Instructions to use flyswot/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flyswot/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="flyswot/test2") 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("flyswot/test2") model = AutoModelForImageClassification.from_pretrained("flyswot/test2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| base_model: flyswot/convnext-tiny-224_flyswot | |
| model-index: | |
| - name: test2 | |
| 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. --> | |
| # test2 | |
| This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224_flyswot) on the None dataset. | |
| ## 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: 2e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 0.1 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | No log | 0.1 | 23 | 0.1128 | 0.9787 | | |
| ### Framework versions | |
| - Transformers 4.19.4 | |
| - Pytorch 1.11.0+cu113 | |
| - Datasets 2.3.0 | |
| - Tokenizers 0.12.1 | |