Instructions to use howdyaendra/xblock-large-patch1-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use howdyaendra/xblock-large-patch1-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="howdyaendra/xblock-large-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-large-patch1-224") model = AutoModelForImageClassification.from_pretrained("howdyaendra/xblock-large-patch1-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from howdyaendra/xblock-large-patch1-224: direct link, hf CLI and curl.
- Browser
- Download file 891 Bytes
-
https://huggingface.co/howdyaendra/xblock-large-patch1-224/resolve/main/README.md
- Command line
-
hf download hf://howdyaendra/xblock-large-patch1-224/README.md
-
curl -L -o README.md https://huggingface.co/howdyaendra/xblock-large-patch1-224/resolve/main/README.md
891 Bytes
metadata
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-large-patch1-224/autotrain-data
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metrics
loss: 0.9025482535362244
f1_macro: 0.6858654529218409
f1_micro: 0.6716867469879518
f1_weighted: 0.676828467951081
precision_macro: 0.7239086041672248
precision_micro: 0.6716867469879518
precision_weighted: 0.7046011538585282
recall_macro: 0.6707409732185557
recall_micro: 0.6716867469879518
recall_weighted: 0.6716867469879518
accuracy: 0.6716867469879518