Instructions to use howdyaendra/xblock-base-patch1-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use howdyaendra/xblock-base-patch1-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="howdyaendra/xblock-base-patch1-224") 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("howdyaendra/xblock-base-patch1-224") model = AutoModelForImageClassification.from_pretrained("howdyaendra/xblock-base-patch1-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from howdyaendra/xblock-base-patch1-224: direct link, hf CLI and curl.
- Browser
- Download file 886 Bytes
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https://huggingface.co/howdyaendra/xblock-base-patch1-224/resolve/main/README.md
- Command line
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hf download hf://howdyaendra/xblock-base-patch1-224/README.md
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curl -L -o README.md https://huggingface.co/howdyaendra/xblock-base-patch1-224/resolve/main/README.md
886 Bytes
| tags: | |
| - autotrain | |
| - image-classification | |
| 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: | |
| - xblock-base-patch1-224/autotrain-data | |
| # Model Trained Using AutoTrain | |
| - Problem type: Image Classification | |
| ## Validation Metrics | |
| loss: 0.4469132423400879 | |
| f1_macro: 0.8236777117298302 | |
| f1_micro: 0.829560585885486 | |
| f1_weighted: 0.8289271724966029 | |
| precision_macro: 0.8243514221166717 | |
| precision_micro: 0.829560585885486 | |
| precision_weighted: 0.8313607282611274 | |
| recall_macro: 0.8260057947019868 | |
| recall_micro: 0.829560585885486 | |
| recall_weighted: 0.829560585885486 | |
| accuracy: 0.829560585885486 | |