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="ahmedALM1221/convnextv2-large-1k-224-finetuned-eurosat-50")
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("ahmedALM1221/convnextv2-large-1k-224-finetuned-eurosat-50")
model = AutoModelForImageClassification.from_pretrained("ahmedALM1221/convnextv2-large-1k-224-finetuned-eurosat-50", device_map="auto")
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convnextv2-large-1k-224-finetuned-eurosat-50

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: 0.1615
  • Accuracy: 0.9599

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.9
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.9003 1.0 122 1.8892 0.3587
1.6055 2.0 244 1.5936 0.5766
1.2982 3.0 366 1.2568 0.6639
1.0273 4.0 488 0.9558 0.7431
0.7863 5.0 610 0.7440 0.7965
0.8142 6.0 732 0.6206 0.8304
0.6118 7.0 854 0.4672 0.8684
0.4776 8.0 976 0.3756 0.9075
0.4176 9.0 1098 0.3065 0.9301
0.282 10.0 1220 0.2586 0.9404
0.306 11.0 1342 0.2098 0.9476
0.2006 12.0 1464 0.1615 0.9599

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Evaluation results