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--- |
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license: apache-2.0 |
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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-50-bottomCleanedData |
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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: 0.8342792281498297 |
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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-50-bottomCleanedData |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4809 |
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- Accuracy: 0.8343 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 7 |
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- total_train_batch_size: 56 |
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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: 0.01 |
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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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| 1.3235 | 1.0 | 141 | 1.3266 | 0.5096 | |
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| 1.1546 | 2.0 | 283 | 1.1380 | 0.5153 | |
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| 0.9412 | 2.99 | 424 | 0.8690 | 0.6515 | |
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| 0.8539 | 4.0 | 566 | 0.6672 | 0.7594 | |
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| 0.7967 | 4.99 | 707 | 0.6256 | 0.7503 | |
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| 0.7679 | 6.0 | 849 | 0.5357 | 0.8229 | |
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| 0.7265 | 7.0 | 991 | 0.5698 | 0.7832 | |
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| 0.7395 | 8.0 | 1132 | 0.5125 | 0.8161 | |
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| 0.7029 | 9.0 | 1274 | 0.4993 | 0.8150 | |
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| 0.7275 | 9.96 | 1410 | 0.4809 | 0.8343 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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