How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_fft")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification

processor = AutoImageProcessor.from_pretrained("SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_fft")
model = AutoModelForImageClassification.from_pretrained("SodaXII/convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_fft", device_map="auto")
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convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_fft

This model is a fine-tuned version of facebook/convnextv2-base-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4315
  • Accuracy: 0.9295

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 512
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.9643 0.5 128 1.7264 0.6040
1.2673 1.0 256 0.7987 0.8490
0.5214 1.5 384 0.3728 0.9094
0.2476 2.0 512 0.4170 0.8792
0.104 2.5 640 0.2676 0.9262
0.0573 3.0 768 0.2642 0.9262
0.0262 3.5 896 0.3739 0.9228
0.0171 4.0 1024 0.3519 0.9362
0.0032 4.5 1152 0.3476 0.9295
0.0011 5.0 1280 0.3547 0.9362
0.0008 5.5 1408 0.3541 0.9329
0.0008 6.0 1536 0.3556 0.9329
0.0008 6.5 1664 0.3524 0.9329
0.0005 7.0 1792 0.3775 0.9329
0.0004 7.5 1920 0.3850 0.9329
0.0003 8.0 2048 0.3900 0.9329
0.0003 8.5 2176 0.3965 0.9295
0.0002 9.0 2304 0.4000 0.9295
0.0002 9.5 2432 0.4019 0.9295
0.0002 10.0 2560 0.4018 0.9295
0.0002 10.5 2688 0.4021 0.9295
0.0002 11.0 2816 0.4063 0.9329
0.0002 11.5 2944 0.4145 0.9295
0.0001 12.0 3072 0.4184 0.9295
0.0001 12.5 3200 0.4249 0.9295
0.0001 13.0 3328 0.4271 0.9295
0.0001 13.5 3456 0.4304 0.9295
0.0001 14.0 3584 0.4315 0.9295
0.0001 14.5 3712 0.4316 0.9295
0.0001 15.0 3840 0.4315 0.9295

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0
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Model size
87.7M params
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F32
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