voc_eval.py:41: RuntimeWarning: invalid value encountered in greater_equal if np.sum(rec = t) ==
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voc_eval.py:41: RuntimeWarning: invalid value encountered in greater_equal if np.sum(rec = t) ==
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訓練faster-rcnn報錯
訓練結束后測試時出現類似
File "/home/xxx/py-faster-rcnn/tools/../lib/datasets/voc_eval.py", line 126, in voc_evalR = [obj for obj in recs[imagename] if obj['name'] == classname] KeyError: '000002'解決方法: 刪除data/VOCdekit2007下的annotations_cache文件夾
基于ResNet101訓練py-faster-rcnn遇到錯誤:
/faster-rcnn-py/tools/../lib/fast_rcnn/bbox_transform.py:50: RuntimeWarning: overflow encountered in exp pred_h = np.exp(dh) * heights[:, np.newaxis] faster-rcnn-py/tools/../lib/rpn/proposal_layer.py:176: RuntimeWarning: invalid value encountered in greater_equal keep = np.where((ws >= min_size) & (hs >= min_size))[0]類似解決方法:減小lr from 0.001 to 0.0001
A possible solution could be to decrease the base learning rate in the solver.prototxt As it is recommended here http://caffe.berkeleyvision.org/tutorial/solver.html Just change the base_lr: 0.001 to 0.0001Note also that the above settings are merely guidelines, and they’re definitely not guaranteed to be optimal (or even work at all!) in every situation. If learning diverges (e.g., you start to see very large or NaN or inf loss values or outputs), try dropping the base_lr (e.g., base_lr: 0.001) and re-training, repeating this until you find a base_lr value that works. I did try to change the base_lr value and now the NAN value disappeared.總結
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