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