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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: vit-base-patch16-224-in21k-Intel_Images |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9486666666666667 |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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# vit-base-patch16-224-in21k-Intel_Images |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1822 |
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- Accuracy: 0.9487 |
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- F1 |
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- Weighted: 0.9485 |
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- Micro: 0.9487 |
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- Macro: 0.9497 |
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- Recall |
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- Weighted: 0.9487 |
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- Micro: 0.9487 |
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- Macro: 0.9500 |
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- Precision |
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- Weighted: 0.9485 |
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- Micro: 0.9487 |
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- Macro: 0.9496 |
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## Model description |
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This is a multiclass image classification model of different scenery types. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Intel%20Image%20Classification/Intel_ViT.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/puneet6060/intel-image-classification |
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_Sample Images From Dataset:_ |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.2305 | 1.0 | 878 | 0.2362 | 0.9153 | 0.9144 | 0.9153 | 0.9152 | 0.9153 | 0.9153 | 0.9148 | 0.9208 | 0.9153 | 0.9231 | |
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| 0.1136 | 2.0 | 1756 | 0.1785 | 0.9393 | 0.9391 | 0.9393 | 0.9405 | 0.9393 | 0.9393 | 0.9405 | 0.9391 | 0.9393 | 0.9407 | |
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| 0.0435 | 3.0 | 2634 | 0.1822 | 0.9487 | 0.9485 | 0.9487 | 0.9497 | 0.9487 | 0.9487 | 0.9500 | 0.9485 | 0.9487 | 0.9496 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
