How do you retrain inception model?

Train Inception with Custom Images on CPU

1. Download the tensorflow-for-poets-2. Let’s start by making a new folder Flowers_Tensorflow.
2. Download the dataset. Go to this link and download the flower data.
3. Retrain the model.
4. Test the newly Trained Model.

What does retraining a model mean?

Rather retraining simply refers to re-running the process that generated the previously selected model on a new training set of data. The features, model algorithm, and hyperparameter search space should all remain the same. It only involves changing the training data set.

How do I retrain a TensorFlow model?

Steps in Retraining Object Detection Models with TensorFlow:

1. Setting up TensorFlow & the API.
2. Creating the image dataset.
3. Labelling images.
4. Training the TensorFlow model.
5. Retraining the model with your data.
6. Exporting your object detection model.

How is inception v3 trained?

Inception V3 was trained using a dataset of 1,000 classes (See the list of classes here) from the original ImageNet dataset which was trained with over 1 million training images, the Tensorflow version has 1,001 classes which is due to an additional “background’ class not used in the original ImageNet.

How long does it take to train inception-v3?

We can train a model from scratch to its best performance on a desktop with 8 NVIDIA Tesla K40s in about 2 weeks. In order to make research progress faster, we are additionally supplying a new version of a pre-trained Inception-v3 model that is ready to be fine-tuned or adapted to a new task.

How often should I retrain my model?

And learn more about retraining strategies You may find a lot of tutorials which would help you build end to end Machine Learning pipelines. But generally, those tutorials do not mention much about how to maintain the quality of predictions generated from the ML systems.

When should you retrain a model?

As data distribution drifts over time, model deployment is not a one time task, but a continuous process. It is a best practice to continuously monitor your incoming data and retrain your model on newer data when you know that your data distribution has deviated from the original training data distribution.

How are pre-trained models used?

Use the Architecture of the pre-trained model – What we can do is that we use architecture of the model while we initialize all the weights randomly and train the model according to our dataset again. Train some layers while freeze others – Another way to use a pre-trained model is to train is partially.

How many layers does inception v3 have?

Inception-v3 is a convolutional neural network that is 48 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

What does inception v3 do?

Inception v3 is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for Googlenet. It is the third edition of Google’s Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge.