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# plant-seedlings-model-beit-free-0-6

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7557
- Accuracy: 0.7475

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.4892        | 0.2   | 100   | 2.4909          | 0.0751   |
| 2.4906        | 0.39  | 200   | 2.4886          | 0.0756   |
| 2.3925        | 0.59  | 300   | 2.3344          | 0.1537   |
| 2.31          | 0.79  | 400   | 2.3306          | 0.1464   |
| 2.2355        | 0.98  | 500   | 2.2335          | 0.1778   |
| 2.2642        | 1.18  | 600   | 2.1889          | 0.1807   |
| 2.0806        | 1.38  | 700   | 2.3229          | 0.1680   |
| 2.1013        | 1.57  | 800   | 2.1519          | 0.2004   |
| 2.0094        | 1.77  | 900   | 2.0611          | 0.2146   |
| 2.0387        | 1.96  | 1000  | 2.0413          | 0.2210   |
| 2.0032        | 2.16  | 1100  | 1.9758          | 0.2618   |
| 1.986         | 2.36  | 1200  | 1.9238          | 0.2638   |
| 2.0885        | 2.55  | 1300  | 1.8944          | 0.2942   |
| 1.8808        | 2.75  | 1400  | 1.9330          | 0.2868   |
| 1.915         | 2.95  | 1500  | 1.8919          | 0.2814   |
| 1.958         | 3.14  | 1600  | 1.8762          | 0.3114   |
| 1.9001        | 3.34  | 1700  | 1.8389          | 0.3232   |
| 1.8572        | 3.54  | 1800  | 1.7978          | 0.3487   |
| 1.9969        | 3.73  | 1900  | 1.9371          | 0.3089   |
| 1.9186        | 3.93  | 2000  | 1.8055          | 0.3502   |
| 1.7591        | 4.13  | 2100  | 1.7695          | 0.3428   |
| 1.8368        | 4.32  | 2200  | 1.7498          | 0.3502   |
| 1.9842        | 4.52  | 2300  | 1.8049          | 0.3193   |
| 1.7606        | 4.72  | 2400  | 1.6730          | 0.3954   |
| 1.7787        | 4.91  | 2500  | 1.7104          | 0.3777   |
| 1.6377        | 5.11  | 2600  | 1.6647          | 0.3870   |
| 1.8834        | 5.3   | 2700  | 1.6325          | 0.3973   |
| 1.6149        | 5.5   | 2800  | 1.6722          | 0.3787   |
| 1.7038        | 5.7   | 2900  | 1.6425          | 0.3973   |
| 1.682         | 5.89  | 3000  | 1.5927          | 0.4180   |
| 1.6326        | 6.09  | 3100  | 1.4982          | 0.4622   |
| 1.5687        | 6.29  | 3200  | 1.4440          | 0.4774   |
| 1.3637        | 6.48  | 3300  | 1.4477          | 0.4877   |
| 1.4079        | 6.68  | 3400  | 1.3827          | 0.5020   |
| 1.3721        | 6.88  | 3500  | 1.4069          | 0.5010   |
| 1.5675        | 7.07  | 3600  | 1.3595          | 0.5083   |
| 1.5725        | 7.27  | 3700  | 1.3790          | 0.4956   |
| 1.4522        | 7.47  | 3800  | 1.3116          | 0.5378   |
| 1.4692        | 7.66  | 3900  | 1.3729          | 0.4980   |
| 1.5073        | 7.86  | 4000  | 1.3799          | 0.5216   |
| 1.2529        | 8.06  | 4100  | 1.2706          | 0.5486   |
| 1.3727        | 8.25  | 4200  | 1.2519          | 0.5535   |
| 1.2451        | 8.45  | 4300  | 1.2595          | 0.5648   |
| 1.339         | 8.64  | 4400  | 1.3614          | 0.5172   |
| 1.2858        | 8.84  | 4500  | 1.3028          | 0.5393   |
| 1.1039        | 9.04  | 4600  | 1.2309          | 0.5771   |
| 1.0351        | 9.23  | 4700  | 1.2678          | 0.5609   |
| 1.1125        | 9.43  | 4800  | 1.2786          | 0.5624   |
| 1.1667        | 9.63  | 4900  | 1.2131          | 0.5840   |
