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

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: 0.2030
- Accuracy: 0.9401

## 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: 11
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5655        | 0.2   | 100  | 0.7734          | 0.7520   |
| 0.663         | 0.39  | 200  | 0.5191          | 0.8276   |
| 0.4009        | 0.59  | 300  | 0.6602          | 0.7706   |
| 0.6175        | 0.79  | 400  | 0.5263          | 0.8129   |
| 0.2571        | 0.98  | 500  | 0.5162          | 0.8502   |
| 0.3698        | 1.18  | 600  | 0.4021          | 0.8684   |
| 0.4092        | 1.38  | 700  | 0.3559          | 0.8787   |
| 0.2506        | 1.57  | 800  | 0.4771          | 0.8571   |
| 0.2597        | 1.77  | 900  | 0.4663          | 0.8517   |
| 0.4016        | 1.96  | 1000 | 0.3367          | 0.8802   |
| 0.2899        | 2.16  | 1100 | 0.3276          | 0.8959   |
| 0.2713        | 2.36  | 1200 | 0.3035          | 0.8988   |
| 0.2707        | 2.55  | 1300 | 0.3661          | 0.8846   |
| 0.4251        | 2.75  | 1400 | 0.3720          | 0.8669   |
| 0.1669        | 2.95  | 1500 | 0.3323          | 0.8870   |
| 0.3079        | 3.14  | 1600 | 0.3322          | 0.8875   |
| 0.2596        | 3.34  | 1700 | 0.3666          | 0.8969   |
| 0.3019        | 3.54  | 1800 | 0.2772          | 0.9023   |
| 0.3429        | 3.73  | 1900 | 0.2936          | 0.9037   |
| 0.2508        | 3.93  | 2000 | 0.3525          | 0.8846   |
| 0.2212        | 4.13  | 2100 | 0.3199          | 0.8934   |
| 0.3104        | 4.32  | 2200 | 0.3546          | 0.8915   |
| 0.1682        | 4.52  | 2300 | 0.2823          | 0.8939   |
| 0.2014        | 4.72  | 2400 | 0.2813          | 0.9150   |
| 0.2805        | 4.91  | 2500 | 0.2907          | 0.9077   |
| 0.1471        | 5.11  | 2600 | 0.2811          | 0.9091   |
| 0.179         | 5.3   | 2700 | 0.2752          | 0.9106   |
| 0.1992        | 5.5   | 2800 | 0.2894          | 0.9072   |
| 0.1635        | 5.7   | 2900 | 0.2397          | 0.9194   |
| 0.2045        | 5.89  | 3000 | 0.2717          | 0.9037   |
| 0.1893        | 6.09  | 3100 | 0.2339          | 0.9273   |
| 0.2664        | 6.29  | 3200 | 0.2772          | 0.9131   |
| 0.1991        | 6.48  | 3300 | 0.2475          | 0.9234   |
| 0.0713        | 6.68  | 3400 | 0.2509          | 0.9185   |
| 0.1968        | 6.88  | 3500 | 0.2410          | 0.9194   |
| 0.1378        | 7.07  | 3600 | 0.2177          | 0.9288   |
| 0.1609        | 7.27  | 3700 | 0.2182          | 0.9214   |
| 0.1001        | 7.47  | 3800 | 0.2110          | 0.9317   |
| 0.097         | 7.66  | 3900 | 0.2949          | 0.9224   |
| 0.1234        | 7.86  | 4000 | 0.2365          | 0.9337   |
| 0.1572        | 8.06  | 4100 | 0.2352          | 0.9283   |
| 0.1402        | 8.25  | 4200 | 0.2299          | 0.9258   |
| 0.1089        | 8.45  | 4300 | 0.2465          | 0.9298   |
| 0.1376        | 8.64  | 4400 | 0.2257          | 0.9298   |
| 0.0911        | 8.84  | 4500 | 0.2057          | 0.9342   |
| 0.1406        | 9.04  | 4600 | 0.2064          | 0.9386   |
| 0.1318        | 9.23  | 4700 | 0.2241          | 0.9347   |
| 0.052         | 9.43  | 4800 | 0.1821          | 0.9425   |
| 0.106         | 9.63  | 4900 | 0.2407          | 0.9283   |
| 0.0975        | 9.82  | 5000 | 0.2013          | 0.9381   |
| 0.1277        | 10.02 | 5100 | 0.1717          | 0.9445   |
| 0.0859        | 10.22 | 5200 | 0.2233          | 0.9352   |
| 0.1242        | 10.41 | 5300 | 0.2232          | 0.9361   |
| 0.0355        | 10.61 | 5400 | 0.1898          | 0.9381   |
| 0.1613        | 10.81 | 5500 | 0.2030          | 0.9401   |


### 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