In the world of artificial intelligence, image classification is an art, and with the emergence of models like the Vision Transformer (ViT), it has become more accessible and efficient. This blog will guide you through the process of fine-tuning a pre-trained model for classifying cats and dogs using the googlevit-base-patch16-224-in21k architecture. So, fasten your seatbelt as we embark on this journey!
Model Overview
The model we’re working with is a fine-tuned version that has been adapted for the cats_vs_dogs dataset. Here are the results:
- Loss: 0.0202
- Accuracy: 0.9935
Setup: Training Procedure
Before you start, here are the training hyperparameters utilized:
- Learning Rate: 0.0002
- Train Batch Size: 64
- Eval Batch Size: 64
- Seed: 1337
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- LR Scheduler Type: Linear
- Number of Epochs: 5.0
- Mixed Precision Training: Native AMP
Understanding the Training Results
The training and evaluation results can be likened to a marathon training session. Just as a runner needs to gradually improve their pace and endurance, our model also refined its ability to classify images through various epochs. Below is a summary of its performance:
Training Loss Epoch Step Validation Loss Accuracy
0.064 1.0 311 0.0483 0.9849
0.0622 2.0 622 0.0275 0.9903
0.0366 3.0 933 0.0262 0.9917
0.0294 4.0 1244 0.0219 0.9932
0.0161 5.0 1555 0.0202 0.9935Troubleshooting Troublesome Tails
Although we’ve set everything in motion, sometimes unexpected challenges may arise. Here are some troubleshooting steps you can take:
- High Loss Values: If the validation loss doesn’t decrease over epochs, try adjusting the learning rate or increasing the number of epochs.
- Overfitting: If you notice a decline in validation accuracy compared to training accuracy, consider applying regularization techniques or using data augmentation.
- Model Not Training: Ensure all dependencies like PyTorch and Transformers are correctly installed and compatible.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
This blog illuminated the magnificent world of image classification using the Vision Transformer, leading to a near-perfect accuracy with classy cats and daring dogs. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
