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Opened 3月 24, 2020 by saxon_zh@saxon_zhGuest

yolov3_mobilenetV1训自己数据,训练验证正常,跑eval 和infer却没有效果

Created by: TianyouChen

训练指令: python3 tools/train.py --eval --c ./configs/yolov3_mobilenet_v1_Headshoulder.yml log: EvalReader: batch_size: 8 bufsize: 32 dataset: !VOCDataSet anno_path: test.txt dataset_dir: dataset/voc image_dir: '' label_list: label_list.txt sample_num: -1 use_default_label: true with_background: false drop_empty: false inputs_def: fields: - image - im_size - im_id - gt_bbox - gt_class - is_difficult num_max_boxes: 50 sample_transforms:

  • !DecodeImage to_rgb: true with_mixup: false
  • !ResizeImage interp: 2 max_size: 0 target_size: 640 use_cv2: true
  • !NormalizeImage is_channel_first: false is_scale: true mean:
    • 0.485
    • 0.456
    • 0.406 std:
    • 0.229
    • 0.224
    • 0.225
  • !PadBox num_max_boxes: 50
  • !Permute channel_first: true to_bgr: false worker_num: 8 LearningRate: [32mbase_lr[0m: 0.001 [32mschedulers[0m:
  • !PiecewiseDecay gamma: 0.1 milestones:
    • 8000
    • 12000
    • 16000 values: null
  • !LinearWarmup start_factor: 0.0 steps: 1000 MobileNet: [32mnorm_type[0m: sync_bn conv_group_scale: 1 conv_learning_rate: 1.0 extra_block_filters:
    • 256
    • 512
    • 128
    • 256
    • 128
    • 256
    • 64
    • 128 norm_decay: 0.0 weight_prefix_name: '' with_extra_blocks: false OptimizerBuilder: [32mregularizer[0m: factor: 0.0005 type: L2 optimizer: momentum: 0.9 type: Momentum TestReader: batch_size: 1 dataset: !ImageFolder anno_path: null dataset_dir: '' image_dir: '' sample_num: -1 use_default_label: true with_background: false inputs_def: fields:
    • image
    • im_size
    • im_id image_shape:
    • 3
    • 640
    • 640 sample_transforms:
  • !DecodeImage to_rgb: true with_mixup: false
  • !ResizeImage interp: 2 max_size: 0 target_size: 640 use_cv2: true
  • !NormalizeImage is_channel_first: false is_scale: true mean:
    • 0.485
    • 0.456
    • 0.406 std:
    • 0.229
    • 0.224
    • 0.225
  • !Permute channel_first: true to_bgr: false TrainReader: batch_size: 8 batch_transforms:
  • !RandomShape random_inter: true sizes:
    • 320
    • 352
    • 384
    • 416
    • 448
    • 480
    • 512
    • 544
    • 576
    • 640
  • !NormalizeImage is_channel_first: false is_scale: true mean:
    • 0.485
    • 0.456
    • 0.406 std:
    • 0.229
    • 0.224
    • 0.225
  • !Permute channel_first: true to_bgr: false
  • !Gt2YoloTarget anchor_masks:
      • 6
      • 7
      • 8
      • 3
      • 4
      • 5
      • 0
      • 1
      • 2 anchors:
      • 10
      • 13
      • 16
      • 30
      • 33
      • 23
      • 30
      • 61
      • 62
      • 45
      • 59
      • 119
      • 116
      • 90
      • 156
      • 198
      • 373
      • 326 downsample_ratios:
    • 32
    • 16
    • 8 num_classes: 80 bufsize: 32 dataset: !VOCDataSet anno_path: trainval.txt dataset_dir: dataset/voc image_dir: '' label_list: label_list.txt sample_num: -1 use_default_label: true with_background: false drop_last: true inputs_def: fields:
    • image
    • gt_bbox
    • gt_class
    • gt_score num_max_boxes: 50 mixup_epoch: 250 sample_transforms:
  • !DecodeImage to_rgb: true with_mixup: true
  • !MixupImage alpha: 1.5 beta: 1.5
  • !ColorDistort brightness:
    • 0.5
    • 1.5
    • 0.5 contrast:
    • 0.5
    • 1.5
    • 0.5 hue:
    • -18
    • 18
    • 0.5 random_apply: true saturation:
    • 0.5
    • 1.5
    • 0.5
  • !RandomExpand fill_value: !!python/tuple
    • 123.675
    • 116.28
    • 103.53 prob: 0.5 ratio: 4.0
  • !RandomCrop allow_no_crop: true aspect_ratio:
    • 0.5
    • 2.0 cover_all_box: false num_attempts: 50 scaling:
    • 0.3
    • 1.0 thresholds:
    • 0.0
    • 0.1
    • 0.3
    • 0.5
    • 0.7
    • 0.9
  • !RandomFlipImage is_mask_flip: false is_normalized: false prob: 0.5
  • !NormalizeBox {}
  • !PadBox num_max_boxes: 50
  • !BboxXYXY2XYWH {} shuffle: true use_process: true worker_num: 8 YOLOv3: [32mbackbone[0m: MobileNet use_fine_grained_loss: false yolo_head: YOLOv3Head YOLOv3Head: [32mnms[0m: background_label: -1 keep_top_k: 100 nms_threshold: 0.45 nms_top_k: 1000 normalized: false score_threshold: 0.01 anchor_masks:
    • 6
    • 7
    • 8
    • 3
    • 4
    • 5
    • 0
    • 1
    • 2 anchors:
    • 10
    • 13
    • 16
    • 30
    • 33
    • 23
    • 30
    • 61
    • 62
    • 45
    • 59
    • 119
    • 116
    • 90
    • 156
    • 198
    • 373
    • 326 block_size: 3 drop_block: false keep_prob: 0.9 norm_decay: 0.0 num_classes: 80 weight_prefix_name: '' yolo_loss: YOLOv3Loss YOLOv3Loss: [32mlabel_smooth[0m: false batch_size: 8 ignore_thresh: 0.7 iou_loss: null use_fine_grained_loss: false architecture: YOLOv3 log_smooth_window: 1 map_type: 11point max_iters: 18000 metric: VOC num_classes: 2 save_dir: output snapshot_iter: 2000 use_gpu: true weights: /home/chenchaocun/PaddleDetection_slim/output/yolov3_mobilenet_v1_Headshoulder

