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# ryan03282024

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: 0.2238
- Ordinal Mae: 0.4441
- Ordinal Accuracy: 0.6446
- Na Accuracy: 0.7992

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Ordinal Mae | Ordinal Accuracy | Na Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|:-----------:|
| 0.3421        | 0.04  | 100  | 0.3331          | 0.8749      | 0.3817           | 0.6911      |
| 0.2813        | 0.09  | 200  | 0.3000          | 0.7492      | 0.5117           | 0.7954      |
| 0.2619        | 0.13  | 300  | 0.3019          | 0.6841      | 0.5273           | 0.7046      |
| 0.2863        | 0.17  | 400  | 0.2960          | 0.6538      | 0.5097           | 0.7336      |
| 0.2159        | 0.22  | 500  | 0.2602          | 0.5404      | 0.5660           | 0.8243      |
| 0.2235        | 0.26  | 600  | 0.2557          | 0.5015      | 0.5874           | 0.7780      |
| 0.285         | 0.31  | 700  | 0.2564          | 0.5000      | 0.6180           | 0.6853      |
| 0.2028        | 0.35  | 800  | 0.2862          | 0.6338      | 0.5068           | 0.7220      |
| 0.2006        | 0.39  | 900  | 0.2495          | 0.4830      | 0.6299           | 0.7587      |
| 0.2663        | 0.44  | 1000 | 0.2660          | 0.4893      | 0.6021           | 0.8610      |
| 0.2062        | 0.48  | 1100 | 0.2481          | 0.4713      | 0.6267           | 0.8436      |
| 0.1749        | 0.52  | 1200 | 0.2586          | 0.4959      | 0.6423           | 0.6737      |
| 0.2197        | 0.57  | 1300 | 0.2349          | 0.4841      | 0.5981           | 0.8031      |
| 0.2073        | 0.61  | 1400 | 0.2587          | 0.4878      | 0.6013           | 0.6950      |
| 0.1915        | 0.66  | 1500 | 0.2393          | 0.4771      | 0.6322           | 0.7683      |
| 0.2374        | 0.7   | 1600 | 0.2238          | 0.4441      | 0.6446           | 0.7992      |
| 0.2278        | 0.74  | 1700 | 0.2453          | 0.4410      | 0.6539           | 0.7278      |
| 0.2033        | 0.79  | 1800 | 0.2251          | 0.4584      | 0.6299           | 0.8185      |
| 0.1843        | 0.83  | 1900 | 0.2280          | 0.4446      | 0.6513           | 0.8127      |
| 0.1878        | 0.87  | 2000 | 0.2277          | 0.4454      | 0.6492           | 0.8127      |
| 0.2608        | 0.92  | 2100 | 0.2309          | 0.4517      | 0.6192           | 0.8494      |
| 0.201         | 0.96  | 2200 | 0.2459          | 0.4654      | 0.6406           | 0.7278      |
| 0.1736        | 1.0   | 2300 | 0.2438          | 0.4474      | 0.6475           | 0.7201      |
| 0.1374        | 1.05  | 2400 | 0.2368          | 0.4145      | 0.6622           | 0.7799      |
| 0.1334        | 1.09  | 2500 | 0.2424          | 0.4105      | 0.6732           | 0.7510      |
| 0.1319        | 1.14  | 2600 | 0.2336          | 0.4155      | 0.6712           | 0.7741      |
| 0.1549        | 1.18  | 2700 | 0.2525          | 0.4040      | 0.6625           | 0.7587      |
| 0.116         | 1.22  | 2800 | 0.2501          | 0.4425      | 0.6371           | 0.7664      |
| 0.1358        | 1.27  | 2900 | 0.2324          | 0.4136      | 0.6498           | 0.8185      |
| 0.1614        | 1.31  | 3000 | 0.2637          | 0.4353      | 0.6316           | 0.7915      |
| 0.1395        | 1.35  | 3100 | 0.2446          | 0.4020      | 0.6726           | 0.8012      |
| 0.1208        | 1.4   | 3200 | 0.2465          | 0.3946      | 0.6764           | 0.8243      |
| 0.1432        | 1.44  | 3300 | 0.2552          | 0.3919      | 0.6576           | 0.8900      |
| 0.1358        | 1.48  | 3400 | 0.2561          | 0.3984      | 0.6796           | 0.7896      |
| 0.0877        | 1.53  | 3500 | 0.2381          | 0.3901      | 0.6822           | 0.7876      |
| 0.1212        | 1.57  | 3600 | 0.2600          | 0.4001      | 0.6949           | 0.7259      |
| 0.1917        | 1.62  | 3700 | 0.2459          | 0.3889      | 0.6894           | 0.7819      |
| 0.1175        | 1.66  | 3800 | 0.2444          | 0.3937      | 0.6819           | 0.7741      |
| 0.1522        | 1.7   | 3900 | 0.2473          | 0.4010      | 0.6608           | 0.8050      |
| 0.1027        | 1.75  | 4000 | 0.2354          | 0.4208      | 0.6478           | 0.7838      |
| 0.1343        | 1.79  | 4100 | 0.2284          | 0.3977      | 0.6744           | 0.7992      |
| 0.1552        | 1.83  | 4200 | 0.2607          | 0.4045      | 0.6715           | 0.7780      |
| 0.1172        | 1.88  | 4300 | 0.2421          | 0.3971      | 0.6666           | 0.8282      |
| 0.1381        | 1.92  | 4400 | 0.2253          | 0.3813      | 0.6793           | 0.7857      |
