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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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- image-classification |
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- clothes-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-clothes-classification |
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results: [] |
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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-clothes-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 DBQ/Matches.Fashion.Product.prices.France dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2328 |
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- Accuracy: 0.6395 |
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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: 8 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 1.0975 | 0.5714 | 500 | 1.2619 | 0.6111 | |
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| 0.8315 | 1.1429 | 1000 | 1.3133 | 0.6322 | |
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| 0.7266 | 1.7143 | 1500 | 1.2077 | 0.6356 | |
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| 0.5451 | 2.2857 | 2000 | 1.2895 | 0.6556 | |
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| 0.4287 | 2.8571 | 2500 | 1.2736 | 0.6644 | |
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| 0.2554 | 3.4286 | 3000 | 1.3801 | 0.6767 | |
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| 0.2265 | 4.0 | 3500 | 1.4924 | 0.6656 | |
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| 0.0738 | 4.5714 | 4000 | 1.6321 | 0.68 | |
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| 0.0761 | 5.1429 | 4500 | 1.6676 | 0.6767 | |
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| 0.0251 | 5.7143 | 5000 | 1.6911 | 0.7056 | |
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| 0.0147 | 6.2857 | 5500 | 1.7312 | 0.7 | |
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| 0.0051 | 6.8571 | 6000 | 1.7282 | 0.6922 | |
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| 0.0028 | 7.4286 | 6500 | 1.7679 | 0.6967 | |
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| 0.0017 | 8.0 | 7000 | 1.7642 | 0.6989 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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