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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: weather_classification_ViT |
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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: 0.9679266895761741 |
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- name: Precision |
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type: precision |
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value: 0.9679235596755258 |
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- name: Recall |
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type: recall |
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value: 0.9679266895761741 |
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- name: F1 |
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type: f1 |
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value: 0.9678827379290899 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# weather_classification_ViT |
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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: 0.1268 |
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- Accuracy: 0.9679 |
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- Precision: 0.9679 |
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- Recall: 0.9679 |
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- F1: 0.9679 |
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- Auc: 0.9974 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| 0.2811 | 0.2288 | 100 | 0.3139 | 0.8958 | 0.9147 | 0.8958 | 0.8970 | 0.9903 | |
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| 0.1396 | 0.4577 | 200 | 0.2454 | 0.9278 | 0.9307 | 0.9278 | 0.9282 | 0.9919 | |
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| 0.3761 | 0.6865 | 300 | 0.2952 | 0.9072 | 0.9117 | 0.9072 | 0.9071 | 0.9889 | |
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| 0.2365 | 0.9153 | 400 | 0.1797 | 0.9444 | 0.9447 | 0.9444 | 0.9445 | 0.9940 | |
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| 0.2528 | 1.1442 | 500 | 0.2470 | 0.9278 | 0.9307 | 0.9278 | 0.9278 | 0.9924 | |
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| 0.2364 | 1.3730 | 600 | 0.2448 | 0.9261 | 0.9306 | 0.9261 | 0.9264 | 0.9934 | |
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| 0.34 | 1.6018 | 700 | 0.1986 | 0.9404 | 0.9409 | 0.9404 | 0.9405 | 0.9929 | |
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| 0.2001 | 1.8307 | 800 | 0.1525 | 0.9542 | 0.9548 | 0.9542 | 0.9539 | 0.9960 | |
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| 0.0958 | 2.0595 | 900 | 0.1783 | 0.9507 | 0.9515 | 0.9507 | 0.9505 | 0.9952 | |
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| 0.1862 | 2.2883 | 1000 | 0.1654 | 0.9553 | 0.9558 | 0.9553 | 0.9551 | 0.9952 | |
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| 0.1021 | 2.5172 | 1100 | 0.1654 | 0.9462 | 0.9472 | 0.9462 | 0.9459 | 0.9958 | |
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| 0.1178 | 2.7460 | 1200 | 0.1591 | 0.9525 | 0.9536 | 0.9525 | 0.9523 | 0.9960 | |
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| 0.0474 | 2.9748 | 1300 | 0.1299 | 0.9633 | 0.9635 | 0.9633 | 0.9633 | 0.9975 | |
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| 0.046 | 3.2037 | 1400 | 0.1384 | 0.9628 | 0.9628 | 0.9628 | 0.9627 | 0.9972 | |
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| 0.0294 | 3.4325 | 1500 | 0.1388 | 0.9645 | 0.9644 | 0.9645 | 0.9644 | 0.9969 | |
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| 0.1833 | 3.6613 | 1600 | 0.1346 | 0.9633 | 0.9634 | 0.9633 | 0.9633 | 0.9971 | |
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| 0.0548 | 3.8902 | 1700 | 0.1268 | 0.9679 | 0.9679 | 0.9679 | 0.9679 | 0.9974 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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