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lower mAP

Open JWSunny opened this issue 3 years ago • 1 comments

hello，I modified the config xxx.py file into a yaml file and used the hrnet or higherhrnet framework code for training. I found that the mAP on the coco validation dataset was only about 0.51.

2022-08-22 15:25:59,519 Epoch: [179][0/2341] Time 3.316s (3.316s) Speed 19.3 samples/s Data 2.263s (2.263s) Loss 0.00042 (0.00042) Accuracy 0.751 (0.751) 2022-08-22 15:30:13,151 Epoch: [179][300/2341] Time 0.813s (0.854s) Speed 78.8 samples/s Data 0.000s (0.019s) Loss 0.00032 (0.00038) Accuracy 0.803 (0.748) 2022-08-22 15:34:33,721 Epoch: [179][600/2341] Time 0.813s (0.861s) Speed 78.7 samples/s Data 0.000s (0.014s) Loss 0.00039 (0.00038) Accuracy 0.725 (0.747) 2022-08-22 15:42:29,909 Epoch: [179][900/2341] Time 1.648s (1.103s) Speed 38.8 samples/s Data 0.000s (0.012s) Loss 0.00035 (0.00038) Accuracy 0.737 (0.746) 2022-08-22 15:50:49,289 Epoch: [179][1200/2341] Time 1.665s (1.243s) Speed 38.4 samples/s Data 0.000s (0.013s) Loss 0.00035 (0.00038) Accuracy 0.756 (0.747) 2022-08-22 15:59:08,989 Epoch: [179][1500/2341] Time 1.639s (1.328s) Speed 39.1 samples/s Data 0.000s (0.013s) Loss 0.00035 (0.00038) Accuracy 0.775 (0.747) 2022-08-22 16:07:28,549 Epoch: [179][1800/2341] Time 1.668s (1.384s) Speed 38.4 samples/s Data 0.000s (0.013s) Loss 0.00041 (0.00038) Accuracy 0.752 (0.748) 2022-08-22 16:15:47,927 Epoch: [179][2100/2341] Time 1.674s (1.424s) Speed 38.2 samples/s Data 0.000s (0.012s) Loss 0.00033 (0.00038) Accuracy 0.785 (0.748) 2022-08-22 16:22:31,716 Test: [0/199] Time 1.750 (1.750) Loss 0.0004 (0.0004) Accuracy 0.816 (0.816) 2022-08-22 16:24:33,818 => writing results json to LiteHRNet_w18_output/coco/HigherLiteHRNet/LiteHRNet_w18_256x256_coco_correct_lr1e-3/results/keypoints_val2017_results_0.json 2022-08-22 16:24:44,456 | Arch | AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) | 2022-08-22 16:24:44,457 |---|---|---|---|---|---|---|---|---|---|---| 2022-08-22 16:24:44,457 | HigherLiteHRNet | 0.511 | 0.807 | 0.544 | 0.501 | 0.530 | 0.557 | 0.830 | 0.598 | 0.539 | 0.583 |

JWSunny avatar Sep 14 '22 02:09 JWSunny

config.yaml 内容如下：

AUTO_RESUME: true CUDNN: BENCHMARK: true DETERMINISTIC: false ENABLED: true DATA_DIR: '' GPUS: (0,1) OUTPUT_DIR: 'LiteHRNet_w18_output' LOG_DIR: 'LiteHRNet_w18_log' WORKERS: 8 PRINT_FREQ: 300

DATASET: COLOR_RGB: false DATASET: 'coco' ROOT: '/mnt/share/COCO/' TEST_SET: 'val2017' TRAIN_SET: 'train2017' NUM_JOINTS_HALF_BODY: 8 PROB_HALF_BODY: 0.3 FLIP: true ROT_FACTOR: 45 SCALE_FACTOR: 0.35 MODEL: NAME: 'LiteHRNet' MODEL_FILE: ''
INIT_WEIGHTS: true IMAGE_SIZE:

  • 256
  • 256 HEATMAP_SIZE:
  • 64
  • 64 SIGMA: 2 NUM_JOINTS: 17 BASE_CHANNEL: 40 TARGET_TYPE: 'gaussian' RATIO: 0.5 NUM_STAGES: 3 STAGE_REPEATS:
  • 2
  • 4
  • 2 STAGE_BRANCHES:
  • 2
  • 3
  • 4 STAGE_BLOCKS:
  • 2
  • 2
  • 2 MODULE_TYPE:
  • 'LITE'
  • 'LITE'
  • 'LITE' WITH_FUSE:
  • True
  • True
  • True REDUCE_RATIOS:
  • 8
  • 8
  • 8 WITH_HEAD: True

LOSS: USE_TARGET_WEIGHT: true TRAIN: BATCH_SIZE_PER_GPU: 32 SHUFFLE: true BEGIN_EPOCH: 0 END_EPOCH: 210 OPTIMIZER: 'adam' LR: 0.002 LR_FACTOR: 0.1 LR_STEP:

  • 160
  • 190 WD: 0.0001 GAMMA1: 0.99 GAMMA2: 0.0 MOMENTUM: 0.9 NESTEROV: false TEST: BATCH_SIZE_PER_GPU: 32 COCO_BBOX_FILE: '/mnt/share/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: 'LiteHRNet_w18_output/coco/LiteHRNet/LiteHRNet_w18_256x256_coco_better_lr1e-3/model_best.pth'
    NMS_THRE: 1.0 OKS_THRE: 0.9 FLIP_TEST: true POST_PROCESS: true BLUR_KERNEL: 11 USE_GT_BBOX: true DEBUG: DEBUG: true SAVE_BATCH_IMAGES_GT: true SAVE_BATCH_IMAGES_PRED: true SAVE_HEATMAPS_GT: false SAVE_HEATMAPS_PRED: false

JWSunny avatar Sep 14 '22 02:09 JWSunny