Instructions to use fcakyon/yolov5n-cls-v7.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcakyon/yolov5n-cls-v7.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fcakyon/yolov5n-cls-v7.0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fcakyon/yolov5n-cls-v7.0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
How to use
- Install yolov5:
pip install -U yolov5
- Load model and perform prediction:
import yolov5
# load model
model = yolov5.load('fcakyon/yolov5n-cls-v7.0')
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img)
- Finetune the model on your custom dataset:
yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu
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