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# plant-seedlings-freeze-0-6-aug-3-all-train

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2362
- Accuracy: 0.9454

## 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: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1589        | 0.25  | 100   | 0.3561          | 0.9141   |
| 0.1629        | 0.49  | 200   | 0.3792          | 0.8932   |
| 0.1222        | 0.74  | 300   | 0.3749          | 0.8975   |
| 0.1363        | 0.98  | 400   | 0.3021          | 0.9122   |
| 0.0699        | 1.23  | 500   | 0.4802          | 0.8883   |
| 0.0759        | 1.47  | 600   | 0.3129          | 0.9214   |
| 0.2157        | 1.72  | 700   | 0.3362          | 0.9159   |
| 0.1148        | 1.97  | 800   | 0.2898          | 0.9122   |
| 0.3137        | 2.21  | 900   | 0.4267          | 0.8969   |
| 0.072         | 2.46  | 1000  | 0.3180          | 0.9141   |
| 0.0775        | 2.7   | 1100  | 0.3856          | 0.9067   |
| 0.1019        | 2.95  | 1200  | 0.3182          | 0.9153   |
| 0.1119        | 3.19  | 1300  | 0.4458          | 0.8944   |
| 0.1342        | 3.44  | 1400  | 0.4718          | 0.8889   |
| 0.1658        | 3.69  | 1500  | 0.3697          | 0.9012   |
| 0.1609        | 3.93  | 1600  | 0.4079          | 0.9024   |
| 0.1223        | 4.18  | 1700  | 0.3688          | 0.9147   |
| 0.1821        | 4.42  | 1800  | 0.3392          | 0.9116   |
| 0.0901        | 4.67  | 1900  | 0.3726          | 0.8969   |
| 0.0857        | 4.91  | 2000  | 0.3158          | 0.9196   |
| 0.1245        | 5.16  | 2100  | 0.3503          | 0.9122   |
| 0.133         | 5.41  | 2200  | 0.3712          | 0.9134   |
| 0.171         | 5.65  | 2300  | 0.3543          | 0.9067   |
| 0.1222        | 5.9   | 2400  | 0.3031          | 0.9227   |
| 0.1504        | 6.14  | 2500  | 0.3356          | 0.9085   |
| 0.0889        | 6.39  | 2600  | 0.3695          | 0.9116   |
| 0.0185        | 6.63  | 2700  | 0.3509          | 0.9141   |
| 0.1201        | 6.88  | 2800  | 0.3330          | 0.9177   |
| 0.0766        | 7.13  | 2900  | 0.2718          | 0.9251   |
| 0.0998        | 7.37  | 3000  | 0.3471          | 0.9233   |
| 0.1654        | 7.62  | 3100  | 0.3285          | 0.9196   |
| 0.0529        | 7.86  | 3200  | 0.3394          | 0.9190   |
| 0.1199        | 8.11  | 3300  | 0.2968          | 0.9294   |
| 0.0338        | 8.35  | 3400  | 0.2784          | 0.9251   |
| 0.124         | 8.6   | 3500  | 0.3099          | 0.9251   |
| 0.0581        | 8.85  | 3600  | 0.3372          | 0.9263   |
| 0.1776        | 9.09  | 3700  | 0.3580          | 0.9134   |
| 0.1598        | 9.34  | 3800  | 0.3158          | 0.9196   |
| 0.1122        | 9.58  | 3900  | 0.3369          | 0.9190   |
| 0.0808        | 9.83  | 4000  | 0.3259          | 0.9368   |
| 0.1086        | 10.07 | 4100  | 0.3691          | 0.9190   |
| 0.0197        | 10.32 | 4200  | 0.3101          | 0.9355   |
| 0.065         | 10.57 | 4300  | 0.3479          | 0.9227   |
| 0.1183        | 10.81 | 4400  | 0.3281          | 0.9319   |
| 0.044         | 11.06 | 4500  | 0.4357          | 0.9134   |
| 0.1021        | 11.3  | 4600  | 0.3211          | 0.9337   |
| 0.0615        | 11.55 | 4700  | 0.2947          | 0.9398   |
| 0.0664        | 11.79 | 4800  | 0.4421          | 0.9184   |
| 0.092         | 12.04 | 4900  | 0.3333          | 0.9202   |
| 0.1544        | 12.29 | 5000  | 0.3062          | 0.9245   |
