This repository aims to demonstrate image classification using a pre-trained ResNet-152 model provided by PyTorch.
The purpose of this project is to showcase how deep learning models, specifically convolutional neural networks (CNNs), can be used for image classification tasks. By leveraging the ResNet-152 architecture, which is known for its exceptional performance on various image recognition benchmarks, we aim to illustrate the effectiveness of pre-trained models in recognizing objects within images.
The project involves the following steps:
- Loading a pre-trained ResNet-152 model provided by PyTorch.
- Preprocessing an image to match the model's input requirements.
- Performing inference on the preprocessed image to obtain predictions.
- Interpreting the model's output to identify the top predicted classes and their probabilities.
ResNet-152 is chosen for its depth and performance. With 152 layers, it can capture intricate features in images, making it suitable for a wide range of classification tasks. Additionally, it has been pre-trained on large-scale datasets like ImageNet, which contributes to its ability to generalize well to unseen data.
PyTorch is a widely used deep learning framework known for its flexibility and ease of use. By utilizing PyTorch, this project provides an accessible implementation of image classification with ResNet-152, enabling researchers and practitioners to easily experiment with deep learning models for image recognition tasks.
Potential future improvements for this project include:
- Integration with web or mobile applications for real-time image classification.
- Fine-tuning the pre-trained model on custom datasets to improve performance on specific domains.
- Exploring other pre-trained models or architectures for comparison and benchmarking.
- The pre-trained ResNet-152 model is provided by PyTorch.
- Here used one common source PyTorch torchvision repository, where they provided a imagenet_classes.txt file containing the class labels for the ImageNet dataset.