
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ryan_model3272024

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the properties dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2636
- Ordinal Mae: 0.5544
- Ordinal Accuracy: 0.5810
- Na Accuracy: 0.7915

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Ordinal Mae | Ordinal Accuracy | Na Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|:-----------:|
| 0.3524        | 0.05  | 100  | 0.3400          | 0.8905      | 0.3875           | 0.7587      |
| 0.2683        | 0.09  | 200  | 0.3671          | 0.7306      | 0.4892           | 0.6236      |
| 0.3314        | 0.14  | 300  | 0.3450          | 0.8077      | 0.4013           | 0.6969      |
| 0.2747        | 0.19  | 400  | 0.2813          | 0.6106      | 0.5423           | 0.7896      |
| 0.3247        | 0.23  | 500  | 0.3144          | 0.7256      | 0.4525           | 0.7104      |
| 0.3612        | 0.28  | 600  | 0.3075          | 0.6416      | 0.4984           | 0.7587      |
| 0.3031        | 0.32  | 700  | 0.2785          | 0.5720      | 0.5556           | 0.7896      |
| 0.2866        | 0.37  | 800  | 0.2878          | 0.5348      | 0.5776           | 0.7336      |
| 0.2927        | 0.42  | 900  | 0.2689          | 0.5855      | 0.5574           | 0.7973      |
| 0.3003        | 0.46  | 1000 | 0.2636          | 0.5544      | 0.5810           | 0.7915      |
| 0.2522        | 0.51  | 1100 | 0.3009          | 0.5651      | 0.5444           | 0.8571      |
| 0.262         | 0.56  | 1200 | 0.2790          | 0.5203      | 0.5802           | 0.8301      |
| 0.2139        | 0.6   | 1300 | 0.2653          | 0.5626      | 0.5493           | 0.7510      |
| 0.2655        | 0.65  | 1400 | 0.2760          | 0.6107      | 0.5426           | 0.7124      |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

poor fair good great excellent not applicable