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argusswift / YOLOv4-pytorch

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训练自己的数据集的时候怎么放置啊？一定设置成voc2007与voc2012的形式吗？

12
haobabuhaoba
haobabuhaoba commented Oct 6, 2020 
[2020-10-06 21:13:34,984]-[train.py line:164]:===== Validate =====
Traceback (most recent call last):
  File "train.py", line 212, in <module>
    fp_16=opt.fp_16).train()
  File "train.py", line 166, in train
    APs, inference_time = Evaluator(self.yolov4, showatt=False).APs_voc()
  File "D:\hua\YOLOv4-pytorch\eval\evaluator.py", line 32, in APs_voc
    with open(img_inds_file, 'r')
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good first issue

open-mmlab / mmclassification

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Roadmap of MMClassification

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hellock
hellock commented Jul 13, 2020

We keep this issue open to collect feature requests from users and hear your voice. Our monthly release plan is also available here.

You can either:

  1. Suggest a new feature by leaving a comment.
  2. Vote for a feature request with 👍 or be against with 👎. (Remember that developers are busy and cannot respond to all feature requests, so vote for your most favorable one!)
  3. Tell us that you wo
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good first issue help wanted

anindox8 / Ensemble-of-Multi-Scale-CNN-for-Dermatoscopy-Classification

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Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.

deep-learning ensemble-learning densenet convolutional-neural-networks binary-classification inception-v3 senet multi-scale seresnext cyclic-learning-rate skin-lesion-classification shades-of-gray tensorflow-keras color-constancy efficientnet
  • Updated Oct 27, 2020
  • Jupyter Notebook

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