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# ryan03302024

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: 0.2044
- Ordinal Mae: 0.4324
- Ordinal Accuracy: 0.6648
- Na Accuracy: 0.8333

## 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: 0.0001
- 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Ordinal Mae | Ordinal Accuracy | Na Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|:-----------:|
| 0.3617        | 0.04  | 100  | 0.3133          | 0.8466      | 0.4174           | 0.8763      |
| 0.2891        | 0.07  | 200  | 0.3022          | 0.7739      | 0.5045           | 0.6993      |
| 0.3163        | 0.11  | 300  | 0.2615          | 0.6167      | 0.5597           | 0.7904      |
| 0.2781        | 0.14  | 400  | 0.2598          | 0.5433      | 0.5936           | 0.8299      |
| 0.2731        | 0.18  | 500  | 0.2613          | 0.5651      | 0.5566           | 0.8454      |
| 0.2926        | 0.22  | 600  | 0.2734          | 0.5305      | 0.5494           | 0.9089      |
| 0.2686        | 0.25  | 700  | 0.2362          | 0.4853      | 0.6250           | 0.7887      |
| 0.2715        | 0.29  | 800  | 0.2454          | 0.4914      | 0.6255           | 0.7715      |
| 0.2459        | 0.32  | 900  | 0.2452          | 0.4763      | 0.6072           | 0.7801      |
| 0.2033        | 0.36  | 1000 | 0.2365          | 0.4967      | 0.6106           | 0.7663      |
| 0.2234        | 0.4   | 1100 | 0.2299          | 0.4947      | 0.6180           | 0.8677      |
| 0.2035        | 0.43  | 1200 | 0.2314          | 0.4744      | 0.6309           | 0.7835      |
| 0.2277        | 0.47  | 1300 | 0.2389          | 0.4649      | 0.6435           | 0.7302      |
| 0.2535        | 0.5   | 1400 | 0.2259          | 0.4509      | 0.6021           | 0.8247      |
| 0.2209        | 0.54  | 1500 | 0.2369          | 0.4507      | 0.6363           | 0.7577      |
| 0.2007        | 0.58  | 1600 | 0.2161          | 0.4272      | 0.6540           | 0.8316      |
| 0.2013        | 0.61  | 1700 | 0.2433          | 0.4326      | 0.6129           | 0.7732      |
| 0.1999        | 0.65  | 1800 | 0.2227          | 0.4460      | 0.6553           | 0.8247      |
| 0.2157        | 0.69  | 1900 | 0.2134          | 0.4728      | 0.6363           | 0.8162      |
| 0.2154        | 0.72  | 2000 | 0.2239          | 0.4734      | 0.5787           | 0.8574      |
| 0.2169        | 0.76  | 2100 | 0.2394          | 0.4392      | 0.6255           | 0.8849      |
| 0.2719        | 0.79  | 2200 | 0.2283          | 0.4324      | 0.6229           | 0.8780      |
| 0.2244        | 0.83  | 2300 | 0.2140          | 0.4483      | 0.6314           | 0.8729      |
| 0.2072        | 0.87  | 2400 | 0.2198          | 0.4330      | 0.6440           | 0.8213      |
| 0.1754        | 0.9   | 2500 | 0.2099          | 0.4198      | 0.6712           | 0.8419      |
| 0.1773        | 0.94  | 2600 | 0.2053          | 0.4105      | 0.6586           | 0.8643      |
| 0.2378        | 0.97  | 2700 | 0.2044          | 0.4324      | 0.6648           | 0.8333      |
| 0.1295        | 1.01  | 2800 | 0.2044          | 0.4016      | 0.6843           | 0.8247      |
| 0.1126        | 1.05  | 2900 | 0.2302          | 0.4025      | 0.6805           | 0.7577      |
| 0.1262        | 1.08  | 3000 | 0.2205          | 0.4017      | 0.6517           | 0.8093      |
| 0.1104        | 1.12  | 3100 | 0.2117          | 0.3931      | 0.6779           | 0.8454      |
| 0.1657        | 1.15  | 3200 | 0.2174          | 0.3890      | 0.6666           | 0.8591      |
| 0.1186        | 1.19  | 3300 | 0.2299          | 0.4013      | 0.6622           | 0.8058      |
| 0.1304        | 1.23  | 3400 | 0.2176          | 0.3801      | 0.6902           | 0.8110      |
| 0.1081        | 1.26  | 3500 | 0.2330          | 0.3867      | 0.6643           | 0.8316      |
| 0.1281        | 1.3   | 3600 | 0.2320          | 0.3954      | 0.6902           | 0.7680      |
| 0.1192        | 1.33  | 3700 | 0.2312          | 0.4109      | 0.6769           | 0.7990      |
| 0.1029        | 1.37  | 3800 | 0.2195          | 0.3870      | 0.6820           | 0.8024      |
| 0.1159        | 1.41  | 3900 | 0.2200          | 0.3860      | 0.6812           | 0.7904      |
| 0.1159        | 1.44  | 4000 | 0.2159          | 0.3712      | 0.6982           | 0.7990      |
