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# Action_model_ViT_384

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the action_class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4520
- Accuracy: 0.8612

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.946         | 0.19  | 100   | 0.7540          | 0.7803   |
| 0.9248        | 0.37  | 200   | 0.6282          | 0.7961   |
| 0.7968        | 0.56  | 300   | 0.5834          | 0.8102   |
| 0.6992        | 0.75  | 400   | 0.5647          | 0.8330   |
| 0.7331        | 0.93  | 500   | 0.5430          | 0.8295   |
| 0.5822        | 1.12  | 600   | 0.5894          | 0.8172   |
| 0.5906        | 1.31  | 700   | 0.6862          | 0.7909   |
| 0.5911        | 1.49  | 800   | 0.5369          | 0.8313   |
| 0.4564        | 1.68  | 900   | 0.4657          | 0.8576   |
| 0.6416        | 1.87  | 1000  | 0.5697          | 0.8190   |
| 0.5653        | 2.05  | 1100  | 0.6152          | 0.8102   |
| 0.4145        | 2.24  | 1200  | 0.5793          | 0.8225   |
| 0.4743        | 2.43  | 1300  | 0.4642          | 0.8576   |
| 0.4908        | 2.61  | 1400  | 0.4520          | 0.8612   |
| 0.523         | 2.8   | 1500  | 0.4989          | 0.8453   |
| 0.3315        | 2.99  | 1600  | 0.4786          | 0.8576   |
| 0.2779        | 3.17  | 1700  | 0.5546          | 0.8524   |
| 0.2984        | 3.36  | 1800  | 0.4977          | 0.8576   |
| 0.5914        | 3.54  | 1900  | 0.6296          | 0.8225   |
| 0.3236        | 3.73  | 2000  | 0.7225          | 0.8172   |
| 0.6194        | 3.92  | 2100  | 0.5783          | 0.8506   |
| 0.5066        | 4.1   | 2200  | 0.5825          | 0.8260   |
| 0.3532        | 4.29  | 2300  | 0.5606          | 0.8594   |
| 0.3531        | 4.48  | 2400  | 0.5068          | 0.8699   |
| 0.2573        | 4.66  | 2500  | 0.5632          | 0.8576   |
| 0.2713        | 4.85  | 2600  | 0.5047          | 0.8612   |
| 0.3538        | 5.04  | 2700  | 0.5988          | 0.8471   |
| 0.2291        | 5.22  | 2800  | 0.5751          | 0.8453   |
| 0.2976        | 5.41  | 2900  | 0.5781          | 0.8559   |
| 0.296         | 5.6   | 3000  | 0.5499          | 0.8664   |
| 0.3776        | 5.78  | 3100  | 0.5718          | 0.8612   |
| 0.2213        | 5.97  | 3200  | 0.5421          | 0.8682   |
| 0.325         | 6.16  | 3300  | 0.6453          | 0.8453   |
| 0.1594        | 6.34  | 3400  | 0.5558          | 0.8647   |
| 0.3377        | 6.53  | 3500  | 0.6619          | 0.8418   |
| 0.3743        | 6.72  | 3600  | 0.5446          | 0.8717   |
| 0.2327        | 6.9   | 3700  | 0.5484          | 0.8735   |
| 0.1659        | 7.09  | 3800  | 0.6629          | 0.8471   |
| 0.4036        | 7.28  | 3900  | 0.6510          | 0.8330   |
| 0.2084        | 7.46  | 4000  | 0.5640          | 0.8629   |
| 0.2251        | 7.65  | 4100  | 0.6379          | 0.8541   |
| 0.192         | 7.84  | 4200  | 0.5897          | 0.8629   |
| 0.1956        | 8.02  | 4300  | 0.5874          | 0.8699   |
| 0.1446        | 8.21  | 4400  | 0.6462          | 0.8594   |
| 0.2971        | 8.4   | 4500  | 0.5909          | 0.8735   |
| 0.2665        | 8.58  | 4600  | 0.6769          | 0.8612   |
| 0.2937        | 8.77  | 4700  | 0.6760          | 0.8506   |
| 0.1437        | 8.96  | 4800  | 0.6566          | 0.8489   |
| 0.1433        | 9.14  | 4900  | 0.6659          | 0.8418   |
| 0.2069        | 9.33  | 5000  | 0.6825          | 0.8541   |
| 0.2095        | 9.51  | 5100  | 0.6157          | 0.8664   |
| 0.1579        | 9.7   | 5200  | 0.6693          | 0.8629   |
