Image Classification
ultralytics
PyTorch
v8
ultralyticsplus
yolov8
yolo
vision
Eval Results (legacy)
Instructions to use feliperafael/my_yolo_model_pantene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use feliperafael/my_yolo_model_pantene with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("feliperafael/my_yolo_model_pantene", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Supported Labels
['pantene']
How to use
- Install ultralyticsplus:
pip install ultralyticsplus==0.0.29 ultralytics==8.0.239
- Load model and perform prediction:
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('feliperafael/my_yolo_model_pantene')
# set model parameters
model.overrides['conf'] = 0.25 # model confidence threshold
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
processed_result = postprocess_classify_output(model, result=results[0])
print(processed_result) # {"cat": 0.4, "dog": 0.6}
- Downloads last month
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Evaluation results
- top1 accuracyself-reported1.000
- top5 accuracyself-reported1.000