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

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1722
- Accuracy: 0.9425

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.799         | 0.2   | 100  | 0.5965          | 0.7976   |
| 0.7161        | 0.39  | 200  | 0.7085          | 0.7633   |
| 0.7159        | 0.59  | 300  | 0.5736          | 0.8094   |
| 0.5998        | 0.79  | 400  | 0.5116          | 0.8202   |
| 0.4901        | 0.98  | 500  | 0.4973          | 0.8468   |
| 0.4669        | 1.18  | 600  | 0.5753          | 0.8021   |
| 0.418         | 1.38  | 700  | 0.5035          | 0.8310   |
| 0.2675        | 1.57  | 800  | 0.4583          | 0.8502   |
| 0.3409        | 1.77  | 900  | 0.4194          | 0.8600   |
| 0.5934        | 1.96  | 1000 | 0.4950          | 0.8320   |
| 0.3727        | 2.16  | 1100 | 0.5086          | 0.8222   |
| 0.2635        | 2.36  | 1200 | 0.4315          | 0.8551   |
| 0.3595        | 2.55  | 1300 | 0.3716          | 0.8728   |
| 0.4091        | 2.75  | 1400 | 0.3692          | 0.8787   |
| 0.3579        | 2.95  | 1500 | 0.3331          | 0.8865   |
| 0.3034        | 3.14  | 1600 | 0.3121          | 0.8895   |
| 0.2943        | 3.34  | 1700 | 0.3551          | 0.8816   |
| 0.3346        | 3.54  | 1800 | 0.4268          | 0.8625   |
| 0.3261        | 3.73  | 1900 | 0.3222          | 0.8880   |
| 0.3287        | 3.93  | 2000 | 0.3072          | 0.8964   |
| 0.2753        | 4.13  | 2100 | 0.3209          | 0.8910   |
| 0.1975        | 4.32  | 2200 | 0.3564          | 0.8757   |
| 0.2291        | 4.52  | 2300 | 0.3057          | 0.9003   |
| 0.232         | 4.72  | 2400 | 0.3124          | 0.8929   |
| 0.2834        | 4.91  | 2500 | 0.2631          | 0.9165   |
| 0.3484        | 5.11  | 2600 | 0.2987          | 0.9008   |
| 0.2019        | 5.3   | 2700 | 0.2976          | 0.8998   |
| 0.2179        | 5.5   | 2800 | 0.2596          | 0.9082   |
| 0.2068        | 5.7   | 2900 | 0.2852          | 0.9121   |
| 0.2791        | 5.89  | 3000 | 0.2523          | 0.9145   |
| 0.1888        | 6.09  | 3100 | 0.2554          | 0.9145   |
| 0.1909        | 6.29  | 3200 | 0.2623          | 0.9170   |
| 0.1677        | 6.48  | 3300 | 0.2885          | 0.9091   |
| 0.1832        | 6.68  | 3400 | 0.2345          | 0.9190   |
| 0.1088        | 6.88  | 3500 | 0.2448          | 0.9214   |
| 0.2065        | 7.07  | 3600 | 0.2341          | 0.9170   |
| 0.2561        | 7.27  | 3700 | 0.2253          | 0.9209   |
| 0.1902        | 7.47  | 3800 | 0.2196          | 0.9244   |
| 0.236         | 7.66  | 3900 | 0.2217          | 0.9293   |
| 0.2483        | 7.86  | 4000 | 0.2314          | 0.9150   |
| 0.1761        | 8.06  | 4100 | 0.2327          | 0.9268   |
| 0.2349        | 8.25  | 4200 | 0.2408          | 0.9258   |
| 0.0809        | 8.45  | 4300 | 0.1858          | 0.9361   |
| 0.1723        | 8.64  | 4400 | 0.2643          | 0.9204   |
| 0.1762        | 8.84  | 4500 | 0.2194          | 0.9278   |
| 0.1438        | 9.04  | 4600 | 0.1897          | 0.9357   |
| 0.0805        | 9.23  | 4700 | 0.2169          | 0.9322   |
| 0.1513        | 9.43  | 4800 | 0.1635          | 0.9460   |
| 0.1356        | 9.63  | 4900 | 0.1940          | 0.9347   |
| 0.0737        | 9.82  | 5000 | 0.2014          | 0.9342   |
| 0.0908        | 10.02 | 5100 | 0.1707          | 0.9460   |
| 0.1082        | 10.22 | 5200 | 0.1620          | 0.9494   |
| 0.0773        | 10.41 | 5300 | 0.2009          | 0.9332   |
| 0.1614        | 10.61 | 5400 | 0.1735          | 0.9425   |
| 0.0983        | 10.81 | 5500 | 0.1722          | 0.9425   |


### 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", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9425343811394892, "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
================================


This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1722
* Accuracy: 0.9425


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



### 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: 11
* 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: 11
* 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: 11
* 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