---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: plant-seedlings-model-ConvNet-all-train
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9171143514965464
---

<!-- 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. -->

# plant-seedlings-model-ConvNet-all-train

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: 0.2966
- Accuracy: 0.9171

## 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: 16
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2313        | 0.31  | 100  | 1.0832          | 0.6731   |
| 0.7221        | 0.61  | 200  | 0.6529          | 0.7913   |
| 0.5858        | 0.92  | 300  | 0.5267          | 0.8204   |
| 0.4257        | 1.23  | 400  | 0.5765          | 0.8051   |
| 0.6183        | 1.53  | 500  | 0.6322          | 0.7928   |
| 0.4392        | 1.84  | 600  | 0.4168          | 0.8649   |
| 0.3589        | 2.15  | 700  | 0.5549          | 0.8066   |
| 0.4259        | 2.45  | 800  | 0.4678          | 0.8396   |
| 0.3705        | 2.76  | 900  | 0.4542          | 0.8396   |
| 0.4609        | 3.07  | 1000 | 0.4723          | 0.8411   |
| 0.2082        | 3.37  | 1100 | 0.3631          | 0.8803   |
| 0.4583        | 3.68  | 1200 | 0.3835          | 0.8688   |
| 0.2218        | 3.99  | 1300 | 0.3913          | 0.8772   |
| 0.3716        | 4.29  | 1400 | 0.3858          | 0.8818   |
| 0.3675        | 4.6   | 1500 | 0.3849          | 0.8734   |
| 0.2602        | 4.91  | 1600 | 0.4080          | 0.8734   |
| 0.2091        | 5.21  | 1700 | 0.3767          | 0.8818   |
| 0.2071        | 5.52  | 1800 | 0.3883          | 0.8795   |
| 0.2426        | 5.83  | 1900 | 0.3557          | 0.8856   |
| 0.2917        | 6.13  | 2000 | 0.3550          | 0.8872   |
| 0.1417        | 6.44  | 2100 | 0.2918          | 0.9110   |
| 0.237         | 6.75  | 2200 | 0.3785          | 0.8864   |
| 0.1372        | 7.06  | 2300 | 0.3106          | 0.9025   |
| 0.161         | 7.36  | 2400 | 0.3809          | 0.8841   |
| 0.2354        | 7.67  | 2500 | 0.3739          | 0.8949   |
| 0.2489        | 7.98  | 2600 | 0.3442          | 0.8941   |
| 0.1962        | 8.28  | 2700 | 0.2875          | 0.9125   |
| 0.3157        | 8.59  | 2800 | 0.2959          | 0.9163   |
| 0.1204        | 8.9   | 2900 | 0.3017          | 0.9087   |
| 0.1272        | 9.2   | 3000 | 0.3380          | 0.9071   |
| 0.1768        | 9.51  | 3100 | 0.3611          | 0.9033   |
| 0.2211        | 9.82  | 3200 | 0.2704          | 0.9210   |
| 0.1213        | 10.12 | 3300 | 0.2813          | 0.9240   |
| 0.0432        | 10.43 | 3400 | 0.2956          | 0.9179   |
| 0.1152        | 10.74 | 3500 | 0.3256          | 0.9094   |
| 0.178         | 11.04 | 3600 | 0.3470          | 0.9094   |
| 0.1427        | 11.35 | 3700 | 0.3221          | 0.9079   |
| 0.1046        | 11.66 | 3800 | 0.2559          | 0.9286   |
| 0.1029        | 11.96 | 3900 | 0.2848          | 0.9202   |
| 0.0459        | 12.27 | 4000 | 0.3051          | 0.9156   |
| 0.1063        | 12.58 | 4100 | 0.2825          | 0.9225   |
| 0.0974        | 12.88 | 4200 | 0.3168          | 0.9233   |
| 0.0923        | 13.19 | 4300 | 0.3134          | 0.9194   |
| 0.0736        | 13.5  | 4400 | 0.2480          | 0.9325   |
| 0.0783        | 13.8  | 4500 | 0.2872          | 0.9202   |
| 0.1444        | 14.11 | 4600 | 0.3011          | 0.9225   |
| 0.1507        | 14.42 | 4700 | 0.2794          | 0.9271   |
| 0.1318        | 14.72 | 4800 | 0.2625          | 0.9271   |
| 0.0931        | 15.03 | 4900 | 0.2914          | 0.9279   |
| 0.074         | 15.34 | 5000 | 0.2826          | 0.9248   |
| 0.1306        | 15.64 | 5100 | 0.2836          | 0.9240   |
| 0.0856        | 15.95 | 5200 | 0.2966          | 0.9171   |


### Framework versions

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