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# resnet-101-finetuned_resnet101-sgd-optimizer-autotags

This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0245
- Accuracy: 0.6781

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7106        | 0.99  | 65   | 2.7615          | 0.1371   |
| 2.5331        | 1.99  | 130  | 2.6702          | 0.1495   |
| 2.2557        | 2.99  | 195  | 2.2049          | 0.2924   |
| 2.0473        | 3.99  | 260  | 2.0434          | 0.3619   |
| 1.6644        | 4.99  | 325  | 1.6585          | 0.4438   |
| 1.5685        | 5.99  | 390  | 1.4183          | 0.5419   |
| 1.377         | 6.99  | 455  | 1.2873          | 0.5981   |
| 1.2441        | 7.99  | 520  | 1.1502          | 0.6362   |
| 1.1983        | 8.99  | 585  | 1.0553          | 0.6657   |
| 1.0988        | 9.99  | 650  | 1.0245          | 0.6781   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

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