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# modeversion2_m7_e8

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 sudo-s/herbier_mesuem7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1060
- Accuracy: 0.9761

## 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: 0.0002
- 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0231        | 0.06  | 100   | 3.8568          | 0.1883   |
| 3.3863        | 0.12  | 200   | 3.2510          | 0.2596   |
| 2.6187        | 0.18  | 300   | 2.6243          | 0.3882   |
| 2.3097        | 0.23  | 400   | 2.2189          | 0.4527   |
| 1.9016        | 0.29  | 500   | 1.9495          | 0.5244   |
| 1.7478        | 0.35  | 600   | 1.6609          | 0.6091   |
| 1.2345        | 0.41  | 700   | 1.4335          | 0.6426   |
| 1.4129        | 0.47  | 800   | 1.3001          | 0.6752   |
| 1.1722        | 0.53  | 900   | 1.2030          | 0.6785   |
| 1.0808        | 0.59  | 1000  | 1.0051          | 0.7273   |
| 0.8814        | 0.64  | 1100  | 1.0715          | 0.7063   |
| 0.9831        | 0.7   | 1200  | 0.9283          | 0.7334   |
| 0.8118        | 0.76  | 1300  | 0.8525          | 0.7631   |
| 0.7203        | 0.82  | 1400  | 0.7849          | 0.7756   |
| 0.8881        | 0.88  | 1500  | 0.8786          | 0.7487   |
| 0.6407        | 0.94  | 1600  | 0.6896          | 0.8000   |
| 0.7574        | 1.0   | 1700  | 0.7314          | 0.7754   |
| 0.6063        | 1.06  | 1800  | 0.6312          | 0.8068   |
| 0.4797        | 1.11  | 1900  | 0.5792          | 0.8296   |
| 0.4973        | 1.17  | 2000  | 0.5846          | 0.8221   |
| 0.4432        | 1.23  | 2100  | 0.7057          | 0.7905   |
| 0.5518        | 1.29  | 2200  | 0.5621          | 0.8304   |
| 0.3256        | 1.35  | 2300  | 0.5890          | 0.8143   |
| 0.4284        | 1.41  | 2400  | 0.5204          | 0.8485   |
| 0.3702        | 1.47  | 2500  | 0.5699          | 0.8256   |
| 0.2858        | 1.52  | 2600  | 0.5815          | 0.8287   |
| 0.3706        | 1.58  | 2700  | 0.4615          | 0.8571   |
| 0.3484        | 1.64  | 2800  | 0.4812          | 0.8518   |
| 0.2865        | 1.7   | 2900  | 0.4285          | 0.8638   |
| 0.4474        | 1.76  | 3000  | 0.5217          | 0.8377   |
| 0.2101        | 1.82  | 3100  | 0.4478          | 0.8589   |
| 0.3545        | 1.88  | 3200  | 0.4444          | 0.8612   |
| 0.2728        | 1.93  | 3300  | 0.4213          | 0.8645   |
| 0.3525        | 1.99  | 3400  | 0.3551          | 0.8848   |
| 0.0936        | 2.05  | 3500  | 0.4074          | 0.8748   |
| 0.2118        | 2.11  | 3600  | 0.4089          | 0.8812   |
| 0.2744        | 2.17  | 3700  | 0.3534          | 0.8894   |
| 0.211         | 2.23  | 3800  | 0.4422          | 0.8599   |
| 0.1684        | 2.29  | 3900  | 0.3705          | 0.8858   |
| 0.1885        | 2.34  | 4000  | 0.3651          | 0.8862   |
| 0.249         | 2.4   | 4100  | 0.4234          | 0.8687   |
| 0.1485        | 2.46  | 4200  | 0.3784          | 0.8798   |