| 0.1324        | 12.53 | 5100  | 0.2756          | 0.9294   |
| 0.1132        | 12.78 | 5200  | 0.2570          | 0.9362   |
| 0.0899        | 13.02 | 5300  | 0.2486          | 0.9386   |
| 0.0712        | 13.27 | 5400  | 0.2878          | 0.9306   |
| 0.0411        | 13.51 | 5500  | 0.2663          | 0.9368   |
| 0.0559        | 13.76 | 5600  | 0.2751          | 0.9355   |
| 0.0928        | 14.0  | 5700  | 0.3093          | 0.9269   |
| 0.0504        | 14.25 | 5800  | 0.2954          | 0.9319   |
| 0.0995        | 14.5  | 5900  | 0.2636          | 0.9337   |
| 0.1139        | 14.74 | 6000  | 0.2827          | 0.9349   |
| 0.0992        | 14.99 | 6100  | 0.2662          | 0.9368   |
| 0.1519        | 15.23 | 6200  | 0.2720          | 0.9398   |
| 0.0192        | 15.48 | 6300  | 0.3252          | 0.9269   |
| 0.0592        | 15.72 | 6400  | 0.3382          | 0.9263   |
| 0.0382        | 15.97 | 6500  | 0.2710          | 0.9349   |
| 0.0723        | 16.22 | 6600  | 0.2671          | 0.9374   |
| 0.0073        | 16.46 | 6700  | 0.3451          | 0.9263   |
| 0.1796        | 16.71 | 6800  | 0.3196          | 0.9196   |
| 0.0919        | 16.95 | 6900  | 0.2464          | 0.9337   |
| 0.0739        | 17.2  | 7000  | 0.2258          | 0.9392   |
| 0.0468        | 17.44 | 7100  | 0.2483          | 0.9411   |
| 0.145         | 17.69 | 7200  | 0.2639          | 0.9312   |
| 0.0243        | 17.94 | 7300  | 0.2574          | 0.9362   |
| 0.0648        | 18.18 | 7400  | 0.2554          | 0.9331   |
| 0.0508        | 18.43 | 7500  | 0.2554          | 0.9374   |
| 0.0475        | 18.67 | 7600  | 0.2915          | 0.9337   |
| 0.0708        | 18.92 | 7700  | 0.2801          | 0.9300   |
| 0.1476        | 19.16 | 7800  | 0.2479          | 0.9411   |
| 0.1535        | 19.41 | 7900  | 0.2412          | 0.9411   |
| 0.0873        | 19.66 | 8000  | 0.2544          | 0.9398   |
| 0.0416        | 19.9  | 8100  | 0.2334          | 0.9423   |
| 0.1157        | 20.15 | 8200  | 0.2059          | 0.9540   |
| 0.039         | 20.39 | 8300  | 0.2601          | 0.9362   |
| 0.0223        | 20.64 | 8400  | 0.2234          | 0.9484   |
| 0.0779        | 20.88 | 8500  | 0.2468          | 0.9405   |
| 0.0604        | 21.13 | 8600  | 0.2334          | 0.9374   |
| 0.1206        | 21.38 | 8700  | 0.2504          | 0.9398   |
| 0.0738        | 21.62 | 8800  | 0.2505          | 0.9398   |
| 0.0438        | 21.87 | 8900  | 0.2148          | 0.9472   |
| 0.0689        | 22.11 | 9000  | 0.2286          | 0.9435   |
| 0.0505        | 22.36 | 9100  | 0.1956          | 0.9472   |
| 0.0581        | 22.6  | 9200  | 0.2104          | 0.9484   |
| 0.1575        | 22.85 | 9300  | 0.2309          | 0.9441   |
| 0.048         | 23.1  | 9400  | 0.2685          | 0.9398   |
| 0.0784        | 23.34 | 9500  | 0.2329          | 0.9454   |
| 0.0771        | 23.59 | 9600  | 0.2294          | 0.9466   |
| 0.0545        | 23.83 | 9700  | 0.2037          | 0.9484   |
| 0.0481        | 24.08 | 9800  | 0.1994          | 0.9540   |
| 0.0663        | 24.32 | 9900  | 0.1993          | 0.9490   |
| 0.0921        | 24.57 | 10000 | 0.2204          | 0.9521   |
| 0.0939        | 24.82 | 10100 | 0.2362          | 0.9454   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

black-grass charlock small-flowered cranesbill sugar beet cleavers common chickweed common wheat fat hen loose silky-bent maize scentless mayweed shepherds purse