| 0.1962        | 9.89  | 5300  | 0.6911          | 0.8524   |
| 0.3149        | 10.07 | 5400  | 0.6260          | 0.8559   |
| 0.2166        | 10.26 | 5500  | 0.6200          | 0.8770   |
| 0.1259        | 10.45 | 5600  | 0.7164          | 0.8576   |
| 0.1892        | 10.63 | 5700  | 0.7182          | 0.8612   |
| 0.1953        | 10.82 | 5800  | 0.7193          | 0.8418   |
| 0.2392        | 11.01 | 5900  | 0.6621          | 0.8664   |
| 0.1594        | 11.19 | 6000  | 0.7471          | 0.8489   |
| 0.2156        | 11.38 | 6100  | 0.7316          | 0.8612   |
| 0.137         | 11.57 | 6200  | 0.6837          | 0.8699   |
| 0.181         | 11.75 | 6300  | 0.6595          | 0.8647   |
| 0.2049        | 11.94 | 6400  | 0.6982          | 0.8506   |
| 0.1028        | 12.13 | 6500  | 0.6771          | 0.8682   |
| 0.1347        | 12.31 | 6600  | 0.6841          | 0.8699   |
| 0.1269        | 12.5  | 6700  | 0.7226          | 0.8594   |
| 0.2288        | 12.69 | 6800  | 0.7083          | 0.8629   |
| 0.1094        | 12.87 | 6900  | 0.7455          | 0.8471   |
| 0.0661        | 13.06 | 7000  | 0.7330          | 0.8541   |
| 0.1811        | 13.25 | 7100  | 0.7363          | 0.8436   |
| 0.2225        | 13.43 | 7200  | 0.7757          | 0.8453   |
| 0.1619        | 13.62 | 7300  | 0.7361          | 0.8576   |
| 0.2032        | 13.81 | 7400  | 0.7656          | 0.8576   |
| 0.0216        | 13.99 | 7500  | 0.7760          | 0.8629   |
| 0.2476        | 14.18 | 7600  | 0.7723          | 0.8612   |
| 0.1616        | 14.37 | 7700  | 0.7247          | 0.8787   |
| 0.1142        | 14.55 | 7800  | 0.7907          | 0.8699   |
| 0.0906        | 14.74 | 7900  | 0.7829          | 0.8647   |
| 0.2199        | 14.93 | 8000  | 0.7427          | 0.8717   |
| 0.0643        | 15.11 | 8100  | 0.7280          | 0.8699   |
| 0.1685        | 15.3  | 8200  | 0.8381          | 0.8541   |
| 0.1677        | 15.49 | 8300  | 0.8638          | 0.8506   |
| 0.1399        | 15.67 | 8400  | 0.8423          | 0.8612   |
| 0.1041        | 15.86 | 8500  | 0.8051          | 0.8541   |
| 0.2223        | 16.04 | 8600  | 0.7768          | 0.8647   |
| 0.1016        | 16.23 | 8700  | 0.7965          | 0.8647   |
| 0.065         | 16.42 | 8800  | 0.8331          | 0.8418   |
| 0.1156        | 16.6  | 8900  | 0.8023          | 0.8629   |
| 0.2263        | 16.79 | 9000  | 0.8116          | 0.8594   |
| 0.1197        | 16.98 | 9100  | 0.8490          | 0.8576   |
| 0.1931        | 17.16 | 9200  | 0.8194          | 0.8612   |
| 0.1289        | 17.35 | 9300  | 0.8353          | 0.8489   |
| 0.2039        | 17.54 | 9400  | 0.8163          | 0.8453   |
| 0.0825        | 17.72 | 9500  | 0.7942          | 0.8524   |
| 0.0712        | 17.91 | 9600  | 0.8027          | 0.8559   |
| 0.244         | 18.1  | 9700  | 0.7803          | 0.8664   |
| 0.1482        | 18.28 | 9800  | 0.7754          | 0.8629   |
| 0.1829        | 18.47 | 9900  | 0.7810          | 0.8594   |
| 0.019         | 18.66 | 10000 | 0.7972          | 0.8559   |
| 0.061         | 18.84 | 10100 | 0.8180          | 0.8576   |
| 0.117         | 19.03 | 10200 | 0.8319          | 0.8559   |
| 0.1858        | 19.22 | 10300 | 0.8432          | 0.8559   |
| 0.1087        | 19.4  | 10400 | 0.8273          | 0.8594   |
| 0.1983        | 19.59 | 10500 | 0.8257          | 0.8612   |
| 0.2453        | 19.78 | 10600 | 0.8177          | 0.8576   |
| 0.1189        | 19.96 | 10700 | 0.8201          | 0.8594   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2

কথা_বলা কম্পিউটার_ব্যবহার_করা খাওয়া খেলা_করা ঘুমানো পান_করা পড়া রান্না_করা লেখা হাঁটা