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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-resnet-18 |
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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: 0.6217151244059268 |
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- name: F1 |
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type: f1 |
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value: 0.5152478617168957 |
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- name: Precision |
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type: precision |
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value: 0.5801734570391287 |
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- name: Recall |
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type: recall |
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value: 0.4633910592025775 |
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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-resnet-18 |
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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: 0.6730 |
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- Accuracy: 0.6217 |
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- F1: 0.5152 |
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- Precision: 0.5802 |
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- Recall: 0.4634 |
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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: 4 |
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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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| 0.5705 | 1.0 | 2015 | 0.6879 | 0.5897 | 0.4460 | 0.5384 | 0.3807 | |
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| 0.5309 | 2.0 | 4031 | 0.6788 | 0.6091 | 0.4859 | 0.5657 | 0.4258 | |
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| 0.5263 | 3.0 | 6047 | 0.7020 | 0.6036 | 0.4322 | 0.5709 | 0.3477 | |
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| 0.496 | 4.0 | 8060 | 0.6730 | 0.6217 | 0.5152 | 0.5802 | 0.4634 | |
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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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