Instructions to use fernandabufon/ft_stable_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernandabufon/ft_stable_diffusion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fernandabufon/ft_stable_diffusion") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("fernandabufon/ft_stable_diffusion") model = AutoModelForImageClassification.from_pretrained("fernandabufon/ft_stable_diffusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from fernandabufon/ft_stable_diffusion: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/fernandabufon/ft_stable_diffusion/resolve/main/README.md
- Command line
-
hf download hf://fernandabufon/ft_stable_diffusion/README.md
-
curl -L -o README.md https://huggingface.co/fernandabufon/ft_stable_diffusion/resolve/main/README.md
1.78 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224 | |
| tags: | |
| - image-classification | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: ft_stable_diffusion | |
| 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. --> | |
| # ft_stable_diffusion | |
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the generated by stable diffusion dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3650 | |
| - Accuracy: 0.9194 | |
| ## 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: 0.0003 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 70 | 0.9239 | 0.7705 | | |
| | 1.1759 | 2.0 | 140 | 0.5778 | 0.8852 | | |
| | 0.5081 | 3.0 | 210 | 0.4438 | 0.9180 | | |
| | 0.5081 | 4.0 | 280 | 0.3857 | 0.9344 | | |
| | 0.3442 | 5.0 | 350 | 0.3700 | 0.9344 | | |
| ### Framework versions | |
| - Transformers 4.46.3 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.20.3 | |