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# plant-seedlings-freeze-0-6-aug-3-whole-data-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: 0.0001
- Accuracy: 1.0

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6146        | 0.16  | 100   | 0.0402          | 1.0      |
| 0.6062        | 0.31  | 200   | 0.2393          | 1.0      |
| 0.4847        | 0.47  | 300   | 0.2164          | 1.0      |
| 0.5282        | 0.63  | 400   | 0.0427          | 1.0      |
| 0.4153        | 0.79  | 500   | 1.0996          | 0.0      |
| 0.3295        | 0.94  | 600   | 0.0499          | 1.0      |
| 0.3541        | 1.1   | 700   | 0.0009          | 1.0      |
| 0.4617        | 1.26  | 800   | 0.0106          | 1.0      |
| 0.3014        | 1.42  | 900   | 0.0045          | 1.0      |
| 0.3558        | 1.57  | 1000  | 0.0038          | 1.0      |
| 0.2357        | 1.73  | 1100  | 0.0140          | 1.0      |
| 0.3055        | 1.89  | 1200  | 0.0809          | 1.0      |
| 0.3278        | 2.04  | 1300  | 0.0077          | 1.0      |
| 0.3075        | 2.2   | 1400  | 0.0059          | 1.0      |
| 0.3462        | 2.36  | 1500  | 0.0377          | 1.0      |
| 0.2968        | 2.52  | 1600  | 0.0082          | 1.0      |
| 0.3392        | 2.67  | 1700  | 0.8628          | 0.0      |
| 0.2155        | 2.83  | 1800  | 0.0022          | 1.0      |
| 0.3521        | 2.99  | 1900  | 0.0671          | 1.0      |
| 0.3968        | 3.14  | 2000  | 0.0014          | 1.0      |
| 0.32          | 3.3   | 2100  | 0.0075          | 1.0      |
| 0.1787        | 3.46  | 2200  | 0.0015          | 1.0      |
| 0.2598        | 3.62  | 2300  | 0.0086          | 1.0      |
| 0.3424        | 3.77  | 2400  | 0.0008          | 1.0      |
| 0.2371        | 3.93  | 2500  | 0.0054          | 1.0      |
| 0.2773        | 4.09  | 2600  | 0.0028          | 1.0      |
| 0.3192        | 4.25  | 2700  | 0.0088          | 1.0      |
| 0.2173        | 4.4   | 2800  | 0.1174          | 1.0      |
| 0.2181        | 4.56  | 2900  | 0.0056          | 1.0      |
| 0.2476        | 4.72  | 3000  | 0.0006          | 1.0      |
| 0.2417        | 4.87  | 3100  | 0.0005          | 1.0      |
| 0.1915        | 5.03  | 3200  | 0.0002          | 1.0      |
| 0.149         | 5.19  | 3300  | 0.0004          | 1.0      |
| 0.1618        | 5.35  | 3400  | 0.1542          | 1.0      |
| 0.1752        | 5.5   | 3500  | 0.0001          | 1.0      |
| 0.1094        | 5.66  | 3600  | 0.0045          | 1.0      |
| 0.2532        | 5.82  | 3700  | 0.0016          | 1.0      |
| 0.1606        | 5.97  | 3800  | 0.0004          | 1.0      |
| 0.1781        | 6.13  | 3900  | 0.0007          | 1.0      |
| 0.1459        | 6.29  | 4000  | 0.0003          | 1.0      |
| 0.2357        | 6.45  | 4100  | 2.6113          | 0.0      |
| 0.2524        | 6.6   | 4200  | 0.0003          | 1.0      |
| 0.1708        | 6.76  | 4300  | 0.0006          | 1.0      |
| 0.1875        | 6.92  | 4400  | 0.0011          | 1.0      |
| 0.1462        | 7.08  | 4500  | 0.0004          | 1.0      |
| 0.1534        | 7.23  | 4600  | 0.0002          | 1.0      |
| 0.2834        | 7.39  | 4700  | 0.0003          | 1.0      |
| 0.2264        | 7.55  | 4800  | 0.0001          | 1.0      |
| 0.1007        | 7.7   | 4900  | 0.0001          | 1.0      |
| 0.2376        | 7.86  | 5000  | 0.0006          | 1.0      |
| 0.2233        | 8.02  | 5100  | 0.0002          | 1.0      |
| 0.1804        | 8.18  | 5200  | 0.0034          | 1.0      |
| 0.185         | 8.33  | 5300  | 0.0002          | 1.0      |
| 0.1149        | 8.49  | 5400  | 0.0007          | 1.0      |
| 0.2048        | 8.65  | 5500  | 0.0009          | 1.0      |
| 0.0786        | 8.81  | 5600  | 0.9478          | 0.0      |
| 0.2222        | 8.96  | 5700  | 0.0007          | 1.0      |
| 0.1289        | 9.12  | 5800  | 0.0009          | 1.0      |
| 0.2248        | 9.28  | 5900  | 0.0005          | 1.0      |
| 0.0987        | 9.43  | 6000  | 0.0002          | 1.0      |
| 0.2897        | 9.59  | 6100  | 0.0002          | 1.0      |
| 0.2023        | 9.75  | 6200  | 0.0042          | 1.0      |
| 0.1481        | 9.91  | 6300  | 0.0003          | 1.0      |
| 0.1224        | 10.06 | 6400  | 0.0009          | 1.0      |
| 0.1353        | 10.22 | 6500  | 0.0080          | 1.0      |
| 0.0659        | 10.38 | 6600  | 0.0006          | 1.0      |
