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How to Train custom Object Detection Neural Network using TensorFlow 2.3 on Google Colab Free GPU
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#tensorflow #objectdetection #computervision
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Link for my deeplearning udemy course coupon code added
Hi Everyone, In this tensorflow tutorial, I have explained how to train tensorflow object detection api with your own data. I have used tensorflow latest version here. For
model training, I am using google colab free GPU. It means it’s a full-on tutorial on how to train object detection api with your own data or custom object detection model on google colab.
I have taken the example of playing card detection in this video and the model is taken from tensorflow model zoo.
-----Time links to each step in the video-----
Chapters:
0:00 Introduction
3:24 Install TensorFlow object detection api using powershell,Set up Object Detection directory and python virtual environment at once
6:30 Gather and label pictures
13:51 Generate training and testing dataset
16:39 Create train tfrecord and test tfrecord files
20:23 Create label map and configure training
28:55 Setup google colab for object detection model training
33:20 Start model training on colab
33:56 Export inference graph
36:57 Try out your object detector for images.
39:46 Try out your object detector on a live webcam.
this contains:
a. PowerShell script to install and set up tensorflow object detection api.
3. run this command for generating csv file for training and testing images
5. Generate tfrecord file for training by this command
6. Generate tfrecord file for training by this command
7. Here are the argument to be updated on the config file for model training
num_classes: 5 [give number of classes here]
learning_rate_base: 0.8e-3
warmup_learning_rate: 0.0001
fine_tune_checkpoint: "efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection"
8. train model command
9. export infrence graph command
If you like the video, please subscribe to the channel by using the below link
Link for my deeplearning udemy course coupon code added
Hi Everyone, In this tensorflow tutorial, I have explained how to train tensorflow object detection api with your own data. I have used tensorflow latest version here. For
model training, I am using google colab free GPU. It means it’s a full-on tutorial on how to train object detection api with your own data or custom object detection model on google colab.
I have taken the example of playing card detection in this video and the model is taken from tensorflow model zoo.
-----Time links to each step in the video-----
Chapters:
0:00 Introduction
3:24 Install TensorFlow object detection api using powershell,Set up Object Detection directory and python virtual environment at once
6:30 Gather and label pictures
13:51 Generate training and testing dataset
16:39 Create train tfrecord and test tfrecord files
20:23 Create label map and configure training
28:55 Setup google colab for object detection model training
33:20 Start model training on colab
33:56 Export inference graph
36:57 Try out your object detector for images.
39:46 Try out your object detector on a live webcam.
this contains:
a. PowerShell script to install and set up tensorflow object detection api.
3. run this command for generating csv file for training and testing images
5. Generate tfrecord file for training by this command
6. Generate tfrecord file for training by this command
7. Here are the argument to be updated on the config file for model training
num_classes: 5 [give number of classes here]
learning_rate_base: 0.8e-3
warmup_learning_rate: 0.0001
fine_tune_checkpoint: "efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection"
8. train model command
9. export infrence graph command
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