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# convnextv2-base-1k-224-finetuned-cassava-leaf-disease-randomflip

This model is a fine-tuned version of [facebook/convnextv2-base-1k-224](https://huggingface.co/facebook/convnextv2-base-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3704
- Accuracy: 0.8766

## 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: 5e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1600
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5605        | 0.98  | 12   | 1.2754          | 0.6150   |
| 1.2015        | 1.96  | 24   | 0.9009          | 0.6290   |
| 0.9048        | 2.94  | 36   | 0.6987          | 0.7701   |
| 0.7362        | 4.0   | 49   | 0.5497          | 0.8206   |
| 0.5294        | 4.98  | 61   | 0.4712          | 0.8542   |
| 0.4777        | 5.96  | 73   | 0.4451          | 0.8547   |
| 0.458         | 6.94  | 85   | 0.4197          | 0.8579   |
| 0.4208        | 8.0   | 98   | 0.4084          | 0.8682   |
| 0.4042        | 8.98  | 110  | 0.3930          | 0.8692   |
| 0.4071        | 9.96  | 122  | 0.3879          | 0.8743   |
| 0.3868        | 10.94 | 134  | 0.3923          | 0.8715   |
| 0.3849        | 12.0  | 147  | 0.3763          | 0.8748   |
| 0.3744        | 12.98 | 159  | 0.3732          | 0.8776   |
| 0.3739        | 13.96 | 171  | 0.3708          | 0.8748   |
| 0.361         | 14.94 | 183  | 0.3693          | 0.8818   |
| 0.3725        | 15.67 | 192  | 0.3704          | 0.8766   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1

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