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metadata
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

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

This model is a fine-tuned version of 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