Instructions to use DeathDaDev/Materializer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeathDaDev/Materializer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeathDaDev/Materializer") 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("DeathDaDev/Materializer") model = AutoModelForImageClassification.from_pretrained("DeathDaDev/Materializer") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - image-classification | |
| base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft | |
| widget: | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg | |
| example_title: Tiger | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg | |
| example_title: Teapot | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg | |
| example_title: Palace | |
| datasets: | |
| - DeathDaDev/textures3 | |
| license: cc-by-sa-4.0 | |
| language: | |
| - en | |
| pipeline_tag: image-classification | |
| library_name: transformers | |
| # Model Trained Using AutoTrain | |
| - Problem type: Image Classification | |
| ## Validation Metrics | |
| loss: 0.595848798751831 | |
| f1_macro: 0.6619490584264867 | |
| f1_micro: 0.7440758293838863 | |
| f1_weighted: 0.7388732927039862 | |
| precision_macro: 0.6678889139696575 | |
| precision_micro: 0.7440758293838863 | |
| precision_weighted: 0.7622779846533339 | |
| recall_macro: 0.6766371375677931 | |
| recall_micro: 0.7440758293838863 | |
| recall_weighted: 0.7440758293838863 | |
| accuracy: 0.7440758293838863 |