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--- |
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base_model: MBZUAI/swiftformer-xs |
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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: swiftformer-xs |
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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: 0.73 |
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- name: Precision |
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type: precision |
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value: 0.5329 |
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- name: Recall |
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type: recall |
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value: 0.73 |
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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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# swiftformer-xs |
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This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co./MBZUAI/swiftformer-xs) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5838 |
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- Accuracy: 0.73 |
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- Precision: 0.5329 |
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- Recall: 0.73 |
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- F1 Score: 0.6161 |
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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: 15 |
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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 | 0.6209 | 0.7292 | 0.6273 | 0.7292 | 0.6259 | |
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| No log | 2.0 | 8 | 0.7514 | 0.3875 | 0.5947 | 0.3875 | 0.3910 | |
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| No log | 3.0 | 12 | 0.7574 | 0.3292 | 0.6284 | 0.3292 | 0.2679 | |
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| 0.6558 | 4.0 | 16 | 0.7080 | 0.5042 | 0.6591 | 0.5042 | 0.5279 | |
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| 0.6558 | 5.0 | 20 | 0.6566 | 0.6458 | 0.6859 | 0.6458 | 0.6604 | |
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| 0.6558 | 6.0 | 24 | 0.6509 | 0.65 | 0.6810 | 0.65 | 0.6621 | |
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| 0.6558 | 7.0 | 28 | 0.6438 | 0.6375 | 0.6639 | 0.6375 | 0.6484 | |
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| 0.5697 | 8.0 | 32 | 0.6455 | 0.65 | 0.6845 | 0.65 | 0.6631 | |
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| 0.5697 | 9.0 | 36 | 0.6480 | 0.6458 | 0.6823 | 0.6458 | 0.6596 | |
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| 0.5697 | 10.0 | 40 | 0.6438 | 0.6542 | 0.6867 | 0.6542 | 0.6667 | |
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| 0.5697 | 11.0 | 44 | 0.6366 | 0.6583 | 0.6924 | 0.6583 | 0.6711 | |
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| 0.5232 | 12.0 | 48 | 0.6391 | 0.6625 | 0.7016 | 0.6625 | 0.6764 | |
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| 0.5232 | 13.0 | 52 | 0.6386 | 0.6583 | 0.6924 | 0.6583 | 0.6711 | |
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| 0.5232 | 14.0 | 56 | 0.6403 | 0.6667 | 0.7038 | 0.6667 | 0.68 | |
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| 0.5068 | 15.0 | 60 | 0.6459 | 0.6708 | 0.7131 | 0.6708 | 0.6851 | |
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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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