Building a CNN model for labeling video data – Labeling Video Data-3

Here is the output:

Figure 9.1 – CNN model loss and accuracy

This code snippet calculates the test loss and accuracy of the model on the test set, using the evaluate function. The results will provide insights into how well the model performs on unseen video data.

7. Make predictions: Once the model is trained and evaluated, we can use it to make predictions on new video data:

Predictions on new video data
Assuming ‘test_video’ is loaded and preprocessed similarly to the training data
predictions = loaded_model.predict(test_video)
Define the label mapping
label_mapping = {0: ‘Dance’, 1: ‘Brushing’}
Print class probabilities for each video in the test set
for i, pred in enumerate(predictions):
print(f”Video {i + 1} – Class Probabilities: \
Dance={pred[0]:.4f}, Brushing={pred[1]:.4f}”)
Convert predictions to labels using the mapping
predicted_labels = np.vectorize(label_mapping.get) \
(np.argmax(predictions, axis=1))
print(predicted_labels)

Here is the output:

Figure 9.2 – The CNN model’s predicted label

In this code snippet, test_video represents new video frames or sequences that the model hasn’t seen before. The predict function generates predictions for each input sample, which can be used for further analysis or decision-making. In the provided code, after making predictions, you convert the predictions to labels and print them.

8. Save and load the model: If you want to reuse the trained model later without retraining, you can save it to disk and load it when needed: Save the model
   model.save(“video_classification_model.h5”)
   Load the model
   loaded_model = keras.models.load_model( \
   “video_classification_model.h5”)

The save function saves the entire model architecture, weights, and optimizer state to a file. The load_model function allows you to load the saved model and use it for predictions or further training.

9. Fine-tuning and hyperparameter optimization: To improve the performance of your video classification model, you can explore techniques such as fine-tuning and hyperparameter optimization. Fine-tuning involves training the model on a smaller, task-specific dataset to adapt it to your specific video classification problem. Hyperparameter optimization involves systematically searching for the best combination of hyperparameters (e.g., the learning rate, batch size, and number of layers) to maximize the model’s performance.

These steps can help you build a supervised CNN model for video data classification. You can customize the steps according to your specific dataset and requirements. Experimentation, iteration, and tuning are key to achieving the best performance for your video classification task.

This code demonstrates the steps of loading, preprocessing, training, evaluating, and saving the model using the Kinetics Human Action Video dataset. Modify and customize the code based on your specific dataset and requirements.

Building CNN models for labeling video data has become essential for extracting valuable insights from the vast amount of visual information available in videos. In this section, we introduced the concept of CNNs, discussed architectures suitable for video data labeling, and covered essential steps in the modeling process, including data preparation, training, and evaluation. By understanding the principles and techniques discussed in this section, you will be empowered to develop your own CNN models for video data labeling, facilitating the analysis and understanding of video content in diverse applications.

In the next section, let’s see how to classify videos using autoencoders