Deep Learning for Image Classification in Python with CNN

Learn to build image classification models with CNNs in Python using Google Colab and Drive. Download natural_scenes dataset, unzip it, and upload train and test folders to your drive.

Explore the train folder with six subfolders labeled 0 to 5, each hosting a distinct image class such as glaciers or forests used to train the image classification model.

Understand the .hdf5 file, an open source format that supports extensive, complex, heterogeneous data, as the CNN training output you obtain after running the model.

Evaluate a trained model saved as an .hdf5 by testing predictions on unseen images in the test folder, which mirrors the train folder structure to assess accuracy.

Explore the our_prediction folder containing four images for classification with the trained model, and build code to predict each photo's category, scalable to any number of images.

Explore the Python code for the image classification workflow in scenes.ipynb with explanatory snippets, and adapt the steps for your problem, then open the notebook in Google Colab.

Activate the GPU in Google Colab to enable faster, high-performance computing for free by changing the runtime type from none to GPU and saving.

Verify Colab gpu connection by running tf.test.gpu_device_name() and viewing the output. If not found, switch to cpu and run slower; keep the optional code cell as a harmless check.

Connect Google Colab with Google Drive to access your dataset for training, testing, and prediction in deep learning image classification.

Set up a deep learning image classification workflow in Python with Keras, mapping six category names to folders, and count the total and per-category images in the training dataset.

Expand limited datasets with image augmentation using Keras ImageDataGenerator, applying rotation, shifts, flips, brightness, and zoom, while normalizing inputs for CNN training.

Apply transfer learning with Keras applications by importing ResNet 50 with include_top false and ImageNet weights to tailor a high-accuracy model for a new dataset, then view the architecture summary.

Fine-tuning a convolutional neural network freezes initial layers to preserve general features while retraining later layers for the task, reducing data needs and training time in Keras.

Compile the model with the Adam optimizer, categorical_crossentropy loss, and accuracy metrics to support multiclass image classification with six output classes.

Learn how to use the EarlyStopping callback to prevent overfitting by monitoring validation loss, set min mode, and a patience of 20 to stop training and print the epoch number.

Save the best model during training by monitoring val_loss with ModelCheckpoint, specifying a path and a file name like scene_weights.hdf5, and customize as needed.

Train deep learning image classifiers with the fit function, using a train_generator with image augmentation, specified epochs, validation data, earlystopping and checkpointer callbacks, and scene_weights.hdf5 for predictions.

Load the saved .hdf5 weights, use Keras predict on the test folder, and compare with original labels to report about 68% accuracy; improve by adjusting inputs and model architecture.

Predict image classes with the predict function in a code cell, using our_predictions folder to generate and visualize four predictions (glaciers, buildings, sea, street) and improve accuracy with cnn architectures.