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--- |
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library_name: transformers |
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license: other |
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base_model: apple/mobilevit-x-small |
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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: mobilevit-x-small |
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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: 0.995850622406639 |
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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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# mobilevit-x-small |
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This model is a fine-tuned version of [apple/mobilevit-x-small](https://huggingface.co/apple/mobilevit-x-small) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0196 |
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- Accuracy: 0.9959 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6911 | 1.0 | 34 | 0.6932 | 0.5083 | |
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| 0.6584 | 2.0 | 68 | 0.6287 | 0.7510 | |
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| 0.5388 | 3.0 | 102 | 0.4852 | 0.8734 | |
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| 0.3891 | 4.0 | 136 | 0.3065 | 0.9357 | |
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| 0.2915 | 5.0 | 170 | 0.2005 | 0.9647 | |
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| 0.2319 | 6.0 | 204 | 0.1498 | 0.9689 | |
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| 0.2038 | 7.0 | 238 | 0.1228 | 0.9710 | |
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| 0.1641 | 8.0 | 272 | 0.0892 | 0.9855 | |
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| 0.1525 | 9.0 | 306 | 0.0778 | 0.9834 | |
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| 0.1584 | 10.0 | 340 | 0.0565 | 0.9896 | |
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| 0.1194 | 11.0 | 374 | 0.0491 | 0.9917 | |
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| 0.1222 | 12.0 | 408 | 0.0436 | 0.9896 | |
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| 0.1229 | 13.0 | 442 | 0.0360 | 0.9979 | |
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| 0.1334 | 14.0 | 476 | 0.0326 | 0.9959 | |
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| 0.122 | 15.0 | 510 | 0.0425 | 0.9896 | |
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| 0.096 | 16.0 | 544 | 0.0315 | 0.9959 | |
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| 0.0989 | 17.0 | 578 | 0.0303 | 0.9938 | |
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| 0.1085 | 18.0 | 612 | 0.0262 | 0.9959 | |
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| 0.0957 | 19.0 | 646 | 0.0232 | 0.9959 | |
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| 0.1129 | 20.0 | 680 | 0.0266 | 0.9959 | |
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| 0.0843 | 21.0 | 714 | 0.0234 | 0.9959 | |
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| 0.0868 | 22.0 | 748 | 0.0217 | 0.9959 | |
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| 0.0867 | 23.0 | 782 | 0.0233 | 0.9959 | |
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| 0.0947 | 24.0 | 816 | 0.0204 | 0.9959 | |
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| 0.0786 | 25.0 | 850 | 0.0199 | 0.9959 | |
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| 0.1009 | 26.0 | 884 | 0.0212 | 0.9959 | |
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| 0.0785 | 27.0 | 918 | 0.0204 | 0.9959 | |
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| 0.0811 | 28.0 | 952 | 0.0180 | 0.9959 | |
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| 0.0883 | 29.0 | 986 | 0.0193 | 0.9959 | |
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| 0.0988 | 30.0 | 1020 | 0.0196 | 0.9959 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.19.1 |
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