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--- |
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license: other |
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base_model: apple/mobilevit-xx-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-xx-small-finetuned-eurosat |
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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: 0.9507407407407408 |
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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-xx-small-finetuned-eurosat |
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This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co./apple/mobilevit-xx-small) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1926 |
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- Accuracy: 0.9507 |
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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: 10 |
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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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| 1.5074 | 1.0 | 190 | 1.3433 | 0.7078 | |
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| 0.9398 | 2.0 | 380 | 0.7177 | 0.85 | |
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| 0.7035 | 3.0 | 570 | 0.4252 | 0.9070 | |
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| 0.5435 | 4.0 | 760 | 0.3080 | 0.9281 | |
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| 0.5007 | 5.0 | 950 | 0.2465 | 0.9389 | |
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| 0.4533 | 6.0 | 1140 | 0.2291 | 0.9444 | |
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| 0.3961 | 7.0 | 1330 | 0.1991 | 0.9496 | |
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| 0.3949 | 8.0 | 1520 | 0.1926 | 0.9507 | |
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| 0.4302 | 9.0 | 1710 | 0.1928 | 0.95 | |
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| 0.4061 | 10.0 | 1900 | 0.1931 | 0.9463 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.1 |
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- Tokenizers 0.13.3 |
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