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🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

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I have been trying to understand the LR scheduler. Code can be found [here](https://github.com/YunYang1994/tensorflow-yolov3/blob/03cb272af2e26d598c553f3a2d38024fc6f67a0b/train.py#L64) Explanation with graph would be much appreciated

Converted `bbox_class_ind` to integer data type to get rid of IndexError when bboxes is of float type.

`python3 train.py` `2019-08-05 06:48:09.794696: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA` `2019-08-05 06:48:10.439188: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from...

我训练的是自制的数据集,1000×500的tif图片 只更改了config.py中的图片和标签的dateset.txt和clsss.names ![image](https://github.com/YunYang1994/tensorflow-yolov3/assets/119485272/7698ade2-9fed-4dc4-a944-ef870d8b03f2)

Hello. I get the following error when running evaluate.py. Unfortunately, I have not found a solution to my problem. Help me please Error : NotFoundError (see above for traceback): Restoring...

Bumps [tensorflow-gpu](https://github.com/tensorflow/tensorflow) from 1.11.0 to 2.12.0. Release notes Sourced from tensorflow-gpu's releases. TensorFlow 2.12.0 Release 2.12.0 TensorFlow Breaking Changes Build, Compilation and Packaging Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These...

dependencies

貌似存在过拟合的问题,训练70个周期,Test loss比 Trainloss大很多,只训练20个周期,两者数值差不多,请问有什么解决方法么 Epoch: 70 Time: 2019-09-27 07:05:06 Train loss: 2.58 Test loss: 9.61 Saving ./checkpoint/yolov3_test_loss=9.6080.ckpt ...

修改这个代码的地方我找到了,但是后面不知道怎么改了 with tf.name_scope('summary'): tf.summary.scalar("learn_rate", self.learn_rate) tf.summary.scalar("giou_loss", self.giou_loss) tf.summary.scalar("conf_loss", self.conf_loss) tf.summary.scalar("prob_loss", self.prob_loss) tf.summary.scalar("total_loss", self.loss) #tf.summary.scalar("accuracy", 这里不知道怎么写了) logdir = "./data/log/" if os.path.exists(logdir): shutil.rmtree(logdir) os.mkdir(logdir) self.write_op = tf.summary.merge_all() self.summary_writer = tf.summary.FileWriter(logdir, graph=self.sess.graph)

用tensorRT进行模型加速,但是tensorRT不支持tensorflow的tf.cast,tf.tile,tf.range等函数,这几个函数在yolov3.py里,不知道用什么方法改成tensorRT能识别的函数