Transfer Learning Vision Transformer (ViT) - Google 224 ViT Base Patch
Description
This model is a Transfer Learning Vision Transformer (ViT) based on Google's 224 ViT Base Patch architecture. It has been fine-tuned on a dataset consisting of fungal images from Russia, with a specific focus on various fungi and lichen species.
Model Information
- Model Name: Transfer Learning ViT - Google 224 ViT Base Patch
- Model Architecture: Vision Transformer (ViT)
- Base Architecture: Google's 224 ViT Base Patch
- Pre-trained on General ImageNet dataset
- Fine-tuned on: Fungal image dataset from Russia
Performance
- Accuracy: 90.31%
- F1 Score: 86.33%
Training Details
- Training Loss:
- Initial: 1.043200
- Final: 0.116200
- Validation Loss:
- Initial: 0.822428
- Final: 0.335994
- Training Epochs: 10
- Training Runtime: 18575.04 seconds
- Training Samples per Second: 33.327
- Training Steps per Second: 1.042
- Total FLOPs: 4.801 x 10^19
Recommended Use Cases
- Species classification of various fungi and lichen in Russia.
- Fungal biodiversity studies.
- Image recognition tasks related to fungi and lichen species.
Limitations
- The model's performance is optimized for fungal species and may not generalize well to other domains.
- The model may not perform well on images of fungi and lichen species from regions other than Russia.
Model Author
Siddhant Dutta
