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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-resnet18 |
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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: 0.7618322547900013 |
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- name: F1 |
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type: f1 |
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value: 0.7033583563808773 |
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- name: Precision |
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type: precision |
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value: 0.7032472149798531 |
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- name: Recall |
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type: recall |
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value: 0.7034695329170948 |
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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-resnet18 |
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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: 0.4869 |
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- Accuracy: 0.7618 |
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- F1: 0.7034 |
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- Precision: 0.7032 |
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- Recall: 0.7035 |
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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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| 0.6225 | 1.0 | 1970 | 0.6131 | 0.6718 | 0.5197 | 0.6298 | 0.4424 | |
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| 0.5749 | 2.0 | 3941 | 0.5577 | 0.7138 | 0.6061 | 0.6771 | 0.5486 | |
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| 0.5506 | 3.0 | 5911 | 0.5347 | 0.7355 | 0.6367 | 0.7096 | 0.5773 | |
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| 0.5304 | 4.0 | 7882 | 0.5114 | 0.7501 | 0.6615 | 0.7250 | 0.6082 | |
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| 0.5196 | 5.0 | 9852 | 0.5057 | 0.7503 | 0.6932 | 0.6838 | 0.7028 | |
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| 0.5125 | 6.0 | 11823 | 0.4920 | 0.7610 | 0.6983 | 0.7078 | 0.6890 | |
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| 0.5016 | 7.0 | 13793 | 0.4929 | 0.7578 | 0.7055 | 0.6890 | 0.7228 | |
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| 0.4871 | 8.0 | 15764 | 0.4796 | 0.7683 | 0.7042 | 0.7222 | 0.6870 | |
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| 0.5069 | 9.0 | 17734 | 0.4766 | 0.7743 | 0.6996 | 0.7512 | 0.6545 | |
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| 0.5059 | 10.0 | 19700 | 0.4869 | 0.7618 | 0.7034 | 0.7032 | 0.7035 | |
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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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