Deep Learning Image Classification in PyTorch 2.0

Learn how to prepare and load an image classification dataset in PyTorch, including two dataset versions (a complete version and a 20-image subset) for training and benchmarking the model.

Learn how to load and process a PyTorch image classification dataset with the image folder function, including root, transform, loader, and is valid file, versus a custom class.

Learn to implement data processing and loading for image classification in PyTorch with ImageFolder, transforms, and DataLoader, including resizing to 224x224 and batching with shuffle.

Learn how to create a custom dataset class in PyTorch by inheriting from torch.utils.data, implementing __init__, __len__, and __getitem__ to control data loading, transforms, and augmentation.

Design a custom PyTorch dataset class for image data processing using OpenCV, loading images, applying transforms, extracting class labels from directory structure, and returning image-label tensors.

Develop a testing data preparation pipeline in PyTorch by implementing a custom dataset loader with train and test transforms, including resize, color jitter, random flips, rotation, and tensor conversion.

Understand the LeNet-5 architecture and its role in popularizing CNNs, detailing its convolutional and pooling layers, ReLU activations, and the transition to fully connected classifiers trained in PyTorch.

Implement the LeNet-5 model in PyTorch, detailing convolutional layers, max pooling, ReLU activations, and fully connected layers for a seven-class RGB image classifier.

Resolve a Colab PyTorch training and inference issue by linking the libcuda.so file before code, then disconnect and delete the runtime and click Run All to proceed.

Train a LeNet image classifier in PyTorch 2.0 with a reusable Colab training script, using GPU, 32x32 RGB images, batch size 16, cross-entropy loss, and Adam optimizer for 30 epochs.

Train a LeNet model in PyTorch 2.0 with a batch training loop, track loss across 30 epochs with tqdm, and save checkpoints; evaluate accuracy with a classification report.

Apply a pretrained LeNet model in PyTorch 2.0 to infer images using OpenCV preprocessing, GPU, and Colab workflow, producing class predictions and saved annotated images.

Explore the VGG 16 architecture, a 16-layer convnet with 13 convolutional and 3 fully connected layers, using 3x3 filters, batch normalization, and ReLU for image classification.

Explore the history and architecture of VGG 16 and implement a batch-normalized VGG 16 in PyTorch 2.0, loading pre-trained weights and adapting the classifier for seven classes.

Train and evaluate a VGG-16 image classifier in PyTorch 2.0 on Colab, using 224 by 224 images, load seven classes, and save weights while assessing accuracy and F1 scores.

Infer the VGG 16 model in PyTorch 2.0 on Colab with GPU, using a saved inference script to classify images and report accuracy versus LeNet.

Learn the inception v3 architecture of GoogLeNet, detailing multi-branch inception modules, reduction blocks, and a final 1000-class classifier built on 299×299 rgb inputs.

Implement the inception v3 model in PyTorch 2.0, load pretrained weights, and replace the final fully connected layer to seven classes for training on a custom dataset.

Train an Inception V3 model in PyTorch 2.0 on Google Colab by modifying the training script, loading data for seven classes, and using an input size of 299 by 299.

Perform inference on the inception v3 model in PyTorch 2.0 using the modified inference lineage script in Google Colab, loading the checkpoint, and saving predicted images.

Explore the history and architecture of ResNet-50, learn residual learning with skip connections, and train the ResNet-50 model in PyTorch 2.0 for accurate image classification.

Implement a ResNet 50 model in PyTorch 2.0 by loading pretrained weights from torchvision and replacing the final layer to seven classes for training.

train a ResNet-50 image classification model in PyTorch 2.0 on Google Colab using data from Drive, with transfer learning and model checkpointing to achieve high accuracy.

Infer the ResNet 50 model in PyTorch 2.0 using the prepared inference script for validation, with guidance available on the question board.

Learn how to train image classification models such as VGG, Inception, and ResNet using PyTorch 2.0, with a note on industry applications and the importance of feedback.