| 0.1282        | 1.97  | 4500 | 0.2335          | 0.4146      | 0.6510           | 0.8436      |
| 0.0734        | 2.01  | 4600 | 0.2382          | 0.3802      | 0.6897           | 0.7896      |
| 0.1046        | 2.05  | 4700 | 0.2358          | 0.3695      | 0.6874           | 0.8012      |
| 0.0529        | 2.1   | 4800 | 0.2463          | 0.3596      | 0.7096           | 0.7934      |
| 0.0687        | 2.14  | 4900 | 0.2615          | 0.3921      | 0.6738           | 0.7857      |
| 0.0613        | 2.18  | 5000 | 0.2543          | 0.3651      | 0.6877           | 0.8108      |
| 0.0591        | 2.23  | 5100 | 0.2539          | 0.3693      | 0.6885           | 0.7915      |
| 0.0474        | 2.27  | 5200 | 0.2650          | 0.3722      | 0.6836           | 0.7992      |
| 0.0511        | 2.31  | 5300 | 0.2631          | 0.3681      | 0.6868           | 0.8127      |
| 0.0683        | 2.36  | 5400 | 0.2714          | 0.3630      | 0.6955           | 0.7838      |
| 0.0654        | 2.4   | 5500 | 0.2769          | 0.3673      | 0.6787           | 0.7992      |
| 0.0581        | 2.45  | 5600 | 0.2777          | 0.3628      | 0.6952           | 0.7992      |
| 0.072         | 2.49  | 5700 | 0.2919          | 0.3610      | 0.6888           | 0.7683      |
| 0.0737        | 2.53  | 5800 | 0.2807          | 0.3612      | 0.6984           | 0.7838      |
| 0.0667        | 2.58  | 5900 | 0.2926          | 0.3607      | 0.7001           | 0.7510      |
| 0.0669        | 2.62  | 6000 | 0.2875          | 0.3616      | 0.6891           | 0.7992      |
| 0.0535        | 2.66  | 6100 | 0.2854          | 0.3565      | 0.6960           | 0.7683      |
| 0.06          | 2.71  | 6200 | 0.2847          | 0.3501      | 0.7015           | 0.7741      |
| 0.0534        | 2.75  | 6300 | 0.2821          | 0.3495      | 0.7007           | 0.7625      |
| 0.0526        | 2.79  | 6400 | 0.2834          | 0.3853      | 0.6700           | 0.7625      |
| 0.0841        | 2.84  | 6500 | 0.2839          | 0.3504      | 0.7044           | 0.7490      |
| 0.0529        | 2.88  | 6600 | 0.2858          | 0.3595      | 0.6897           | 0.7819      |
| 0.0811        | 2.93  | 6700 | 0.2843          | 0.3480      | 0.7047           | 0.7799      |
| 0.0502        | 2.97  | 6800 | 0.2892          | 0.3483      | 0.7010           | 0.7819      |
| 0.0273        | 3.01  | 6900 | 0.2801          | 0.3454      | 0.6958           | 0.8108      |
| 0.0306        | 3.06  | 7000 | 0.2782          | 0.3444      | 0.7024           | 0.8031      |
| 0.0257        | 3.1   | 7100 | 0.2797          | 0.3352      | 0.7085           | 0.7934      |
| 0.0241        | 3.14  | 7200 | 0.2828          | 0.3343      | 0.7059           | 0.7954      |
| 0.0255        | 3.19  | 7300 | 0.2890          | 0.3364      | 0.6981           | 0.8050      |
| 0.0245        | 3.23  | 7400 | 0.2906          | 0.3392      | 0.7044           | 0.7992      |
| 0.0232        | 3.28  | 7500 | 0.2891          | 0.3338      | 0.7036           | 0.7857      |
| 0.0352        | 3.32  | 7600 | 0.2908          | 0.3443      | 0.6926           | 0.7896      |
| 0.0376        | 3.36  | 7700 | 0.2877          | 0.3315      | 0.7050           | 0.7915      |
| 0.025         | 3.41  | 7800 | 0.2889          | 0.3316      | 0.7076           | 0.7896      |
| 0.0225        | 3.45  | 7900 | 0.2902          | 0.3286      | 0.7070           | 0.7819      |
| 0.024         | 3.49  | 8000 | 0.2902          | 0.3270      | 0.7102           | 0.7954      |
| 0.0404        | 3.54  | 8100 | 0.2950          | 0.3294      | 0.7053           | 0.7896      |
| 0.0221        | 3.58  | 8200 | 0.2924          | 0.3271      | 0.7093           | 0.7934      |
| 0.0182        | 3.62  | 8300 | 0.2921          | 0.3237      | 0.7105           | 0.7934      |
| 0.0304        | 3.67  | 8400 | 0.2911          | 0.3231      | 0.7134           | 0.7857      |
| 0.0193        | 3.71  | 8500 | 0.2915          | 0.3221      | 0.7166           | 0.7838      |
| 0.0223        | 3.76  | 8600 | 0.2931          | 0.3235      | 0.7122           | 0.7896      |
| 0.0254        | 3.8   | 8700 | 0.2947          | 0.3214      | 0.7174           | 0.7876      |
| 0.0215        | 3.84  | 8800 | 0.2936          | 0.3202      | 0.7128           | 0.7857      |
| 0.0312        | 3.89  | 8900 | 0.2956          | 0.3210      | 0.7134           | 0.7857      |
| 0.0189        | 3.93  | 9000 | 0.2946          | 0.3210      | 0.7125           | 0.7876      |
| 0.021         | 3.97  | 9100 | 0.2949          | 0.3194      | 0.7145           | 0.7876      |


### 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