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# ryan03312024_lr_2e-5_wd_001_v2

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: 0.1914
- Ordinal Mae: 0.4198
- Ordinal Accuracy: 0.6843
- Na Accuracy: 0.8505

## 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: 2e-05
- 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: 2.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Ordinal Mae | Ordinal Accuracy | Na Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|:-----------:|
| 0.4426        | 0.04  | 100  | 0.3707          | 0.8707      | 0.3409           | 0.8076      |
| 0.3133        | 0.07  | 200  | 0.3203          | 0.8545      | 0.4300           | 0.7749      |
| 0.3349        | 0.11  | 300  | 0.2997          | 0.8339      | 0.4593           | 0.8419      |
| 0.3173        | 0.14  | 400  | 0.2870          | 0.7993      | 0.4819           | 0.8660      |
| 0.2946        | 0.18  | 500  | 0.2856          | 0.7690      | 0.5112           | 0.8935      |
| 0.3002        | 0.22  | 600  | 0.2724          | 0.7233      | 0.5345           | 0.9210      |
| 0.2817        | 0.25  | 700  | 0.2657          | 0.6928      | 0.5566           | 0.8625      |
| 0.2939        | 0.29  | 800  | 0.2596          | 0.6425      | 0.5862           | 0.7921      |
| 0.2525        | 0.32  | 900  | 0.2459          | 0.6053      | 0.6047           | 0.8265      |
| 0.2163        | 0.36  | 1000 | 0.2400          | 0.5777      | 0.6245           | 0.8110      |
| 0.2181        | 0.4   | 1100 | 0.2339          | 0.5430      | 0.6024           | 0.8763      |
| 0.1949        | 0.43  | 1200 | 0.2331          | 0.5329      | 0.6286           | 0.7955      |
| 0.214         | 0.47  | 1300 | 0.2424          | 0.5244      | 0.6183           | 0.7629      |
| 0.27          | 0.5   | 1400 | 0.2298          | 0.4995      | 0.6368           | 0.7869      |
| 0.2117        | 0.54  | 1500 | 0.2301          | 0.4950      | 0.6473           | 0.7784      |
| 0.2038        | 0.58  | 1600 | 0.2156          | 0.4899      | 0.6550           | 0.8368      |
| 0.1974        | 0.61  | 1700 | 0.2212          | 0.4639      | 0.6347           | 0.8282      |
| 0.1916        | 0.65  | 1800 | 0.2151          | 0.4790      | 0.6440           | 0.8797      |
| 0.1921        | 0.69  | 1900 | 0.2050          | 0.4614      | 0.6609           | 0.8729      |
| 0.1936        | 0.72  | 2000 | 0.2061          | 0.4566      | 0.6496           | 0.8574      |
| 0.1939        | 0.76  | 2100 | 0.2294          | 0.4657      | 0.6363           | 0.9089      |
| 0.257         | 0.79  | 2200 | 0.2054          | 0.4567      | 0.6527           | 0.8608      |
| 0.2236        | 0.83  | 2300 | 0.2044          | 0.4542      | 0.6640           | 0.8763      |
| 0.1925        | 0.87  | 2400 | 0.2085          | 0.4463      | 0.6887           | 0.8076      |
| 0.1657        | 0.9   | 2500 | 0.2034          | 0.4392      | 0.6769           | 0.8522      |
| 0.1723        | 0.94  | 2600 | 0.1957          | 0.4257      | 0.6756           | 0.8385      |
| 0.2279        | 0.97  | 2700 | 0.1946          | 0.4287      | 0.6740           | 0.8643      |
| 0.1421        | 1.01  | 2800 | 0.1914          | 0.4198      | 0.6843           | 0.8505      |
| 0.1116        | 1.05  | 2900 | 0.2019          | 0.4214      | 0.6704           | 0.8230      |
| 0.1194        | 1.08  | 3000 | 0.1954          | 0.4178      | 0.6807           | 0.8368      |
| 0.1312        | 1.12  | 3100 | 0.1930          | 0.4166      | 0.6874           | 0.8591      |
| 0.1836        | 1.15  | 3200 | 0.1989          | 0.4107      | 0.6794           | 0.8643      |
| 0.1282        | 1.19  | 3300 | 0.1951          | 0.4127      | 0.6971           | 0.8540      |
| 0.1406        | 1.23  | 3400 | 0.1959          | 0.4036      | 0.6974           | 0.8505      |
| 0.0929        | 1.26  | 3500 | 0.1969          | 0.4020      | 0.6977           | 0.8454      |
| 0.1135        | 1.3   | 3600 | 0.1957          | 0.4026      | 0.6982           | 0.8316      |
| 0.1345        | 1.33  | 3700 | 0.1987          | 0.4107      | 0.6833           | 0.8814      |
| 0.1198        | 1.37  | 3800 | 0.1969          | 0.3988      | 0.6992           | 0.8522      |
| 0.1281        | 1.41  | 3900 | 0.1977          | 0.4066      | 0.6966           | 0.8402      |
| 0.1153        | 1.44  | 4000 | 0.2014          | 0.4091      | 0.6936           | 0.8436      |
| 0.1485        | 1.48  | 4100 | 0.1965          | 0.3989      | 0.7038           | 0.8385      |
| 0.1292        | 1.51  | 4200 | 0.1969          | 0.3978      | 0.7031           | 0.8471      |
| 0.1233        | 1.55  | 4300 | 0.1989          | 0.3993      | 0.6951           | 0.8660      |
| 0.1128        | 1.59  | 4400 | 0.1998          | 0.3920      | 0.6971           | 0.8522      |
| 0.0964        | 1.62  | 4500 | 0.2005          | 0.3926      | 0.6982           | 0.8625      |
| 0.1184        | 1.66  | 4600 | 0.2008          | 0.3860      | 0.6969           | 0.8711      |
| 0.108         | 1.69  | 4700 | 0.1994          | 0.3907      | 0.7020           | 0.8574      |
| 0.129         | 1.73  | 4800 | 0.1985          | 0.3896      | 0.7033           | 0.8591      |
| 0.1396        | 1.77  | 4900 | 0.1998          | 0.3834      | 0.6984           | 0.8574      |
| 0.1323        | 1.8   | 5000 | 0.1986          | 0.3844      | 0.7051           | 0.8454      |
| 0.1079        | 1.84  | 5100 | 0.1974          | 0.3833      | 0.7054           | 0.8402      |
| 0.0802        | 1.88  | 5200 | 0.1965          | 0.3822      | 0.7074           | 0.8488      |
| 0.1391        | 1.91  | 5300 | 0.1975          | 0.3809      | 0.7051           | 0.8454      |
| 0.1183        | 1.95  | 5400 | 0.1973          | 0.3827      | 0.7087           | 0.8351      |
| 0.1368        | 1.98  | 5500 | 0.1975          | 0.3813      | 0.7082           | 0.8333      |


### 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