
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ebayes/tree-crown-latest

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6589
- Accuracy: 0.8636

## 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 36   | 1.5994          | 0.6364   |
| No log        | 2.0   | 72   | 1.2587          | 0.6818   |
| No log        | 3.0   | 108  | 1.0993          | 0.7045   |
| No log        | 4.0   | 144  | 0.9721          | 0.7955   |
| No log        | 5.0   | 180  | 0.9282          | 0.7955   |
| No log        | 6.0   | 216  | 0.8947          | 0.7955   |
| No log        | 7.0   | 252  | 0.8858          | 0.7727   |
| No log        | 8.0   | 288  | 0.8159          | 0.7955   |
| No log        | 9.0   | 324  | 0.7772          | 0.7727   |
| No log        | 10.0  | 360  | 0.7519          | 0.7955   |
| No log        | 11.0  | 396  | 0.6982          | 0.7955   |
| No log        | 12.0  | 432  | 0.6639          | 0.7955   |
| No log        | 13.0  | 468  | 0.6690          | 0.8409   |
| 0.6601        | 14.0  | 504  | 0.6565          | 0.8409   |
| 0.6601        | 15.0  | 540  | 0.6401          | 0.8409   |
| 0.6601        | 16.0  | 576  | 0.5868          | 0.8864   |
| 0.6601        | 17.0  | 612  | 0.5840          | 0.8864   |
| 0.6601        | 18.0  | 648  | 0.6214          | 0.8409   |
| 0.6601        | 19.0  | 684  | 0.6447          | 0.8636   |
| 0.6601        | 20.0  | 720  | 0.6387          | 0.8409   |
| 0.6601        | 21.0  | 756  | 0.5714          | 0.8636   |
| 0.6601        | 22.0  | 792  | 0.5483          | 0.8864   |
| 0.6601        | 23.0  | 828  | 0.5600          | 0.8864   |
| 0.6601        | 24.0  | 864  | 0.5785          | 0.8864   |
| 0.6601        | 25.0  | 900  | 0.5806          | 0.8864   |
| 0.6601        | 26.0  | 936  | 0.5598          | 0.8636   |
| 0.6601        | 27.0  | 972  | 0.5549          | 0.8864   |
| 0.1909        | 28.0  | 1008 | 0.5145          | 0.8864   |
| 0.1909        | 29.0  | 1044 | 0.5294          | 0.8636   |
| 0.1909        | 30.0  | 1080 | 0.5846          | 0.8636   |
| 0.1909        | 31.0  | 1116 | 0.5347          | 0.8864   |
| 0.1909        | 32.0  | 1152 | 0.5251          | 0.8864   |
| 0.1909        | 33.0  | 1188 | 0.5193          | 0.8864   |
| 0.1909        | 34.0  | 1224 | 0.6406          | 0.8409   |
| 0.1909        | 35.0  | 1260 | 0.5039          | 0.8864   |
| 0.1909        | 36.0  | 1296 | 0.5137          | 0.8864   |
| 0.1909        | 37.0  | 1332 | 0.6023          | 0.8636   |
| 0.1909        | 38.0  | 1368 | 0.5625          | 0.8864   |
| 0.1909        | 39.0  | 1404 | 0.5752          | 0.8864   |
| 0.1909        | 40.0  | 1440 | 0.5903          | 0.8864   |
| 0.1909        | 41.0  | 1476 | 0.5143          | 0.8864   |
| 0.0968        | 42.0  | 1512 | 0.5261          | 0.8864   |
| 0.0968        | 43.0  | 1548 | 0.5942          | 0.8864   |
| 0.0968        | 44.0  | 1584 | 0.6026          | 0.8636   |
| 0.0968        | 45.0  | 1620 | 0.5638          | 0.8864   |
| 0.0968        | 46.0  | 1656 | 0.6019          | 0.8864   |
| 0.0968        | 47.0  | 1692 | 0.5953          | 0.8864   |
| 0.0968        | 48.0  | 1728 | 0.6043          | 0.8864   |
| 0.0968        | 49.0  | 1764 | 0.5866          | 0.8864   |
