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--- |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: msi-nat-mini |
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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: 0.6308708414872799 |
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- name: F1 |
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type: f1 |
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value: 0.47632740072381147 |
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- name: Precision |
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type: precision |
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value: 0.6193914388860238 |
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- name: Recall |
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type: recall |
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value: 0.3869512686266613 |
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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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# msi-nat-mini |
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This model was trained from scratch on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8600 |
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- Accuracy: 0.6309 |
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- F1: 0.4763 |
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- Precision: 0.6194 |
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- Recall: 0.3870 |
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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: 1e-06 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.5496 | 1.0 | 2015 | 0.7573 | 0.5955 | 0.4196 | 0.5559 | 0.3369 | |
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| 0.4807 | 2.0 | 4031 | 0.7416 | 0.6309 | 0.4981 | 0.6074 | 0.4222 | |
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| 0.4235 | 3.0 | 6047 | 0.7680 | 0.6325 | 0.5047 | 0.6076 | 0.4317 | |
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| 0.3879 | 4.0 | 8063 | 0.7875 | 0.6339 | 0.4923 | 0.6179 | 0.4092 | |
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| 0.3702 | 5.0 | 10078 | 0.7923 | 0.6383 | 0.5128 | 0.6168 | 0.4388 | |
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| 0.3568 | 6.0 | 12094 | 0.8311 | 0.6313 | 0.4969 | 0.6090 | 0.4197 | |
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| 0.3661 | 7.0 | 14110 | 0.8345 | 0.6316 | 0.4843 | 0.6166 | 0.3987 | |
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| 0.354 | 8.0 | 16126 | 0.8501 | 0.6305 | 0.4800 | 0.6162 | 0.3931 | |
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| 0.3569 | 9.0 | 18141 | 0.8552 | 0.6318 | 0.4809 | 0.6193 | 0.3931 | |
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| 0.3536 | 10.0 | 20150 | 0.8600 | 0.6309 | 0.4763 | 0.6194 | 0.3870 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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