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CGIAR-Crop-disease

Image Classification
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Safetensors
swin
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CGIAR-Crop-disease / README.md
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metadata
license: apache-2.0
base_model: gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: CGIAR-Crop-disease
    results: []

CGIAR-Crop-disease

This model is a fine-tuned version of gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7668
  • Accuracy: 0.6726

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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.047 1.0 652 0.9302 0.5817
0.899 2.0 1304 0.8669 0.6285
0.8656 3.0 1956 0.8434 0.6385
0.8514 4.0 2608 0.8421 0.6277
0.8395 5.0 3260 0.8275 0.6506
0.832 6.0 3912 0.8444 0.6415
0.8065 7.0 4564 0.8204 0.6494
0.8031 8.0 5216 0.8271 0.6438
0.7954 9.0 5868 0.8025 0.6632
0.7939 10.0 6520 0.7917 0.6592
0.7893 11.0 7172 0.8043 0.6515
0.7731 12.0 7824 0.7878 0.6695
0.7759 13.0 8476 0.7806 0.6657
0.7676 14.0 9128 0.7816 0.6653
0.7605 15.0 9780 0.7882 0.6550
0.7566 16.0 10432 0.7881 0.6548
0.7554 17.0 11084 0.7824 0.6619
0.7384 18.0 11736 0.7668 0.6726
0.7442 19.0 12388 0.7830 0.6594
0.7296 20.0 13040 0.7709 0.6667

Framework versions

  • Transformers 4.37.1
  • Pytorch 2.0.0
  • Datasets 2.16.1
  • Tokenizers 0.15.0