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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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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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model-index: |
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- name: vit-base-patch16-224 |
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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: validation |
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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: 0.8566666666666667 |
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- name: Precision |
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type: precision |
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value: 0.8522571872571872 |
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- name: Recall |
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type: recall |
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value: 0.8566666666666667 |
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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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# vit-base-patch16-224 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4410 |
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- Accuracy: 0.8567 |
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- Precision: 0.8523 |
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- Recall: 0.8567 |
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- F1 Score: 0.8517 |
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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: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 4 | 0.5841 | 0.7333 | 0.6770 | 0.7333 | 0.6479 | |
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| No log | 2.0 | 8 | 0.5727 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 3.0 | 12 | 0.6089 | 0.7208 | 0.7222 | 0.7208 | 0.7215 | |
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| No log | 4.0 | 16 | 0.5332 | 0.7458 | 0.7205 | 0.7458 | 0.6727 | |
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| No log | 5.0 | 20 | 0.5314 | 0.7625 | 0.7410 | 0.7625 | 0.7416 | |
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| No log | 6.0 | 24 | 0.5284 | 0.7583 | 0.7486 | 0.7583 | 0.6959 | |
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| No log | 7.0 | 28 | 0.5220 | 0.775 | 0.7700 | 0.775 | 0.7286 | |
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| 0.5564 | 8.0 | 32 | 0.5204 | 0.7833 | 0.7740 | 0.7833 | 0.7481 | |
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| 0.5564 | 9.0 | 36 | 0.5044 | 0.7708 | 0.7616 | 0.7708 | 0.7650 | |
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| 0.5564 | 10.0 | 40 | 0.4845 | 0.8125 | 0.8051 | 0.8125 | 0.7941 | |
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| 0.5564 | 11.0 | 44 | 0.4921 | 0.7833 | 0.7726 | 0.7833 | 0.7757 | |
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| 0.5564 | 12.0 | 48 | 0.4792 | 0.8167 | 0.8098 | 0.8167 | 0.7996 | |
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| 0.5564 | 13.0 | 52 | 0.4825 | 0.8 | 0.7889 | 0.8 | 0.7901 | |
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| 0.5564 | 14.0 | 56 | 0.4987 | 0.8083 | 0.7989 | 0.8083 | 0.8002 | |
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| 0.3176 | 15.0 | 60 | 0.4970 | 0.8208 | 0.8144 | 0.8208 | 0.8050 | |
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| 0.3176 | 16.0 | 64 | 0.5076 | 0.8083 | 0.7983 | 0.8083 | 0.7923 | |
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| 0.3176 | 17.0 | 68 | 0.5227 | 0.8083 | 0.7979 | 0.8083 | 0.7941 | |
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| 0.3176 | 18.0 | 72 | 0.5132 | 0.8042 | 0.7928 | 0.8042 | 0.7905 | |
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| 0.3176 | 19.0 | 76 | 0.5081 | 0.8167 | 0.8087 | 0.8167 | 0.8014 | |
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| 0.3176 | 20.0 | 80 | 0.5140 | 0.8292 | 0.8220 | 0.8292 | 0.8187 | |
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| 0.3176 | 21.0 | 84 | 0.5392 | 0.8125 | 0.8032 | 0.8125 | 0.7977 | |
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| 0.3176 | 22.0 | 88 | 0.5175 | 0.7958 | 0.7829 | 0.7958 | 0.7815 | |
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| 0.1778 | 23.0 | 92 | 0.5109 | 0.8125 | 0.8032 | 0.8125 | 0.7977 | |
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| 0.1778 | 24.0 | 96 | 0.4961 | 0.8292 | 0.8217 | 0.8292 | 0.8213 | |
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| 0.1778 | 25.0 | 100 | 0.5251 | 0.8083 | 0.7979 | 0.8083 | 0.7941 | |
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| 0.1778 | 26.0 | 104 | 0.5192 | 0.8167 | 0.8075 | 0.8167 | 0.8046 | |
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| 0.1778 | 27.0 | 108 | 0.5030 | 0.8333 | 0.8274 | 0.8333 | 0.8286 | |
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| 0.1778 | 28.0 | 112 | 0.5031 | 0.8375 | 0.8310 | 0.8375 | 0.8300 | |
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| 0.1778 | 29.0 | 116 | 0.5164 | 0.8208 | 0.8127 | 0.8208 | 0.8083 | |
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| 0.1109 | 30.0 | 120 | 0.5192 | 0.8208 | 0.8127 | 0.8208 | 0.8083 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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