
<!-- 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. -->

[<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/mqnke3pt)
# swinModel

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4645
- Accuracy: 0.7823

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5524        | 0.2278  | 100  | 0.3380          | 0.9845   |
| 0.4727        | 0.4556  | 200  | 0.3134          | 0.9439   |
| 0.3821        | 0.6834  | 300  | 0.3179          | 0.8939   |
| 0.2765        | 0.9112  | 400  | 0.3308          | 0.8603   |
| 0.1905        | 1.1390  | 500  | 0.4489          | 0.8069   |
| 0.1258        | 1.3667  | 600  | 0.5830          | 0.7731   |
| 0.0846        | 1.5945  | 700  | 0.4515          | 0.8439   |
| 0.064         | 1.8223  | 800  | 0.5274          | 0.8248   |
| 0.0494        | 2.0501  | 900  | 0.6575          | 0.7969   |
| 0.0378        | 2.2779  | 1000 | 0.6267          | 0.8261   |
| 0.0284        | 2.5057  | 1100 | 0.8875          | 0.7677   |
| 0.023         | 2.7335  | 1200 | 1.0218          | 0.7502   |
| 0.0225        | 2.9613  | 1300 | 0.8597          | 0.7930   |
| 0.0158        | 3.1891  | 1400 | 0.9559          | 0.7875   |
| 0.0134        | 3.4169  | 1500 | 0.7133          | 0.8378   |
| 0.0146        | 3.6446  | 1600 | 0.8297          | 0.8159   |
| 0.0116        | 3.8724  | 1700 | 0.9716          | 0.7930   |
| 0.0099        | 4.1002  | 1800 | 0.8118          | 0.8289   |
| 0.009         | 4.3280  | 1900 | 0.8361          | 0.8305   |
| 0.0059        | 4.5558  | 2000 | 0.9536          | 0.8127   |
| 0.009         | 4.7836  | 2100 | 1.0436          | 0.8003   |
| 0.0107        | 5.0114  | 2200 | 1.0988          | 0.7929   |
| 0.0077        | 5.2392  | 2300 | 0.9100          | 0.8344   |
| 0.007         | 5.4670  | 2400 | 0.9920          | 0.8186   |
| 0.0037        | 5.6948  | 2500 | 1.0256          | 0.8130   |
| 0.0073        | 5.9226  | 2600 | 1.5456          | 0.7387   |
| 0.0055        | 6.1503  | 2700 | 1.2020          | 0.7793   |
| 0.0039        | 6.3781  | 2800 | 1.1095          | 0.8048   |
| 0.0022        | 6.6059  | 2900 | 1.2638          | 0.7887   |
| 0.0042        | 6.8337  | 3000 | 1.0389          | 0.8263   |
| 0.005         | 7.0615  | 3100 | 1.3570          | 0.7763   |
| 0.0017        | 7.2893  | 3200 | 1.6866          | 0.7303   |
| 0.0024        | 7.5171  | 3300 | 1.4244          | 0.7679   |
| 0.0036        | 7.7449  | 3400 | 1.4379          | 0.7609   |
| 0.0032        | 7.9727  | 3500 | 1.1855          | 0.8006   |
| 0.0016        | 8.2005  | 3600 | 1.1089          | 0.8163   |
| 0.0023        | 8.4282  | 3700 | 0.9546          | 0.8441   |
| 0.0022        | 8.6560  | 3800 | 1.0083          | 0.8378   |
| 0.002         | 8.8838  | 3900 | 1.6526          | 0.7368   |
| 0.0032        | 9.1116  | 4000 | 1.5307          | 0.7619   |
| 0.0008        | 9.3394  | 4100 | 1.1384          | 0.8191   |
| 0.002         | 9.5672  | 4200 | 1.2104          | 0.8063   |
| 0.0031        | 9.7950  | 4300 | 1.5793          | 0.7564   |
| 0.0024        | 10.0228 | 4400 | 1.3544          | 0.7857   |
| 0.0035        | 10.2506 | 4500 | 1.5046          | 0.7667   |
| 0.0009        | 10.4784 | 4600 | 1.8010          | 0.7306   |
| 0.0007        | 10.7062 | 4700 | 1.2062          | 0.8115   |
| 0.0025        | 10.9339 | 4800 | 1.2110          | 0.8127   |
| 0.0016        | 11.1617 | 4900 | 1.3772          | 0.7875   |
| 0.001         | 11.3895 | 5000 | 1.3586          | 0.7947   |
| 0.0024        | 11.6173 | 5100 | 1.2359          | 0.8094   |
| 0.0012        | 11.8451 | 5200 | 0.8793          | 0.8679   |
| 0.0011        | 12.0729 | 5300 | 1.5563          | 0.7648   |
| 0.0021        | 12.3007 | 5400 | 1.3154          | 0.8003   |
| 0.0018        | 12.5285 | 5500 | 1.2115          | 0.8168   |
| 0.001         | 12.7563 | 5600 | 1.4905          | 0.7773   |
| 0.0012        | 12.9841 | 5700 | 1.4290          | 0.7868   |
| 0.0022        | 13.2118 | 5800 | 1.1928          | 0.8214   |
| 0.0023        | 13.4396 | 5900 | 1.2761          | 0.8077   |
| 0.0014        | 13.6674 | 6000 | 1.1804          | 0.8211   |
| 0.0021        | 13.8952 | 6100 | 1.3523          | 0.7965   |
| 0.0007        | 14.1230 | 6200 | 1.2330          | 0.8128   |
| 0.0008        | 14.3508 | 6300 | 1.3563          | 0.7955   |
| 0.0004        | 14.5786 | 6400 | 1.3969          | 0.7903   |
| 0.0011        | 14.8064 | 6500 | 1.4645          | 0.7823   |


### Framework versions

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

0 1