| 0.0968        | 50.0  | 1800 | 0.5090          | 0.8864   |
| 0.0968        | 51.0  | 1836 | 0.5704          | 0.8864   |
| 0.0968        | 52.0  | 1872 | 0.5579          | 0.8636   |
| 0.0968        | 53.0  | 1908 | 0.5058          | 0.8864   |
| 0.0968        | 54.0  | 1944 | 0.5418          | 0.8864   |
| 0.0968        | 55.0  | 1980 | 0.5708          | 0.8864   |
| 0.0656        | 56.0  | 2016 | 0.5818          | 0.8864   |
| 0.0656        | 57.0  | 2052 | 0.5539          | 0.8864   |
| 0.0656        | 58.0  | 2088 | 0.5849          | 0.8864   |
| 0.0656        | 59.0  | 2124 | 0.5396          | 0.8864   |
| 0.0656        | 60.0  | 2160 | 0.5631          | 0.8864   |
| 0.0656        | 61.0  | 2196 | 0.5919          | 0.8864   |
| 0.0656        | 62.0  | 2232 | 0.5955          | 0.8864   |
| 0.0656        | 63.0  | 2268 | 0.5438          | 0.8864   |
| 0.0656        | 64.0  | 2304 | 0.5989          | 0.8636   |
| 0.0656        | 65.0  | 2340 | 0.5062          | 0.8864   |
| 0.0656        | 66.0  | 2376 | 0.5820          | 0.8636   |
| 0.0656        | 67.0  | 2412 | 0.5301          | 0.8864   |
| 0.0656        | 68.0  | 2448 | 0.6138          | 0.8864   |
| 0.0656        | 69.0  | 2484 | 0.5710          | 0.8636   |
| 0.0491        | 70.0  | 2520 | 0.6141          | 0.8636   |
| 0.0491        | 71.0  | 2556 | 0.6304          | 0.8636   |
| 0.0491        | 72.0  | 2592 | 0.5568          | 0.8636   |
| 0.0491        | 73.0  | 2628 | 0.6437          | 0.8636   |
| 0.0491        | 74.0  | 2664 | 0.5329          | 0.8864   |
| 0.0491        | 75.0  | 2700 | 0.6453          | 0.8864   |
| 0.0491        | 76.0  | 2736 | 0.6267          | 0.8636   |
| 0.0491        | 77.0  | 2772 | 0.6246          | 0.8636   |
| 0.0491        | 78.0  | 2808 | 0.6408          | 0.8636   |
| 0.0491        | 79.0  | 2844 | 0.6208          | 0.8636   |
| 0.0491        | 80.0  | 2880 | 0.5944          | 0.8636   |
| 0.0491        | 81.0  | 2916 | 0.6848          | 0.8636   |
| 0.0491        | 82.0  | 2952 | 0.6700          | 0.8409   |
| 0.0491        | 83.0  | 2988 | 0.5625          | 0.8864   |
| 0.0474        | 84.0  | 3024 | 0.4997          | 0.8864   |
| 0.0474        | 85.0  | 3060 | 0.6110          | 0.8864   |
| 0.0474        | 86.0  | 3096 | 0.5661          | 0.8864   |
| 0.0474        | 87.0  | 3132 | 0.5681          | 0.8864   |
| 0.0474        | 88.0  | 3168 | 0.5794          | 0.8636   |
| 0.0474        | 89.0  | 3204 | 0.6098          | 0.8864   |
| 0.0474        | 90.0  | 3240 | 0.6009          | 0.8636   |
| 0.0474        | 91.0  | 3276 | 0.5000          | 0.8864   |
| 0.0474        | 92.0  | 3312 | 0.5285          | 0.8864   |
| 0.0474        | 93.0  | 3348 | 0.5774          | 0.8864   |
| 0.0474        | 94.0  | 3384 | 0.5896          | 0.8864   |
| 0.0474        | 95.0  | 3420 | 0.5478          | 0.8864   |
| 0.0474        | 96.0  | 3456 | 0.5815          | 0.8864   |
| 0.0474        | 97.0  | 3492 | 0.5675          | 0.8864   |
| 0.0393        | 98.0  | 3528 | 0.5773          | 0.8864   |
| 0.0393        | 99.0  | 3564 | 0.6099          | 0.8864   |
| 0.0393        | 100.0 | 3600 | 0.7255          | 0.8409   |
| 0.0393        | 101.0 | 3636 | 0.6300          | 0.8864   |