| 1.1386        | 9.82  | 5000  | 1.1359          | 0.6154   |
| 1.1888        | 10.02 | 5100  | 1.1309          | 0.6041   |
| 1.1777        | 10.22 | 5200  | 1.1288          | 0.6287   |
| 1.3693        | 10.41 | 5300  | 1.3827          | 0.5182   |
| 1.1016        | 10.61 | 5400  | 1.2255          | 0.5594   |
| 1.1527        | 10.81 | 5500  | 1.0772          | 0.6434   |
| 1.1039        | 11.0  | 5600  | 1.1032          | 0.6100   |
| 1.2502        | 11.2  | 5700  | 1.1230          | 0.6169   |
| 1.0818        | 11.39 | 5800  | 1.0750          | 0.6302   |
| 1.0872        | 11.59 | 5900  | 1.0397          | 0.6331   |
| 1.0425        | 11.79 | 6000  | 1.0231          | 0.6483   |
| 1.0791        | 11.98 | 6100  | 1.0250          | 0.6636   |
| 0.9736        | 12.18 | 6200  | 1.0879          | 0.6267   |
| 0.9788        | 12.38 | 6300  | 1.1334          | 0.5968   |
| 0.8982        | 12.57 | 6400  | 0.9934          | 0.6528   |
| 1.077         | 12.77 | 6500  | 0.9698          | 0.6812   |
| 1.0347        | 12.97 | 6600  | 1.0265          | 0.6513   |
| 0.9159        | 13.16 | 6700  | 0.9442          | 0.6788   |
| 1.1187        | 13.36 | 6800  | 0.9738          | 0.6685   |
| 0.9624        | 13.56 | 6900  | 1.0008          | 0.6699   |
| 0.922         | 13.75 | 7000  | 0.9502          | 0.6906   |
| 0.9317        | 13.95 | 7100  | 0.9687          | 0.6758   |
| 0.9979        | 14.15 | 7200  | 0.9869          | 0.6768   |
| 0.8362        | 14.34 | 7300  | 0.9220          | 0.6994   |
| 0.8449        | 14.54 | 7400  | 0.9181          | 0.6861   |
| 0.9678        | 14.73 | 7500  | 0.9789          | 0.6729   |
| 0.9119        | 14.93 | 7600  | 0.8879          | 0.7009   |
| 0.9517        | 15.13 | 7700  | 0.8816          | 0.6994   |
| 0.9688        | 15.32 | 7800  | 0.8803          | 0.7117   |
| 0.8625        | 15.52 | 7900  | 0.8782          | 0.7038   |
| 0.9121        | 15.72 | 8000  | 0.8225          | 0.7191   |
| 0.9035        | 15.91 | 8100  | 0.8649          | 0.7087   |
| 0.8762        | 16.11 | 8200  | 0.8427          | 0.7102   |
| 0.7708        | 16.31 | 8300  | 0.8685          | 0.7117   |
| 0.8893        | 16.5  | 8400  | 0.8178          | 0.7264   |
| 0.9584        | 16.7  | 8500  | 0.8709          | 0.7092   |
| 0.757         | 16.9  | 8600  | 0.8244          | 0.7254   |
| 0.8184        | 17.09 | 8700  | 0.8128          | 0.7240   |
| 0.8858        | 17.29 | 8800  | 0.8360          | 0.7156   |
| 0.7116        | 17.49 | 8900  | 0.7952          | 0.7279   |
| 0.9579        | 17.68 | 9000  | 0.8263          | 0.7274   |
| 0.7037        | 17.88 | 9100  | 0.7884          | 0.7348   |
| 1.0359        | 18.07 | 9200  | 0.8118          | 0.7402   |
| 1.067         | 18.27 | 9300  | 0.8203          | 0.7186   |
| 0.8503        | 18.47 | 9400  | 0.7918          | 0.7362   |
| 0.8552        | 18.66 | 9500  | 0.7972          | 0.7382   |
| 0.7498        | 18.86 | 9600  | 0.8038          | 0.7343   |
| 0.8542        | 19.06 | 9700  | 0.7799          | 0.7333   |
| 0.9539        | 19.25 | 9800  | 0.7795          | 0.7333   |
| 0.7369        | 19.45 | 9900  | 0.8103          | 0.7269   |
| 0.6637        | 19.65 | 10000 | 0.7597          | 0.7441   |
| 0.6712        | 19.84 | 10100 | 0.7557          | 0.7475   |


### 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