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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-weather-images-classification |
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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: data |
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split: train |
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args: data |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9339762611275965 |
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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-weather-images-classification |
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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) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2255 |
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- Accuracy: 0.9340 |
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- Weighted f1: 0.9341 |
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- Micro f1: 0.9340 |
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- Macro f1: 0.9372 |
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- Weighted recall: 0.9340 |
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- Micro recall: 0.9340 |
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- Macro recall: 0.9354 |
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- Weighted precision: 0.9347 |
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- Micro precision: 0.9340 |
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- Macro precision: 0.9398 |
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## Model description |
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This is a classification model of weather images. |
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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/Weather%20Images/Weather_Images_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/jehanbhathena/weather-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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| 2.4333 | 1.0 | 337 | 0.3374 | 0.9036 | 0.9028 | 0.9036 | 0.9080 | 0.9036 | 0.9036 | 0.9002 | 0.9088 | 0.9036 | 0.9234 | |
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| 0.4422 | 2.0 | 674 | 0.2504 | 0.9228 | 0.9226 | 0.9228 | 0.9285 | 0.9228 | 0.9228 | 0.9273 | 0.9248 | 0.9228 | 0.9318 | |
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| 0.1051 | 3.0 | 1011 | 0.2255 | 0.9340 | 0.9341 | 0.9340 | 0.9372 | 0.9340 | 0.9340 | 0.9354 | 0.9347 | 0.9340 | 0.9398 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.12.1 |