2020-03-24 15:42:13,779-INFO: 1690 samples in file dataset/voc/test.txt 2020-03-24 15:42:13,780-INFO: places would be ommited when DataLoader is not iterable W0324 15:42:14.554183 35746 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.1, Runtime API Version: 9.0 W0324 15:42:14.558101 35746 device_context.cc:245] device: 0, cuDNN Version: 7.6. 2020-03-24 15:42:16,664-INFO: 1690 samples in file dataset/voc/trainval.txt 2020-03-24 15:42:24,083-INFO: places would be ommited when DataLoader is not iterable I0324 15:42:24.101703 35746 parallel_executor.cc:440] The Program will be executed on CUDA using ParallelExecutor, 2 cards are used, so 2 programs are executed in parallel. I0324 15:42:26.898975 35746 build_strategy.cc:354] set enable_sequential_execution:1 W0324 15:42:26.973911 35746 fuse_all_reduce_op_pass.cc:74] Find all_reduce operators: 147. To make the speed faster, some all_reduce ops are fused during training, after fusion, the number of all_reduce ops is 84. I0324 15:42:26.979429 35746 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 I0324 15:42:27.174090 35746 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True I0324 15:42:27.206694 35746 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0 2020-03-24 15:42:35,910-INFO: iter: 0, lr: 0.000000, 'loss': '15755.842773', time: 0.000, eta: 0:00:01 2020-03-24 15:43:28,829-INFO: iter: 20, lr: 0.000020, 'loss': '233.463013', time: 2.964, eta: 14:48:09 2020-03-24 15:44:20,384-INFO: iter: 40, lr: 0.000040, 'loss': '164.347748', time: 1.452, eta: 7:14:29 2020-03-24 15:45:08,243-INFO: iter: 60, lr: 0.000060, 'loss': '153.642609', time: 2.696, eta: 13:26:04 2020-03-24 15:45:55,259-INFO: iter: 80, lr: 0.000080, 'loss': '126.635468', time: 2.793, eta: 13:54:09 2020-03-24 15:46:45,618-INFO: iter: 100, lr: 0.000100, 'loss': '97.038879', time: 0.186, eta: 0:55:23 2020-03-24 15:48:20,168-INFO: iter: 120, lr: 0.000120, 'loss': '62.637665', time: 2.511, eta: 12:28:12 2020-03-24 15:49:11,236-INFO: iter: 140, lr: 0.000140, 'loss': '96.968536', time: 1.741, eta: 8:38:11 2020-03-24 15:49:18,606-INFO: iter: 160, lr: 0.000160, 'loss': '100.652145', time: 4.316, eta: 21:23:23 2020-03-24 15:50:48,240-INFO: iter: 180, lr: 0.000180, 'loss': '60.300674', time: 2.717, eta: 13:26:54 2020-03-24 15:51:34,845-INFO: iter: 200, lr: 0.000200, 'loss': '74.678650', time: 1.113, eta: 5:30:02 2020-03-24 15:51:45,582-INFO: iter: 220, lr: 0.000220, 'loss': '96.790237', time: 3.995, eta: 19:43:49...... 训练中2000次验证map=56% 将此模型用eval.py跑出来map=0.005 ,跑infer.py 满屏框 box的置信度还很高 0.92~0.99 请问这是啥问题呢?

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标识: paddlepaddle/PaddleDetection#393
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