This project focuses on using transfer learning to classify burning liquids in static flame images. The goal is to fine-tune a pretrained ResNet-34 model and evaluate its performance comprehensively.
- Data Source: [Burning Liquid Dataset](https://doi.org/10.1007/s10973-021-10903-2 – Supplementary Information, File #2)
- Description: The dataset comprises 3000 high-resolution flame images of burning ethanol, pentane, and propanol.
- Data Preparation: Images should be extracted into a
datafolder and organized into subfolders based on their respective classes (ethanol, pentane, propanol).
- Pretrained Model: ResNet-34 from torchvision
- Model Adaptation: The final classification output layer is modified to accommodate the three burning liquid classes.
- Fine-tuning: The pretrained model is fine-tuned using the custom dataset.
- Hyperparameter Exploration: Experimented with learning rates, batch sizes, and training epochs.
- Layer Freezing: Implemented layer freezing techniques to optimize model adaptation.
- Internal Representation: Visualized feature maps from different convolutional layers within the model.
- Insight Gathering: Gained insights into the model's internal representation using PyTorch hooks.
- Visualization: Produced image grids displaying output activations for selected layers.
- Performance Evaluation: Reported the accuracy of the fine-tuned model on the testing set and compare it with the baseline ResNet-34 model.
- Confusion Matrix: Generated a confusion matrix to analyze inter-class error rates.
- Optional Metrics: Utilized the sklearn.metrics module for comprehensive classification performance metrics.
- Precision-Recall Curves: Created precision-recall curves for each class to assess binary classification performance.
- Martinka, J., Neˇcas, A., Rantuch, P. The recognition of selected burning liquids by convolutional neural networks under laboratory conditions. J Therm Anal Calorim 147, 5787-5799 (2022).
- TorchVision ResNet Source Code