| # 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 |