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# plant-seedlings-model-ConvNet

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2410
- Accuracy: 0.9522

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.494         | 0.8   | 100  | 0.4274          | 0.8828   |
| 0.246         | 1.6   | 200  | 0.2878          | 0.8930   |
| 0.1042        | 2.4   | 300  | 0.2227          | 0.9172   |
| 0.0174        | 3.2   | 400  | 0.2208          | 0.9299   |
| 0.0088        | 4.0   | 500  | 0.3197          | 0.9185   |
| 0.0078        | 4.8   | 600  | 0.2555          | 0.9357   |
| 0.0013        | 5.6   | 700  | 0.2599          | 0.9427   |
| 0.0068        | 6.4   | 800  | 0.3072          | 0.9312   |
| 0.0007        | 7.2   | 900  | 0.2217          | 0.9484   |
| 0.0004        | 8.0   | 1000 | 0.2551          | 0.9401   |
| 0.0003        | 8.8   | 1100 | 0.2321          | 0.9478   |
| 0.0002        | 9.6   | 1200 | 0.2329          | 0.9484   |
| 0.0002        | 10.4  | 1300 | 0.2322          | 0.9478   |
| 0.0002        | 11.2  | 1400 | 0.2342          | 0.9478   |
| 0.0002        | 12.0  | 1500 | 0.2348          | 0.9490   |
| 0.0001        | 12.8  | 1600 | 0.2358          | 0.9490   |
| 0.0001        | 13.6  | 1700 | 0.2368          | 0.9497   |
| 0.0001        | 14.4  | 1800 | 0.2377          | 0.9510   |
| 0.0001        | 15.2  | 1900 | 0.2384          | 0.9516   |
| 0.0001        | 16.0  | 2000 | 0.2391          | 0.9516   |
| 0.0001        | 16.8  | 2100 | 0.2397          | 0.9522   |
| 0.0001        | 17.6  | 2200 | 0.2401          | 0.9522   |
| 0.0001        | 18.4  | 2300 | 0.2406          | 0.9522   |
| 0.0001        | 19.2  | 2400 | 0.2409          | 0.9522   |
| 0.0001        | 20.0  | 2500 | 0.2410          | 0.9522   |


### 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-ConvNet", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9522292993630573, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard convnext image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #convnext #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

plant-seedlings-model-ConvNet
=============================


This model is a fine-tuned version of facebook/convnext-tiny-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2410
* Accuracy: 0.9522


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 #convnext #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 #convnext #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