Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
convnext
Generated from Trainer
Eval Results (legacy)
Instructions to use nielsr/convnext-tiny-224-finetuned-eurosat-albumentations with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/convnext-tiny-224-finetuned-eurosat-albumentations with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nielsr/convnext-tiny-224-finetuned-eurosat-albumentations") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nielsr/convnext-tiny-224-finetuned-eurosat-albumentations") model = AutoModelForImageClassification.from_pretrained("nielsr/convnext-tiny-224-finetuned-eurosat-albumentations", device_map="auto") - Notebooks
- Google Colab
- Kaggle
convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of facebook/convnext-tiny-224 on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0727
- Accuracy: 0.9748
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.141 | 1.0 | 190 | 0.1496 | 0.9544 |
| 0.0736 | 2.0 | 380 | 0.0958 | 0.9719 |
| 0.0568 | 3.0 | 570 | 0.0727 | 0.9748 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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Model tree for nielsr/convnext-tiny-224-finetuned-eurosat-albumentations
Base model
facebook/convnext-tiny-224Evaluation results
- Accuracy on image_folderself-reported0.975