| 0.1188        | 2.52  | 4300  | 0.3589          | 0.8873   |
| 0.1274        | 2.58  | 4400  | 0.3570          | 0.8917   |
| 0.2206        | 2.64  | 4500  | 0.3377          | 0.8920   |
| 0.1287        | 2.7   | 4600  | 0.3170          | 0.9023   |
| 0.1805        | 2.75  | 4700  | 0.3469          | 0.8934   |
| 0.1505        | 2.81  | 4800  | 0.4258          | 0.8757   |
| 0.1592        | 2.87  | 4900  | 0.3415          | 0.8948   |
| 0.1297        | 2.93  | 5000  | 0.3168          | 0.9028   |
| 0.1284        | 2.99  | 5100  | 0.3060          | 0.9089   |
| 0.0833        | 3.05  | 5200  | 0.2610          | 0.9207   |
| 0.0334        | 3.11  | 5300  | 0.2766          | 0.9197   |
| 0.0847        | 3.17  | 5400  | 0.3366          | 0.9016   |
| 0.1112        | 3.22  | 5500  | 0.3098          | 0.9079   |
| 0.0477        | 3.28  | 5600  | 0.3385          | 0.9041   |
| 0.0419        | 3.34  | 5700  | 0.2944          | 0.9139   |
| 0.0827        | 3.4   | 5800  | 0.2715          | 0.9239   |
| 0.0659        | 3.46  | 5900  | 0.2695          | 0.9230   |
| 0.0244        | 3.52  | 6000  | 0.3050          | 0.9147   |
| 0.0883        | 3.58  | 6100  | 0.2862          | 0.9203   |
| 0.0527        | 3.63  | 6200  | 0.2383          | 0.9319   |
| 0.0828        | 3.69  | 6300  | 0.2984          | 0.9182   |
| 0.0678        | 3.75  | 6400  | 0.2135          | 0.9436   |
| 0.0492        | 3.81  | 6500  | 0.2605          | 0.9296   |
| 0.0374        | 3.87  | 6600  | 0.2192          | 0.9380   |
| 0.1846        | 3.93  | 6700  | 0.2804          | 0.9187   |
| 0.0557        | 3.99  | 6800  | 0.2599          | 0.9253   |
| 0.0127        | 4.04  | 6900  | 0.2412          | 0.9336   |
| 0.0203        | 4.1   | 7000  | 0.2214          | 0.9415   |
| 0.0272        | 4.16  | 7100  | 0.2322          | 0.9356   |
| 0.066         | 4.22  | 7200  | 0.2643          | 0.9325   |
| 0.0628        | 4.28  | 7300  | 0.2170          | 0.9406   |
| 0.0108        | 4.34  | 7400  | 0.2388          | 0.9405   |
| 0.026         | 4.4   | 7500  | 0.2533          | 0.9372   |
| 0.0401        | 4.45  | 7600  | 0.2407          | 0.9358   |
| 0.0493        | 4.51  | 7700  | 0.2213          | 0.9415   |
| 0.0951        | 4.57  | 7800  | 0.3016          | 0.9237   |
| 0.0017        | 4.63  | 7900  | 0.2183          | 0.9448   |
| 0.0561        | 4.69  | 8000  | 0.1962          | 0.9492   |
| 0.0063        | 4.75  | 8100  | 0.1868          | 0.9522   |
| 0.0054        | 4.81  | 8200  | 0.2068          | 0.9459   |
| 0.0519        | 4.87  | 8300  | 0.2141          | 0.9429   |
| 0.027         | 4.92  | 8400  | 0.2138          | 0.9438   |
| 0.0034        | 4.98  | 8500  | 0.1774          | 0.9529   |
| 0.0096        | 5.04  | 8600  | 0.1778          | 0.9512   |
| 0.0011        | 5.1   | 8700  | 0.1854          | 0.9512   |
| 0.0195        | 5.16  | 8800  | 0.1914          | 0.9483   |
| 0.0245        | 5.22  | 8900  | 0.2156          | 0.9471   |
| 0.0055        | 5.28  | 9000  | 0.1640          | 0.9574   |
