运行 YunYang1994/tensorflow-yolov3 所遇到的一些问题记录

将 video_demo.py 的 video_path 参数设置为0后运行（说是设置为0就是调用本地摄像头的地址），显示错误信息如下：

D:\Yolov3_Tensorflow\python\python.exe D:/Yolov3_Tensorflow/tensorflow-yolov3/video_demo.py
2019-10-02 09:48:48.209137: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-10-02 09:48:48.907262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties:
name: GeForce MX250 major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 2.00GiB freeMemory: 1.62GiB
2019-10-02 09:48:48.907463: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0
2019-10-02 09:48:49.886713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-10-02 09:48:49.886834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977]      0
2019-10-02 09:48:49.886922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0:   N
2019-10-02 09:48:49.887131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1374 MB memory) -> physical GPU (device: 0, name: GeForce MX250, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-10-02 09:49:06.266401: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-10-02 09:49:06.293074: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-10-02 09:49:06.296075: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-10-02 09:49:06.430939: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.60GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-10-02 09:49:06.506296: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.56GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-10-02 09:49:06.519117: W tensorflow/core/common_runtime/bfc_allocator.cc:215] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
Traceback (most recent call last):File "D:/Yolov3_Tensorflow/tensorflow-yolov3/video_demo.py", line 60, in <module>image = utils.draw_bbox(frame, bboxes)File "D:\Yolov3_Tensorflow\tensorflow-yolov3\core\utils.py", line 83, in draw_bboxbbox_color = colors[class_ind]
IndexError: list index out of range
[ WARN:1] terminating async callbackProcess finished with exit code 1

解决方案参见：YunYang1994/tensorflow-yolov3 IndexError: list index out of range 解决办法

运行 YunYang1994/tensorflow-yolov3 所遇到的一些问题记录相关推荐

tensorflow yolov3训练自己的数据集，详细教程
这个教程是我在自己学习的过程中写的,当作一个笔记,写的比较详细在github上下载yolov3的tensorflow1.0版本: https://github.com/YunYang1994/ten ...
yolov3 训练及数据集准备【记录】
------------------------------ 本文仅供学习交流,如有错误,望交流指教 ------------------------------ yolov3 训练及数据集准备[记录 ...
Tensorflow yolov3 Intel Realsense D435 单摄像头下各模块识别时间测试
文章目录 1.一次循环总耗时 2.从循环开始到self.predict()之前 3.第一次循环主要耗时函数:wait_for_frames()(其实self.predict()函数在第一次循环更耗时) ...
Tensorflow YOLOv3实现与详解
tips:大部分资源来自https://www.jianshu.com/p/3943be47fe84,这里权当一组学习记录. 另外推荐一个英文详解blog:YOLO V3 PyTorch实现 1 环境 ...
Tensorflow yolov3 Intel Realsense D435 双摄像头下测试python多线程（假的多线程）self.predict()函数运行时间（191204）
测试代码: # -*- coding: utf-8 -*- """ @File : test-191204-两个摄像头调用多线程识别.py @Time : 2019/12 ...
成功运行官方Tensorflow Android的demo的过程
记录下运行tensorflow官方demo的过程运行环境 windows 10 .Android Studio 3.1.4 1.在github上下载源码 https://github.com/ten ...
如何在Hi3559A上运行自己的YOLOv3模型
发现现在相关资源大多比较混乱,看起来比较难有头绪,入门难度比较大,所以单独整理并记录自己在3559A上的学习yolo模型的过程此贴为索引贴,下为目录.博主正在加紧更新中.... 注:海思官方推荐的编 ...
如何在Hi3559A上运行自己的YOLOv3模型(五)
将自己的网络放到HI3559A上运行前言:在上一篇中我们已经可以根据仿真的结果看到自己转化后的模型的效果是什么样的,此次我们来将这个结果在海思处理器上复现出来 step1. 在ubuntu上进入SD ...
Tensorflow yolov3 Intel Realsense D435 多进程multiprocessing线程池pool识别时间测试
单摄像头下: 耗时:3.571178674697876秒耗时:0.0682528018951416秒耗时:0.07838797569274902秒耗时:0.08464503288269043秒 ...

运行 YunYang1994/tensorflow-yolov3 所遇到的一些问题记录

运行 YunYang1994/tensorflow-yolov3 所遇到的一些问题记录相关推荐

最新文章

热门文章