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README.md

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+# 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