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--- |
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license: apache-2.0 |
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base_model: microsoft/resnet-101 |
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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-101-CivilEng11k_3Classes |
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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: 1.0 |
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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-101-CivilEng11k_3Classes |
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0587 |
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- Accuracy: 1.0 |
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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: 0.0002 |
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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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- 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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| 1.0885 | 1.0 | 37 | 0.8955 | 0.4305 | |
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| 0.6832 | 2.0 | 74 | 0.4990 | 0.8475 | |
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| 0.2591 | 3.0 | 111 | 0.0587 | 1.0 | |
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| 0.024 | 4.0 | 148 | 0.0026 | 1.0 | |
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| 0.005 | 5.0 | 185 | 0.0007 | 1.0 | |
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| 0.0121 | 6.0 | 222 | 0.0005 | 1.0 | |
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| 0.0214 | 7.0 | 259 | 0.0003 | 1.0 | |
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| 0.0035 | 8.0 | 296 | 0.0002 | 1.0 | |
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| 0.0026 | 9.0 | 333 | 0.0002 | 1.0 | |
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| 0.0054 | 10.0 | 370 | 0.0002 | 1.0 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 1.12.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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