Instructions to use Seba42/cifar100-butterfly-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seba42/cifar100-butterfly-demo with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import ButterflyMlpNet model = ButterflyMlpNet.from_pretrained("Seba42/cifar100-butterfly-demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
cifar100-butterfly-demo
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1115
- Accuracy: 0.2904
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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.4272 | 1.0 | 782 | 3.4173 | 0.1836 |
| 3.13 | 2.0 | 1564 | 3.1456 | 0.2398 |
| 2.9701 | 3.0 | 2346 | 3.0407 | 0.264 |
| 2.7407 | 4.0 | 3128 | 2.9624 | 0.2742 |
| 2.5488 | 5.0 | 3910 | 2.9198 | 0.2884 |
| 2.4276 | 6.0 | 4692 | 2.9361 | 0.2964 |
| 2.2345 | 7.0 | 5474 | 2.9901 | 0.2967 |
| 2.0829 | 8.0 | 6256 | 3.0555 | 0.2905 |
| 1.9186 | 9.0 | 7038 | 3.1012 | 0.2903 |
| 1.8801 | 10.0 | 7820 | 3.1115 | 0.2904 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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