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README.md
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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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model-index:
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- name: 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: 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: 0.6425188074672611
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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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# 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: 0.9403
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- Accuracy: 0.6425
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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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| 1.4726 | 1.0 | 252 | 1.3072 | 0.5068 |
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| 1.2683 | 2.0 | 505 | 1.0996 | 0.5865 |
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| 1.2177 | 3.0 | 757 | 1.0444 | 0.6096 |
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| 1.1636 | 4.0 | 1010 | 1.0185 | 0.6096 |
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| 1.1372 | 5.0 | 1262 | 0.9945 | 0.6205 |
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| 1.113 | 6.0 | 1515 | 0.9703 | 0.6342 |
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| 1.0734 | 7.0 | 1767 | 0.9574 | 0.6333 |
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| 1.0501 | 8.0 | 2020 | 0.9503 | 0.6375 |
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| 1.0361 | 9.0 | 2272 | 0.9488 | 0.6389 |
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| 1.0302 | 9.98 | 2520 | 0.9403 | 0.6425 |
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### Framework versions
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- Transformers 4.30.0
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.13.3
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