| 0.107         | 1.48  | 4100 | 0.2262          | 0.3757      | 0.6905           | 0.8213      |
| 0.1262        | 1.51  | 4200 | 0.2291          | 0.3841      | 0.6835           | 0.8247      |
| 0.1437        | 1.55  | 4300 | 0.2311          | 0.3751      | 0.6923           | 0.8007      |
| 0.0916        | 1.59  | 4400 | 0.2343          | 0.3743      | 0.6792           | 0.8660      |
| 0.1266        | 1.62  | 4500 | 0.2251          | 0.3724      | 0.6861           | 0.8505      |
| 0.1185        | 1.66  | 4600 | 0.2242          | 0.3666      | 0.6902           | 0.8265      |
| 0.1037        | 1.69  | 4700 | 0.2219          | 0.3700      | 0.6846           | 0.8522      |
| 0.1264        | 1.73  | 4800 | 0.2211          | 0.3677      | 0.6892           | 0.8351      |
| 0.1404        | 1.77  | 4900 | 0.2206          | 0.3718      | 0.6946           | 0.7938      |
| 0.1238        | 1.8   | 5000 | 0.2098          | 0.3723      | 0.6948           | 0.8265      |
| 0.0868        | 1.84  | 5100 | 0.2089          | 0.3574      | 0.7025           | 0.8144      |
| 0.0828        | 1.88  | 5200 | 0.2204          | 0.3680      | 0.7031           | 0.7818      |
| 0.0986        | 1.91  | 5300 | 0.2126          | 0.3543      | 0.6982           | 0.8127      |
| 0.0869        | 1.95  | 5400 | 0.2247          | 0.3532      | 0.7108           | 0.8076      |
| 0.1006        | 1.98  | 5500 | 0.2268          | 0.3637      | 0.7028           | 0.8162      |
| 0.0639        | 2.02  | 5600 | 0.2252          | 0.3479      | 0.7069           | 0.8110      |
| 0.0569        | 2.06  | 5700 | 0.2315          | 0.3399      | 0.7167           | 0.8076      |
| 0.0626        | 2.09  | 5800 | 0.2304          | 0.3481      | 0.7028           | 0.8127      |
| 0.0502        | 2.13  | 5900 | 0.2381          | 0.3624      | 0.6954           | 0.8093      |
| 0.0541        | 2.16  | 6000 | 0.2298          | 0.3405      | 0.7159           | 0.8110      |
| 0.0671        | 2.2   | 6100 | 0.2432          | 0.3529      | 0.7031           | 0.7990      |
| 0.0672        | 2.24  | 6200 | 0.2431          | 0.3361      | 0.7195           | 0.7715      |
| 0.0446        | 2.27  | 6300 | 0.2447          | 0.3401      | 0.7141           | 0.7938      |
| 0.0424        | 2.31  | 6400 | 0.2426          | 0.3485      | 0.7018           | 0.8162      |
| 0.0386        | 2.34  | 6500 | 0.2488          | 0.3387      | 0.7123           | 0.8127      |
| 0.0736        | 2.38  | 6600 | 0.2454          | 0.3382      | 0.7054           | 0.8316      |
| 0.0421        | 2.42  | 6700 | 0.2513          | 0.3394      | 0.7120           | 0.8316      |
| 0.0607        | 2.45  | 6800 | 0.2546          | 0.3370      | 0.7092           | 0.8265      |
| 0.0517        | 2.49  | 6900 | 0.2594          | 0.3376      | 0.7082           | 0.8299      |
| 0.062         | 2.52  | 7000 | 0.2533          | 0.3369      | 0.7105           | 0.8110      |
| 0.0664        | 2.56  | 7100 | 0.2534          | 0.3329      | 0.7185           | 0.8024      |
| 0.0389        | 2.6   | 7200 | 0.2470          | 0.3288      | 0.7259           | 0.8093      |
| 0.0671        | 2.63  | 7300 | 0.2516          | 0.3294      | 0.7159           | 0.8041      |
| 0.0416        | 2.67  | 7400 | 0.2507          | 0.3307      | 0.7133           | 0.8058      |
| 0.0541        | 2.7   | 7500 | 0.2529          | 0.3355      | 0.7110           | 0.8058      |
| 0.0374        | 2.74  | 7600 | 0.2530          | 0.3315      | 0.7149           | 0.8110      |
| 0.04          | 2.78  | 7700 | 0.2520          | 0.3290      | 0.7167           | 0.8076      |
| 0.0507        | 2.81  | 7800 | 0.2555          | 0.3297      | 0.7105           | 0.8127      |
| 0.0379        | 2.85  | 7900 | 0.2531          | 0.3274      | 0.7162           | 0.8127      |
| 0.0736        | 2.88  | 8000 | 0.2526          | 0.3279      | 0.7164           | 0.8196      |
| 0.0589        | 2.92  | 8100 | 0.2522          | 0.3267      | 0.7144           | 0.8162      |
| 0.0449        | 2.96  | 8200 | 0.2521          | 0.3272      | 0.7149           | 0.8162      |
| 0.0498        | 2.99  | 8300 | 0.2520          | 0.3265      | 0.7167           | 0.8144      |


### 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