---
license: apache-2.0
base_model: facebook/convnextv2-large-1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnextv2-large-1k-224-finetuned-cassava-leaf-disease
  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: 0.8691588785046729
---

<!-- 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. -->

# convnextv2-large-1k-224-finetuned-cassava-leaf-disease

This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4210
- Accuracy: 0.8692

## 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: 240
- eval_batch_size: 240
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 960
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 8.2962        | 0.49  | 10   | 5.4110          | 0.0033   |
| 3.1666        | 0.99  | 20   | 2.0615          | 0.5883   |
| 1.4693        | 1.48  | 30   | 1.0935          | 0.6084   |
| 0.8718        | 1.98  | 40   | 0.7291          | 0.7463   |
| 0.6252        | 2.47  | 50   | 0.5894          | 0.7916   |
| 0.5198        | 2.96  | 60   | 0.5204          | 0.8299   |
| 0.4517        | 3.46  | 70   | 0.4658          | 0.8393   |
| 0.4266        | 3.95  | 80   | 0.4664          | 0.8407   |
| 0.4049        | 4.44  | 90   | 0.4337          | 0.8579   |
| 0.3817        | 4.94  | 100  | 0.4247          | 0.8523   |
| 0.3696        | 5.43  | 110  | 0.4146          | 0.8621   |
| 0.3577        | 5.93  | 120  | 0.4058          | 0.8607   |
| 0.3577        | 6.42  | 130  | 0.4047          | 0.8636   |
| 0.3354        | 6.91  | 140  | 0.3985          | 0.8617   |
| 0.3356        | 7.41  | 150  | 0.4025          | 0.8645   |
| 0.3286        | 7.9   | 160  | 0.4054          | 0.8673   |
| 0.3225        | 8.4   | 170  | 0.4062          | 0.8631   |
| 0.317         | 8.89  | 180  | 0.4007          | 0.8692   |
| 0.3101        | 9.38  | 190  | 0.3931          | 0.8701   |
| 0.293         | 9.88  | 200  | 0.3928          | 0.8682   |
| 0.2992        | 10.37 | 210  | 0.3942          | 0.8668   |
| 0.2968        | 10.86 | 220  | 0.3892          | 0.8692   |
| 0.2794        | 11.36 | 230  | 0.3988          | 0.8701   |
| 0.2707        | 11.85 | 240  | 0.3865          | 0.8762   |
| 0.2883        | 12.35 | 250  | 0.4040          | 0.8640   |
| 0.2784        | 12.84 | 260  | 0.3930          | 0.8692   |
| 0.2667        | 13.33 | 270  | 0.3985          | 0.8701   |
| 0.2642        | 13.83 | 280  | 0.4160          | 0.8668   |
| 0.2612        | 14.32 | 290  | 0.4086          | 0.8687   |
| 0.2586        | 14.81 | 300  | 0.3990          | 0.8668   |
| 0.2483        | 15.31 | 310  | 0.4111          | 0.8720   |
| 0.254         | 15.8  | 320  | 0.4082          | 0.8748   |
| 0.2283        | 16.3  | 330  | 0.4165          | 0.8668   |
| 0.246         | 16.79 | 340  | 0.4264          | 0.8692   |
| 0.2365        | 17.28 | 350  | 0.4185          | 0.8692   |
| 0.2388        | 17.78 | 360  | 0.4152          | 0.8650   |
| 0.2401        | 18.27 | 370  | 0.4169          | 0.8659   |
| 0.2334        | 18.77 | 380  | 0.4187          | 0.8696   |
| 0.2245        | 19.26 | 390  | 0.4192          | 0.8692   |
| 0.2291        | 19.75 | 400  | 0.4210          | 0.8692   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1