This repository hosts a notebook featuring an in-depth analysis of 2 Pytorch EfficientNet models, along with an app deployment using Gradio. The following models were meticulously evaluated:
- EffNetB0
- EffNetB2
The dataset used has been downloaded from Kaggle and contains a set of 525 bird species. 84635 training images, 2625 test images(5 images per species) and 2625 validation images(5 images per species).
The app can be tested in Hugging Face (.py files hosted there) following this link. Feel free to ⭐ and clone this repo 😉
In the first stage, a set of helper functions was created in order to easily perform the modeling and prediction
- Set seed: Set random seeds for PyTorch operations, both on the CPU and the GPU
- DataLoaders: Create data loaders for training and testing datasets using PyTorch's DataLoader class
- Writer: SummaryWriter object for logging experiments and metrics in TensorBoard
- Training and Testing: Several functions for training and testing a PyTorch model
- Plots: Several plots including loss curve, predictions, and images
The first approach was to train 2 Pytorch EfficientNet models (EffNetB0, EffNetB2) with 5 and 10 epochs using the pre-trained model weights of EffNetB0 for the DataLoaders in order to establish a baseline. The EffNetB2 with 10 epochs showed the best performance above 93% on the test set.
Then the EffNetB2 with 10 epochs was trained again but this time using the pretrained model weights of EffNetB2 for the DataLoaders. This time an accuracy above 95% on the test set and above 93% on the validation set was achieved.
The last step was to deploy an app hosted in Hugging Face using Gradio. This app can be tested with available sample images or with your own ones.
