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# plant-seedlings-model

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: 0.2095
- Accuracy: 0.9701

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5298        | 0.8   | 100  | 0.3284          | 0.8816   |
| 0.227         | 1.6   | 200  | 0.2696          | 0.9164   |
| 0.0994        | 2.4   | 300  | 0.4517          | 0.8886   |
| 0.1671        | 3.2   | 400  | 0.3317          | 0.9164   |
| 0.1438        | 4.0   | 500  | 0.2582          | 0.9363   |
| 0.0072        | 4.8   | 600  | 0.2833          | 0.9413   |
| 0.0873        | 5.6   | 700  | 0.2988          | 0.9154   |
| 0.0193        | 6.4   | 800  | 0.2307          | 0.9483   |
| 0.0141        | 7.2   | 900  | 0.3538          | 0.9333   |
| 0.0057        | 8.0   | 1000 | 0.2162          | 0.9552   |
| 0.0046        | 8.8   | 1100 | 0.2587          | 0.9502   |
| 0.0235        | 9.6   | 1200 | 0.3755          | 0.9443   |
| 0.0399        | 10.4  | 1300 | 0.2639          | 0.9493   |
| 0.0031        | 11.2  | 1400 | 0.2164          | 0.9632   |
| 0.0001        | 12.0  | 1500 | 0.2075          | 0.9701   |
| 0.0001        | 12.8  | 1600 | 0.2074          | 0.9701   |
| 0.0001        | 13.6  | 1700 | 0.2078          | 0.9701   |
| 0.0001        | 14.4  | 1800 | 0.2081          | 0.9701   |
| 0.0001        | 15.2  | 1900 | 0.2084          | 0.9701   |
| 0.0001        | 16.0  | 2000 | 0.2088          | 0.9701   |
| 0.0001        | 16.8  | 2100 | 0.2089          | 0.9701   |
| 0.0001        | 17.6  | 2200 | 0.2092          | 0.9701   |
| 0.0001        | 18.4  | 2300 | 0.2095          | 0.9701   |
| 0.0001        | 19.2  | 2400 | 0.2095          | 0.9701   |
| 0.0001        | 20.0  | 2500 | 0.2095          | 0.9701   |


### 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-model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9701492537313433, "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-model
=====================


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: 0.2095
* Accuracy: 0.9701


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