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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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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: finetuned-electrical-images |
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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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# finetuned-electrical-images |
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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 Electrical_components(VIT) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3726 |
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- Accuracy: 0.8861 |
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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: 10 |
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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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| 0.7116 | 0.4651 | 100 | 0.6399 | 0.7921 | |
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| 0.6953 | 0.9302 | 200 | 0.5589 | 0.8086 | |
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| 0.4078 | 1.3953 | 300 | 0.4946 | 0.8399 | |
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| 0.5852 | 1.8605 | 400 | 0.4872 | 0.8399 | |
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| 0.4993 | 2.3256 | 500 | 0.4687 | 0.8597 | |
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| 0.4479 | 2.7907 | 600 | 0.3986 | 0.8845 | |
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| 0.4101 | 3.2558 | 700 | 0.4385 | 0.8729 | |
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| 0.283 | 3.7209 | 800 | 0.4413 | 0.8762 | |
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| 0.3959 | 4.1860 | 900 | 0.4121 | 0.8729 | |
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| 0.318 | 4.6512 | 1000 | 0.4397 | 0.8696 | |
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| 0.2401 | 5.1163 | 1100 | 0.4887 | 0.8680 | |
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| 0.1273 | 5.5814 | 1200 | 0.4224 | 0.8663 | |
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| 0.1101 | 6.0465 | 1300 | 0.4378 | 0.8779 | |
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| 0.1773 | 6.5116 | 1400 | 0.3730 | 0.8845 | |
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| 0.2248 | 6.9767 | 1500 | 0.3726 | 0.8861 | |
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| 0.0987 | 7.4419 | 1600 | 0.4398 | 0.8845 | |
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| 0.16 | 7.9070 | 1700 | 0.4171 | 0.8828 | |
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| 0.1224 | 8.3721 | 1800 | 0.4336 | 0.8878 | |
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| 0.2111 | 8.8372 | 1900 | 0.3948 | 0.8944 | |
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| 0.112 | 9.3023 | 2000 | 0.4004 | 0.8944 | |
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| 0.0962 | 9.7674 | 2100 | 0.4092 | 0.8927 | |
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### Framework versions |
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- Transformers 4.40.1 |
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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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