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="zireael08/logs")
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("zireael08/logs")
model = AutoModelForImageClassification.from_pretrained("zireael08/logs")
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logs

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0098
  • Accuracy: 0.9982

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6367 1.0 83 0.4612 0.8248
0.4656 2.0 166 0.3608 0.8496
0.4911 3.0 249 0.1344 0.9646
0.1630 4.0 332 0.1347 0.9575
0.1872 5.0 415 0.1106 0.9628
0.1801 6.0 498 0.0968 0.9823
0.1453 7.0 581 0.1196 0.9717
0.0787 8.0 664 0.0838 0.9894
0.0353 9.0 747 0.0801 0.9912
0.0878 10.0 830 0.0818 0.9912

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2
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