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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: vit-weld-classify |
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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: 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: 0.6894977168949772 |
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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-weld-classify |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7966 |
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- Accuracy: 0.6895 |
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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: 18 |
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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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| 0.8686 | 0.8130 | 100 | 0.7966 | 0.6895 | |
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| 0.6935 | 1.6260 | 200 | 1.2217 | 0.5068 | |
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| 0.4225 | 2.4390 | 300 | 0.9592 | 0.6210 | |
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| 0.2586 | 3.2520 | 400 | 1.3123 | 0.5936 | |
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| 0.237 | 4.0650 | 500 | 0.8075 | 0.6986 | |
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| 0.2658 | 4.8780 | 600 | 1.0878 | 0.6210 | |
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| 0.1904 | 5.6911 | 700 | 1.1048 | 0.7169 | |
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| 0.0964 | 6.5041 | 800 | 1.3602 | 0.6849 | |
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| 0.0474 | 7.3171 | 900 | 1.1331 | 0.7671 | |
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| 0.1179 | 8.1301 | 1000 | 1.1228 | 0.7306 | |
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| 0.0447 | 8.9431 | 1100 | 1.2609 | 0.7397 | |
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| 0.0043 | 9.7561 | 1200 | 1.1746 | 0.7763 | |
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| 0.1059 | 10.5691 | 1300 | 1.1867 | 0.7763 | |
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| 0.0026 | 11.3821 | 1400 | 1.2890 | 0.7534 | |
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| 0.0039 | 12.1951 | 1500 | 1.3283 | 0.7580 | |
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| 0.002 | 13.0081 | 1600 | 1.1871 | 0.7671 | |
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| 0.0019 | 13.8211 | 1700 | 1.1643 | 0.7900 | |
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| 0.0264 | 14.6341 | 1800 | 1.1537 | 0.7900 | |
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| 0.0015 | 15.4472 | 1900 | 1.1821 | 0.7945 | |
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| 0.0015 | 16.2602 | 2000 | 1.1962 | 0.7900 | |
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| 0.0014 | 17.0732 | 2100 | 1.2036 | 0.7900 | |
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| 0.0014 | 17.8862 | 2200 | 1.2067 | 0.7900 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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