Instructions to use Tushar86/yolo-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tushar86/yolo-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Tushar86/yolo-testing") 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("Tushar86/yolo-testing") model = AutoModelForImageClassification.from_pretrained("Tushar86/yolo-testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Tushar86/yolo-testing: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/Tushar86/yolo-testing/resolve/main/README.md
- Command line
-
hf download hf://Tushar86/yolo-testing/README.md
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curl -L -o README.md https://huggingface.co/Tushar86/yolo-testing/resolve/main/README.md
1.05 kB
metadata
tags:
- autotrain
- vision
- image-classification
datasets:
- juliensimon/autotrain-data-food101
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
co2_eq_emissions:
emissions: 179.11544810549532
Usage
from transformers import pipeline
p = pipeline("image-classification", model="juliensimon/autotrain-food101-1471154053")
result = p("my_image.jpg")
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154
Validation Metrics
- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915