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Releases: sglvladi/TensorFlowObjectDetectionTutorial

TensorFlowObjectDetectionTutorial-v2.5.0

08 Jun 19:23
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.9
Tensorflow 2.5
CUDA Toolkit 11.2
CuDNN 8.1.0
Anaconda Python 3.8

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Updated material for Tensoflow 2.5.0

TensorFlowObjectDetectionTutorial-v2.2.1

08 Jun 19:07
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.8
Tensorflow 2.2
CUDA Toolkit 10.1
CuDNN 7.6.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

TensorFlowObjectDetectionTutorial-v1.14.3

15 Sep 03:09
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.7
Tensorflow 1.14
CUDA Toolkit 10.0
CuDNN 7.6.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Corrected TensorFlow Model Detection Zoo links

TensorFlowObjectDetectionTutorial-v2.2.0

26 Jul 04:12
ce60a05
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.8
Tensorflow 2.2
CUDA Toolkit 10.1
CuDNN 7.6.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Updated the tutorial for TensorFlow 2
  • Added examples using sphinx-gallery

TensorFlowObjectDetectionTutorial-v1.14.2

08 Jul 01:54
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.7
Tensorflow 1.14
CUDA Toolkit 10.0
CuDNN 7.6.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

TensorFlowObjectDetectionTutorial-v1.14.1

24 Jun 14:24
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.7
Tensorflow 1.14
CUDA Toolkit 10.0
CuDNN 7.6.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Fixed missing images in Installation section

TensorFlowObjectDetectionTutorial-v1.14.0

04 Apr 03:13
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.7
Tensorflow 1.14
CUDA Toolkit 10.0
CuDNN 7.6.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Updated for TensorFlow 1.14, CUDA 10.0 and CuDNN 7.6.5
  • Added dataset partitioning instructions and macro script
  • Added instructions for training/evaluation model_main.py script
  • Added model evaluation instructions

TensorFlowObjectDetectionTutorial-v1.9.1

26 Nov 03:45
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.6
Tensorflow 1.9
CUDA Toolkit v9.0
CuDNN v7.0.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Added cython to prerequisites of Tensorflow models.

TensorFlowObjectDetectionTutorial-v1.9.0

28 Feb 17:21
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Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.6
Tensorflow 1.9
CUDA Toolkit v9.0
CuDNN v7.0.5
Anaconda Python 3.7

[1] Even though this tutorial is mostly based (and properly tested) on Windows 10, information is also provided for Linux systems.

Updates

  • Updated to Anaconda Python 3.7 distribution
  • Added installation information for Linux systems.
  • Updated tutorial to point latest Tensorflow models version (used since v2.1)

TensorFlowObjectDetectionTutorial-v1.6

28 Feb 15:30
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Initial version of the tutorial.

Target Software versions

Framework Version
OS Windows, Linux 1
Python 3.6
Tensorflow 1.6
CUDA Toolkit v9.0
CuDNN v7.0.5
Anaconda Python 3.6

[1] Even though this tutorial is based on Windows 10, most steps (excluding the setting of environmental variables) should apply for Linux, too.