| 0.0166        | 5.33  | 9100  | 0.1770          | 0.9568   |
| 0.0217        | 5.39  | 9200  | 0.2011          | 0.9479   |
| 0.0017        | 5.45  | 9300  | 0.2210          | 0.9462   |
| 0.0161        | 5.51  | 9400  | 0.1510          | 0.9621   |
| 0.0193        | 5.57  | 9500  | 0.1643          | 0.9586   |
| 0.0121        | 5.63  | 9600  | 0.1716          | 0.9535   |
| 0.0146        | 5.69  | 9700  | 0.1720          | 0.9554   |
| 0.0071        | 5.74  | 9800  | 0.1831          | 0.9541   |
| 0.0018        | 5.8   | 9900  | 0.2076          | 0.9485   |
| 0.0007        | 5.86  | 10000 | 0.1636          | 0.9599   |
| 0.0005        | 5.92  | 10100 | 0.1625          | 0.9602   |
| 0.0277        | 5.98  | 10200 | 0.1874          | 0.9546   |
| 0.0005        | 6.04  | 10300 | 0.1790          | 0.9579   |
| 0.0012        | 6.1   | 10400 | 0.1840          | 0.9544   |
| 0.0431        | 6.15  | 10500 | 0.1571          | 0.9628   |
| 0.0332        | 6.21  | 10600 | 0.1599          | 0.9591   |
| 0.0014        | 6.27  | 10700 | 0.1493          | 0.9632   |
| 0.0014        | 6.33  | 10800 | 0.1366          | 0.9661   |
| 0.0006        | 6.39  | 10900 | 0.1582          | 0.9609   |
| 0.0005        | 6.45  | 11000 | 0.1704          | 0.9589   |
| 0.0004        | 6.51  | 11100 | 0.1376          | 0.9671   |
| 0.0755        | 6.57  | 11200 | 0.1375          | 0.9654   |
| 0.0002        | 6.62  | 11300 | 0.1361          | 0.9661   |
| 0.0006        | 6.68  | 11400 | 0.1323          | 0.9675   |
| 0.0009        | 6.74  | 11500 | 0.1239          | 0.9692   |
| 0.0004        | 6.8   | 11600 | 0.1514          | 0.9631   |
| 0.0002        | 6.86  | 11700 | 0.1386          | 0.9664   |
| 0.0004        | 6.92  | 11800 | 0.1368          | 0.9659   |
| 0.0004        | 6.98  | 11900 | 0.1276          | 0.9684   |
| 0.0002        | 7.03  | 12000 | 0.1171          | 0.9712   |
| 0.0002        | 7.09  | 12100 | 0.1142          | 0.9711   |
| 0.0001        | 7.15  | 12200 | 0.1183          | 0.9727   |
| 0.0002        | 7.21  | 12300 | 0.1167          | 0.9732   |
| 0.0002        | 7.27  | 12400 | 0.1143          | 0.9737   |
| 0.0001        | 7.33  | 12500 | 0.1129          | 0.9737   |
| 0.0002        | 7.39  | 12600 | 0.1116          | 0.9742   |
| 0.0002        | 7.44  | 12700 | 0.1126          | 0.9745   |
| 0.0002        | 7.5   | 12800 | 0.1111          | 0.9748   |
| 0.0002        | 7.56  | 12900 | 0.1102          | 0.9747   |
| 0.0001        | 7.62  | 13000 | 0.1094          | 0.9747   |
| 0.0001        | 7.68  | 13100 | 0.1086          | 0.9742   |
| 0.0001        | 7.74  | 13200 | 0.1079          | 0.9748   |
| 0.0002        | 7.8   | 13300 | 0.1062          | 0.9754   |
| 0.0002        | 7.85  | 13400 | 0.1068          | 0.9757   |
| 0.0001        | 7.91  | 13500 | 0.1061          | 0.9762   |
| 0.0001        | 7.97  | 13600 | 0.1060          | 0.9761   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1

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