| 0.0393        | 102.0 | 3672 | 0.5979          | 0.8409   |
| 0.0393        | 103.0 | 3708 | 0.6031          | 0.8864   |
| 0.0393        | 104.0 | 3744 | 0.6200          | 0.8864   |
| 0.0393        | 105.0 | 3780 | 0.6120          | 0.8864   |
| 0.0393        | 106.0 | 3816 | 0.5514          | 0.9091   |
| 0.0393        | 107.0 | 3852 | 0.6425          | 0.8864   |
| 0.0393        | 108.0 | 3888 | 0.6152          | 0.8864   |
| 0.0393        | 109.0 | 3924 | 0.6023          | 0.8864   |
| 0.0393        | 110.0 | 3960 | 0.6170          | 0.8864   |
| 0.0393        | 111.0 | 3996 | 0.6556          | 0.8864   |
| 0.0404        | 112.0 | 4032 | 0.6380          | 0.8864   |
| 0.0404        | 113.0 | 4068 | 0.6216          | 0.8864   |
| 0.0404        | 114.0 | 4104 | 0.5775          | 0.8864   |
| 0.0404        | 115.0 | 4140 | 0.6120          | 0.8864   |
| 0.0404        | 116.0 | 4176 | 0.6221          | 0.8864   |
| 0.0404        | 117.0 | 4212 | 0.6807          | 0.8636   |
| 0.0404        | 118.0 | 4248 | 0.6805          | 0.8636   |
| 0.0404        | 119.0 | 4284 | 0.6660          | 0.8636   |
| 0.0404        | 120.0 | 4320 | 0.6626          | 0.8636   |
| 0.0404        | 121.0 | 4356 | 0.6656          | 0.8636   |
| 0.0404        | 122.0 | 4392 | 0.6151          | 0.8636   |
| 0.0404        | 123.0 | 4428 | 0.6525          | 0.8636   |
| 0.0404        | 124.0 | 4464 | 0.6311          | 0.8636   |
| 0.0268        | 125.0 | 4500 | 0.6375          | 0.8636   |
| 0.0268        | 126.0 | 4536 | 0.6252          | 0.8636   |
| 0.0268        | 127.0 | 4572 | 0.6182          | 0.8409   |
| 0.0268        | 128.0 | 4608 | 0.6195          | 0.8636   |
| 0.0268        | 129.0 | 4644 | 0.6417          | 0.8636   |
| 0.0268        | 130.0 | 4680 | 0.6440          | 0.8636   |
| 0.0268        | 131.0 | 4716 | 0.6726          | 0.8636   |
| 0.0268        | 132.0 | 4752 | 0.6781          | 0.8636   |
| 0.0268        | 133.0 | 4788 | 0.6412          | 0.8636   |
| 0.0268        | 134.0 | 4824 | 0.6514          | 0.8636   |
| 0.0268        | 135.0 | 4860 | 0.6452          | 0.8636   |
| 0.0268        | 136.0 | 4896 | 0.6453          | 0.8864   |
| 0.0268        | 137.0 | 4932 | 0.6408          | 0.8864   |
| 0.0268        | 138.0 | 4968 | 0.6461          | 0.8864   |
| 0.0244        | 139.0 | 5004 | 0.6597          | 0.8864   |
| 0.0244        | 140.0 | 5040 | 0.6539          | 0.8864   |
| 0.0244        | 141.0 | 5076 | 0.6415          | 0.8864   |
| 0.0244        | 142.0 | 5112 | 0.6438          | 0.8864   |
| 0.0244        | 143.0 | 5148 | 0.6581          | 0.8636   |
| 0.0244        | 144.0 | 5184 | 0.6570          | 0.8636   |
| 0.0244        | 145.0 | 5220 | 0.6626          | 0.8636   |
| 0.0244        | 146.0 | 5256 | 0.6622          | 0.8636   |
| 0.0244        | 147.0 | 5292 | 0.6647          | 0.8636   |
| 0.0244        | 148.0 | 5328 | 0.6619          | 0.8636   |
| 0.0244        | 149.0 | 5364 | 0.6591          | 0.8636   |
| 0.0244        | 150.0 | 5400 | 0.6589          | 0.8636   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

cecropia cecropia_concolor cecropia_sciadophylla cecropia_sp_branches distinctive rosette euterpe fallen_tree flowering_canopy mauritia_flexuosa oenocarpus_bacaba