| 0.1692        | 10.53 | 6700  | 0.0005          | 1.0      |
| 0.1713        | 10.69 | 6800  | 0.0006          | 1.0      |
| 0.1131        | 10.85 | 6900  | 0.0012          | 1.0      |
| 0.2325        | 11.01 | 7000  | 0.0003          | 1.0      |
| 0.0817        | 11.16 | 7100  | 0.0003          | 1.0      |
| 0.1854        | 11.32 | 7200  | 0.0001          | 1.0      |
| 0.0956        | 11.48 | 7300  | 0.0002          | 1.0      |
| 0.0758        | 11.64 | 7400  | 0.0127          | 1.0      |
| 0.0928        | 11.79 | 7500  | 0.0002          | 1.0      |
| 0.1563        | 11.95 | 7600  | 0.0004          | 1.0      |
| 0.0596        | 12.11 | 7700  | 0.0003          | 1.0      |
| 0.1266        | 12.26 | 7800  | 0.0031          | 1.0      |
| 0.1788        | 12.42 | 7900  | 0.0002          | 1.0      |
| 0.1663        | 12.58 | 8000  | 0.0071          | 1.0      |
| 0.064         | 12.74 | 8100  | 0.0003          | 1.0      |
| 0.1459        | 12.89 | 8200  | 0.0005          | 1.0      |
| 0.1237        | 13.05 | 8300  | 0.0001          | 1.0      |
| 0.1334        | 13.21 | 8400  | 0.0001          | 1.0      |
| 0.0802        | 13.36 | 8500  | 0.0001          | 1.0      |
| 0.1418        | 13.52 | 8600  | 0.0000          | 1.0      |
| 0.048         | 13.68 | 8700  | 0.0001          | 1.0      |
| 0.1267        | 13.84 | 8800  | 0.0121          | 1.0      |
| 0.1298        | 13.99 | 8900  | 0.0001          | 1.0      |
| 0.16          | 14.15 | 9000  | 0.0001          | 1.0      |
| 0.1295        | 14.31 | 9100  | 0.0001          | 1.0      |
| 0.1714        | 14.47 | 9200  | 0.0001          | 1.0      |
| 0.1377        | 14.62 | 9300  | 0.0001          | 1.0      |
| 0.1336        | 14.78 | 9400  | 0.0001          | 1.0      |
| 0.1293        | 14.94 | 9500  | 0.0001          | 1.0      |
| 0.111         | 15.09 | 9600  | 0.0001          | 1.0      |
| 0.0818        | 15.25 | 9700  | 0.0000          | 1.0      |
| 0.1884        | 15.41 | 9800  | 0.0001          | 1.0      |
| 0.1004        | 15.57 | 9900  | 0.0002          | 1.0      |
| 0.1029        | 15.72 | 10000 | 0.0000          | 1.0      |
| 0.0772        | 15.88 | 10100 | 0.0000          | 1.0      |
| 0.1573        | 16.04 | 10200 | 0.0001          | 1.0      |
| 0.0748        | 16.19 | 10300 | 0.0001          | 1.0      |
| 0.088         | 16.35 | 10400 | 0.0001          | 1.0      |
| 0.1062        | 16.51 | 10500 | 0.0001          | 1.0      |
| 0.0237        | 16.67 | 10600 | 0.0001          | 1.0      |
| 0.0729        | 16.82 | 10700 | 0.0000          | 1.0      |
| 0.1028        | 16.98 | 10800 | 0.0001          | 1.0      |
| 0.0423        | 17.14 | 10900 | 0.0000          | 1.0      |
| 0.0922        | 17.3  | 11000 | 0.0002          | 1.0      |
| 0.0788        | 17.45 | 11100 | 0.0001          | 1.0      |
| 0.0357        | 17.61 | 11200 | 0.0001          | 1.0      |
| 0.0519        | 17.77 | 11300 | 0.0000          | 1.0      |
| 0.108         | 17.92 | 11400 | 0.0001          | 1.0      |
| 0.1746        | 18.08 | 11500 | 0.1221          | 1.0      |
| 0.1           | 18.24 | 11600 | 0.0006          | 1.0      |
| 0.0798        | 18.4  | 11700 | 0.0001          | 1.0      |
| 0.0118        | 18.55 | 11800 | 0.0001          | 1.0      |
| 0.1151        | 18.71 | 11900 | 0.0001          | 1.0      |
| 0.0617        | 18.87 | 12000 | 0.0001          | 1.0      |
| 0.1577        | 19.03 | 12100 | 0.0001          | 1.0      |
| 0.1928        | 19.18 | 12200 | 0.0001          | 1.0      |
| 0.0462        | 19.34 | 12300 | 0.0001          | 1.0      |
| 0.0461        | 19.5  | 12400 | 0.3145          | 1.0      |
| 0.0454        | 19.65 | 12500 | 0.0001          | 1.0      |
| 0.0637        | 19.81 | 12600 | 0.0001          | 1.0      |
| 0.0733        | 19.97 | 12700 | 0.0001          | 1.0      |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "plant-seedlings-freeze-0-6-aug-3-whole-data-train", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

plant-seedlings-freeze-0-6-aug-3-whole-data-train
=================================================


This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0001
* Accuracy: 1.0


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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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