Image Classification
Transformers
TensorBoard
Safetensors
convnext
Generated from Trainer
Eval Results (legacy)
Instructions to use jinneer/convnext-tiny-224-eurosat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinneer/convnext-tiny-224-eurosat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jinneer/convnext-tiny-224-eurosat") 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("jinneer/convnext-tiny-224-eurosat") model = AutoModelForImageClassification.from_pretrained("jinneer/convnext-tiny-224-eurosat") - Notebooks
- Google Colab
- Kaggle
convnext-tiny-224-eurosat
This model is a fine-tuned version of facebook/convnext-tiny-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.6036
- Accuracy: 1.0
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 |
|---|---|---|---|---|
| No log | 0.97 | 7 | 1.6036 | 1.0 |
| 1.8522 | 1.93 | 14 | 1.1066 | 1.0 |
| 1.12 | 2.9 | 21 | 0.9435 | 1.0 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for jinneer/convnext-tiny-224-eurosat
Base model
facebook/convnext-tiny-224Evaluation results
- Accuracy on imagefolderself-reported1.000