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--- |
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license: apache-2.0 |
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base_model: WinKawaks/vit-small-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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: msi-vit-small-1218-2 |
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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: 0.6164383561643836 |
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- name: F1 |
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type: f1 |
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value: 0.3276157804459692 |
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- name: Precision |
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type: precision |
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value: 0.6840624200562804 |
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- name: Recall |
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type: recall |
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value: 0.2153846153846154 |
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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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# msi-vit-small-1218-2 |
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This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co./WinKawaks/vit-small-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3372 |
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- Accuracy: 0.6164 |
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- F1: 0.3276 |
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- Precision: 0.6841 |
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- Recall: 0.2154 |
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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-06 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4367 | 1.0 | 1008 | 0.6603 | 0.6572 | 0.5313 | 0.6530 | 0.4478 | |
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| 0.2161 | 2.0 | 2016 | 0.8021 | 0.6329 | 0.4989 | 0.6118 | 0.4211 | |
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| 0.169 | 3.0 | 3024 | 1.4062 | 0.6010 | 0.2653 | 0.6592 | 0.1661 | |
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| 0.1543 | 4.0 | 4032 | 1.1498 | 0.6259 | 0.3670 | 0.6903 | 0.2499 | |
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| 0.1534 | 5.0 | 5040 | 1.5067 | 0.6208 | 0.3519 | 0.6808 | 0.2373 | |
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| 0.1596 | 6.0 | 6048 | 0.8837 | 0.6504 | 0.6505 | 0.5744 | 0.7498 | |
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| 0.1504 | 7.0 | 7056 | 1.0030 | 0.6302 | 0.4192 | 0.6580 | 0.3075 | |
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| 0.1795 | 8.0 | 8064 | 1.3908 | 0.5953 | 0.2950 | 0.6041 | 0.1952 | |
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| 0.1636 | 9.0 | 9072 | 1.1040 | 0.6290 | 0.4619 | 0.6230 | 0.3671 | |
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| 0.1629 | 10.0 | 10080 | 1.3372 | 0.6164 | 0.3276 | 0.6841 | 0.2154 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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