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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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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: Action_all_10_class |
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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: Action_small_dataset |
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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: 0.8517382413087935 |
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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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# Action_all_10_class |
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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 Action_small_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4725 |
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- Accuracy: 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: 0.0001 |
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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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- mixed_precision_training: Native AMP |
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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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| 1.2411 | 0.36 | 100 | 1.1517 | 0.7546 | |
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| 0.8932 | 0.72 | 200 | 0.7856 | 0.7975 | |
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| 0.6907 | 1.08 | 300 | 0.6636 | 0.8221 | |
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| 0.5841 | 1.43 | 400 | 0.6388 | 0.8160 | |
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| 0.5425 | 1.79 | 500 | 0.5871 | 0.8436 | |
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| 0.5929 | 2.15 | 600 | 0.5646 | 0.8211 | |
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| 0.4406 | 2.51 | 700 | 0.5439 | 0.8405 | |
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| 0.4541 | 2.87 | 800 | 0.5318 | 0.8415 | |
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| 0.3835 | 3.23 | 900 | 0.5225 | 0.8344 | |
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| 0.3924 | 3.58 | 1000 | 0.5515 | 0.8303 | |
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| 0.5741 | 3.94 | 1100 | 0.5519 | 0.8252 | |
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| 0.3991 | 4.3 | 1200 | 0.4990 | 0.8446 | |
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| 0.4732 | 4.66 | 1300 | 0.5336 | 0.8303 | |
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| 0.3324 | 5.02 | 1400 | 0.5351 | 0.8282 | |
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| 0.3433 | 5.38 | 1500 | 0.4725 | 0.8517 | |
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| 0.2187 | 5.73 | 1600 | 0.5042 | 0.8466 | |
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| 0.2952 | 6.09 | 1700 | 0.5240 | 0.8548 | |
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| 0.2687 | 6.45 | 1800 | 0.5523 | 0.8364 | |
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| 0.3111 | 6.81 | 1900 | 0.5304 | 0.8497 | |
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| 0.2431 | 7.17 | 2000 | 0.5104 | 0.8569 | |
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| 0.3265 | 7.53 | 2100 | 0.5085 | 0.8691 | |
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| 0.2595 | 7.89 | 2200 | 0.5015 | 0.8569 | |
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| 0.1825 | 8.24 | 2300 | 0.4920 | 0.8620 | |
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| 0.2602 | 8.6 | 2400 | 0.5016 | 0.8620 | |
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| 0.2628 | 8.96 | 2500 | 0.4746 | 0.8681 | |
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| 0.1024 | 9.32 | 2600 | 0.4818 | 0.8691 | |
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| 0.1468 | 9.68 | 2700 | 0.4765 | 0.8681 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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