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YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
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Test model Error #7482

Open philp123 opened 3 years ago

philp123 commented 3 years ago

After training the self-defined dataset(using coco data format) using yolov4 model the training log specify that (mAP@0.50) is above 90%, however when using pretrained model to do inference, it gets the following info:

conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BFLOPs 78 Couldn't find activation function mish, going with ReLU conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BFLOPs 79 res 76 26 x 26 x 256 -> 26 x 26 x 256 80 Couldn't find activation function mish, going with ReLU conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BFLOPs 81 Couldn't find activation function mish, going with ReLU conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BFLOPs 82 res 79 26 x 26 x 256 -> 26 x 26 x 256 83 Couldn't find activation function mish, going with ReLU conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BFLOPs 84 route 83 56 85 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BFLOPs 86 Couldn't find activation function mish, going with ReLU conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BFLOPs 87 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 88 route 86 89 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 90 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BFLOPs 91 Couldn't find activation function mish, going with ReLU conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BFLOPs 92 res 89 13 x 13 x 512 -> 13 x 13 x 512 93 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BFLOPs 94 Couldn't find activation function mish, going with ReLU conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BFLOPs 95 res 92 13 x 13 x 512 -> 13 x 13 x 512 96 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BFLOPs 97 Couldn't find activation function mish, going with ReLU conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BFLOPs 98 res 95 13 x 13 x 512 -> 13 x 13 x 512 99 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BFLOPs 100 Couldn't find activation function mish, going with ReLU conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BFLOPs 101 res 98 13 x 13 x 512 -> 13 x 13 x 512 102 Couldn't find activation function mish, going with ReLU conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BFLOPs 103 route 102 87 104 Couldn't find activation function mish, going with ReLU conv 1024 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BFLOPs 105 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 106 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 107 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 108 max 5 x 5 / 1 13 x 13 x 512 -> 13 x 13 x 512 109 route 107 110 max 9 x 9 / 1 13 x 13 x 512 -> 13 x 13 x 512 111 route 107 112 max 13 x 13 / 1 13 x 13 x 512 -> 13 x 13 x 512 113 route 112 110 108 107 114 conv 512 1 x 1 / 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BFLOPs 115 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 116 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 117 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BFLOPs 118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 119 route 85 120 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 121 route 120 118 122 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 123 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 124 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 125 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 126 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 127 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BFLOPs 128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 129 route 54 130 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs 131 route 130 128 132 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs 133 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs 134 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs 135 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs 136 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BFLOPs 137 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BFLOPs 138 conv 75 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 75 0.104 BFLOPs 139 yolo Unused field: 'scale_x_y = 1.2' Unused field: 'iou_thresh = 0.213' Unused field: 'cls_normalizer = 1.0' Unused field: 'iou_normalizer = 0.07' Unused field: 'iou_loss = ciou' Unused field: 'nms_kind = greedynms' Unused field: 'beta_nms = 0.6' Unused field: 'max_delta = 5' 140 route 136 141 conv 256 3 x 3 / 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BFLOPs 142 route 141 126 143 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 144 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 145 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 146 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 147 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BFLOPs 148 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BFLOPs 149 conv 75 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 75 0.052 BFLOPs 150 yolo Unused field: 'scale_x_y = 1.1' Unused field: 'iou_thresh = 0.213' Unused field: 'cls_normalizer = 1.0' Unused field: 'iou_normalizer = 0.07' Unused field: 'iou_loss = ciou' Unused field: 'nms_kind = greedynms' Unused field: 'beta_nms = 0.6' Unused field: 'max_delta = 5' 151 route 147 152 conv 512 3 x 3 / 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BFLOPs 153 route 152 116 154 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 155 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 156 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 157 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 158 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BFLOPs 159 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs 160 conv 75 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 75 0.026 BFLOPs 161 yolo Unused field: 'scale_x_y = 1.05' Unused field: 'iou_thresh = 0.213' Unused field: 'cls_normalizer = 1.0' Unused field: 'iou_normalizer = 0.07' Unused field: 'iou_loss = ciou' Unused field: 'nms_kind = greedynms' Unused field: 'beta_nms = 0.6' Unused field: 'max_delta = 5'

the following is the cfg file for yolov4

Screenshot_2021-03-09_17-07-04

razgzy commented 3 years ago

I met the same error.