Instructions to use MinhLe999/3class_EfficientNetv2_ForTesting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinhLe999/3class_EfficientNetv2_ForTesting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MinhLe999/3class_EfficientNetv2_ForTesting", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("MinhLe999/3class_EfficientNetv2_ForTesting", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: 3class_EfficientNetv2_ForTesting | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # 3class_EfficientNetv2_ForTesting | |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - eval_loss: 0.1331 | |
| - eval_model_preparation_time: 0.0183 | |
| - eval_precision: 0.9637 | |
| - eval_recall: 0.9679 | |
| - eval_accuracy: 0.9712 | |
| - eval_f1: 0.9656 | |
| - eval_roc_auc: 0.9966 | |
| - eval_runtime: 62.5585 | |
| - eval_samples_per_second: 18.303 | |
| - eval_steps_per_second: 0.575 | |
| - epoch: 0.3106 | |
| - step: 200 | |
| ## 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: 32 | |
| - 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 | |
| - num_epochs: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 5.3.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |