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# resnet-101-finetuned-CivilEng11k

This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5490
- Accuracy: 0.8542

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 320
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.81  | 3    | 1.0724          | 0.5729   |
| No log        | 1.89  | 7    | 0.9717          | 0.6542   |
| 1.0293        | 2.97  | 11   | 0.8594          | 0.6678   |
| 1.0293        | 3.78  | 14   | 0.7830          | 0.7017   |
| 1.0293        | 4.86  | 18   | 0.6764          | 0.7593   |
| 0.78          | 5.95  | 22   | 0.6072          | 0.7831   |
| 0.78          | 6.76  | 25   | 0.5745          | 0.8339   |
| 0.78          | 7.84  | 29   | 0.5489          | 0.8508   |
| 0.6037        | 8.11  | 30   | 0.5490          | 0.8542   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3

crazing live_knot steel_defect