to use a pre-trained model (transfer learning, if I am not wrong). Can I use pre-trained weight on a custom dataset?

Thanks

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[khandriod]

could you please write down the steps that I should follow in order to achieve the goal? my goal is to use HRNET to estimate the human pose on my dataset using pre-trained weights.

Thanks

[Aliiiu]

@ing-NT Thanks for the feedbacks, please i will like to know if i can train this model, using a pretrained model, on a images with lesser joint label. If not how can i go about it without it causing conflict with the pretrained model.

[inever789]

Make the same data set format as COCO2017 and put it in the same place as the code

Hello, may I ask, if the custom dataset is used, does each picture need to be marked manually and then converted into coco format? Thank you very much.

[inever789]

could you please write down the steps that I should follow in order to achieve the goal? my goal is to use HRNET to estimate the human pose on my dataset using pre-trained weights.

Thanks

Hello, have you implemented training on custom datasets now? Could you tell me the operation procedure? I'm having the same problem right now. Thank you very much.