Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results (legacy)
Instructions to use zireael08/logs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zireael08/logs with Transformers:
# 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") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("zireael08/logs")
model = AutoModelForImageClassification.from_pretrained("zireael08/logs")Quick Links
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
- Downloads last month
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Model tree for zireael08/logs
Base model
microsoft/swinv2-tiny-patch4-window8-256Evaluation results
- Accuracy on imagefoldervalidation set self-reported0.998
# 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")