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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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- 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: project_4_transfer_learning |
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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: 0.64375 |
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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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# project_4_transfer_learning |
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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: 1.1429 |
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- Accuracy: 0.6438 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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 | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 2.0754 | 1.0 | 10 | 0.125 | 2.0725 | |
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| 2.0459 | 2.0 | 20 | 0.2625 | 2.0286 | |
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| 1.968 | 3.0 | 30 | 0.3 | 1.9506 | |
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| 1.8311 | 4.0 | 40 | 0.4188 | 1.8060 | |
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| 1.6911 | 5.0 | 50 | 0.4313 | 1.6814 | |
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| 1.5677 | 6.0 | 60 | 0.4313 | 1.5851 | |
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| 1.4801 | 7.0 | 70 | 0.4813 | 1.5169 | |
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| 1.4033 | 8.0 | 80 | 0.4813 | 1.4614 | |
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| 1.3435 | 9.0 | 90 | 0.475 | 1.4358 | |
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| 1.3054 | 10.0 | 100 | 0.525 | 1.4292 | |
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| 1.2532 | 11.0 | 110 | 0.5188 | 1.3942 | |
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| 1.2178 | 12.0 | 120 | 0.5312 | 1.3684 | |
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| 1.1857 | 13.0 | 130 | 0.5062 | 1.3599 | |
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| 1.1558 | 14.0 | 140 | 0.5312 | 1.2992 | |
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| 1.1118 | 15.0 | 150 | 0.5375 | 1.3217 | |
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| 1.0967 | 16.0 | 160 | 0.525 | 1.3177 | |
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| 1.0671 | 17.0 | 170 | 0.5312 | 1.3420 | |
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| 1.0635 | 18.0 | 180 | 0.5062 | 1.3319 | |
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| 1.044 | 19.0 | 190 | 0.5813 | 1.2977 | |
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| 1.037 | 20.0 | 200 | 0.5125 | 1.3127 | |
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| 1.0743 | 21.0 | 210 | 1.2062 | 0.6062 | |
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| 1.0454 | 22.0 | 220 | 1.1564 | 0.65 | |
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| 1.0457 | 23.0 | 230 | 1.1484 | 0.6312 | |
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| 1.0246 | 24.0 | 240 | 1.1470 | 0.6312 | |
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| 0.9859 | 25.0 | 250 | 1.1200 | 0.6438 | |
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| 0.9885 | 26.0 | 260 | 1.1331 | 0.6375 | |
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| 0.9823 | 27.0 | 270 | 1.1069 | 0.6562 | |
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| 0.9412 | 28.0 | 280 | 1.1163 | 0.6375 | |
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| 0.9172 | 29.0 | 290 | 1.1192 | 0.6375 | |
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| 0.9334 | 30.0 | 300 | 1.1573 | 0.6 | |
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### Framework versions |
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- Transformers 4.33.2 |
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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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