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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/huangyangyu/huggingface/runs/2zsefy9c)
# MyPetModel

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4452
- Accuracy: 0.7787

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.4374        | 0.2278  | 100  | 0.4457          | 0.7826   |
| 0.2148        | 0.4556  | 200  | 0.4591          | 0.7908   |
| 0.0794        | 0.6834  | 300  | 0.5510          | 0.8048   |
| 0.0433        | 0.9112  | 400  | 0.5158          | 0.8157   |
| 0.029         | 1.1390  | 500  | 0.8849          | 0.7725   |
| 0.0181        | 1.3667  | 600  | 0.7288          | 0.8135   |
| 0.0126        | 1.5945  | 700  | 0.6898          | 0.8206   |
| 0.0112        | 1.8223  | 800  | 0.9681          | 0.7703   |
| 0.0084        | 2.0501  | 900  | 0.9025          | 0.7846   |
| 0.0088        | 2.2779  | 1000 | 1.0068          | 0.7766   |
| 0.0046        | 2.5057  | 1100 | 0.8545          | 0.8148   |
| 0.0033        | 2.7335  | 1200 | 1.0199          | 0.7904   |
| 0.0036        | 2.9613  | 1300 | 1.3595          | 0.7381   |
| 0.005         | 3.1891  | 1400 | 1.3578          | 0.7361   |
| 0.0051        | 3.4169  | 1500 | 0.9367          | 0.8104   |
| 0.0021        | 3.6446  | 1600 | 1.6113          | 0.7173   |
| 0.0036        | 3.8724  | 1700 | 0.6898          | 0.8617   |
| 0.0026        | 4.1002  | 1800 | 1.0784          | 0.7958   |
| 0.0041        | 4.3280  | 1900 | 1.6640          | 0.7018   |
| 0.0054        | 4.5558  | 2000 | 0.9458          | 0.8047   |
| 0.0012        | 4.7836  | 2100 | 1.3136          | 0.7621   |
| 0.0047        | 5.0114  | 2200 | 1.7060          | 0.7058   |
| 0.0036        | 5.2392  | 2300 | 1.2863          | 0.7700   |
| 0.0023        | 5.4670  | 2400 | 1.1671          | 0.7861   |
| 0.0034        | 5.6948  | 2500 | 1.1628          | 0.7908   |
| 0.0022        | 5.9226  | 2600 | 1.2225          | 0.7844   |
| 0.0018        | 6.1503  | 2700 | 0.9177          | 0.8307   |
| 0.0007        | 6.3781  | 2800 | 1.1363          | 0.8003   |
| 0.001         | 6.6059  | 2900 | 0.9644          | 0.8274   |
| 0.0013        | 6.8337  | 3000 | 1.0775          | 0.8211   |
| 0.0027        | 7.0615  | 3100 | 1.4378          | 0.7627   |
| 0.0013        | 7.2893  | 3200 | 2.0668          | 0.7030   |
| 0.0038        | 7.5171  | 3300 | 1.9682          | 0.6941   |
| 0.0024        | 7.7449  | 3400 | 1.1606          | 0.7992   |
| 0.002         | 7.9727  | 3500 | 1.1364          | 0.8029   |
| 0.0002        | 8.2005  | 3600 | 1.0540          | 0.8185   |
| 0.0021        | 8.4282  | 3700 | 1.8765          | 0.7040   |
| 0.0003        | 8.6560  | 3800 | 1.3804          | 0.7721   |
| 0.0006        | 8.8838  | 3900 | 1.5498          | 0.7456   |
| 0.0011        | 9.1116  | 4000 | 1.4061          | 0.7703   |
| 0.0003        | 9.3394  | 4100 | 1.5528          | 0.7514   |
| 0.0013        | 9.5672  | 4200 | 1.5510          | 0.7585   |
| 0.0002        | 9.7950  | 4300 | 1.1179          | 0.8148   |
| 0.0001        | 10.0228 | 4400 | 1.5844          | 0.7557   |
| 0.001         | 10.2506 | 4500 | 1.2355          | 0.7990   |
| 0.0004        | 10.4784 | 4600 | 1.0223          | 0.8287   |
| 0.0009        | 10.7062 | 4700 | 1.7575          | 0.7332   |
| 0.0015        | 10.9339 | 4800 | 1.8685          | 0.7193   |
| 0.0007        | 11.1617 | 4900 | 1.2402          | 0.8015   |
| 0.0004        | 11.3895 | 5000 | 1.3765          | 0.7848   |
| 0.0003        | 11.6173 | 5100 | 1.6795          | 0.7471   |
| 0.0002        | 11.8451 | 5200 | 1.3439          | 0.7901   |
| 0.0007        | 12.0729 | 5300 | 2.0598          | 0.7095   |
| 0.0002        | 12.3007 | 5400 | 1.2197          | 0.8070   |
| 0.0001        | 12.5285 | 5500 | 1.1483          | 0.8127   |
| 0.0005        | 12.7563 | 5600 | 1.4303          | 0.7808   |
| 0.0005        | 12.9841 | 5700 | 1.2517          | 0.8017   |
| 0.0003        | 13.2118 | 5800 | 1.0473          | 0.8307   |
| 0.0005        | 13.4396 | 5900 | 1.3444          | 0.7816   |
| 0.0           | 13.6674 | 6000 | 1.2738          | 0.7930   |
| 0.0012        | 13.8952 | 6100 | 1.1715          | 0.8116   |
| 0.0003        | 14.1230 | 6200 | 1.4235          | 0.7794   |
| 0.0           | 14.3508 | 6300 | 1.4135          | 0.7815   |
| 0.0           | 14.5786 | 6400 | 1.4045          | 0.7830   |
| 0.0           | 14.8064 | 6500 | 1.4452          | 0.7787   |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.1.1
- Datasets 2.19.2
- Tokenizers 0.19.1

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