JWSunny
commented
1 year ago 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
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 |