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")# 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
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
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