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--- |
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license: apache-2.0 |
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base_model: Zetatech/pvt-tiny-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: pvt-tiny-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: 0.7833333333333333 |
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- name: Precision |
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type: precision |
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value: 0.7680555555555556 |
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- name: Recall |
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type: recall |
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value: 0.7833333333333333 |
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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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# pvt-tiny-224 |
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This model is a fine-tuned version of [Zetatech/pvt-tiny-224](https://huggingface.co/Zetatech/pvt-tiny-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4869 |
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- Accuracy: 0.7833 |
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- Precision: 0.7681 |
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- Recall: 0.7833 |
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- F1 Score: 0.7632 |
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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 | 0.5984 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 2.0 | 8 | 0.6103 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 3.0 | 12 | 0.5861 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 4.0 | 16 | 0.5478 | 0.7333 | 0.5378 | 0.7333 | 0.6205 | |
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| No log | 5.0 | 20 | 0.5961 | 0.725 | 0.7119 | 0.725 | 0.7171 | |
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| No log | 6.0 | 24 | 0.5317 | 0.7542 | 0.7261 | 0.7542 | 0.7159 | |
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| No log | 7.0 | 28 | 0.5620 | 0.7458 | 0.7289 | 0.7458 | 0.7342 | |
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| 0.5878 | 8.0 | 32 | 0.5281 | 0.7542 | 0.7316 | 0.7542 | 0.6973 | |
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| 0.5878 | 9.0 | 36 | 0.5434 | 0.7625 | 0.7395 | 0.7625 | 0.7368 | |
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| 0.5878 | 10.0 | 40 | 0.5236 | 0.775 | 0.7658 | 0.775 | 0.7321 | |
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| 0.5878 | 11.0 | 44 | 0.5411 | 0.7542 | 0.7382 | 0.7542 | 0.7429 | |
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| 0.5878 | 12.0 | 48 | 0.5186 | 0.7708 | 0.7507 | 0.7708 | 0.7460 | |
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| 0.5878 | 13.0 | 52 | 0.5194 | 0.7667 | 0.7500 | 0.7667 | 0.7533 | |
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| 0.5878 | 14.0 | 56 | 0.5049 | 0.7875 | 0.7739 | 0.7875 | 0.7621 | |
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| 0.4973 | 15.0 | 60 | 0.5125 | 0.7833 | 0.7691 | 0.7833 | 0.7709 | |
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| 0.4973 | 16.0 | 64 | 0.5000 | 0.7917 | 0.7804 | 0.7917 | 0.7656 | |
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| 0.4973 | 17.0 | 68 | 0.5137 | 0.7583 | 0.7560 | 0.7583 | 0.7571 | |
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| 0.4973 | 18.0 | 72 | 0.4833 | 0.8 | 0.788 | 0.8 | 0.7833 | |
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| 0.4973 | 19.0 | 76 | 0.4929 | 0.7917 | 0.7816 | 0.7917 | 0.7843 | |
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| 0.4973 | 20.0 | 80 | 0.4858 | 0.8042 | 0.7930 | 0.8042 | 0.7887 | |
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| 0.4973 | 21.0 | 84 | 0.4900 | 0.7917 | 0.7777 | 0.7917 | 0.7743 | |
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| 0.4973 | 22.0 | 88 | 0.4886 | 0.7958 | 0.7829 | 0.7958 | 0.7815 | |
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| 0.439 | 23.0 | 92 | 0.4841 | 0.7917 | 0.7778 | 0.7917 | 0.7723 | |
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| 0.439 | 24.0 | 96 | 0.4855 | 0.8 | 0.7883 | 0.8 | 0.7885 | |
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| 0.439 | 25.0 | 100 | 0.4856 | 0.8 | 0.7879 | 0.8 | 0.7869 | |
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| 0.439 | 26.0 | 104 | 0.4839 | 0.8 | 0.7879 | 0.8 | 0.7869 | |
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| 0.439 | 27.0 | 108 | 0.4811 | 0.8 | 0.7879 | 0.8 | 0.7869 | |
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| 0.439 | 28.0 | 112 | 0.4834 | 0.8 | 0.7889 | 0.8 | 0.7901 | |
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| 0.439 | 29.0 | 116 | 0.4839 | 0.8 | 0.7889 | 0.8 | 0.7901 | |
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| 0.4092 | 30.0 | 120 | 0.4838 | 0.8 | 0.7889 | 0.8 | 0.7901 | |
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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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