Instructions to use kausthubkannan17/dropex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kausthubkannan17/dropex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="kausthubkannan17/dropex")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("kausthubkannan17/dropex") model = AutoModelForObjectDetection.from_pretrained("kausthubkannan17/dropex") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95690bca9d9cbb19406480f386ec29975a2f6e895f28aa779d8e768610004d3f
- Size of remote file:
- 166 MB
- SHA256:
- 830bd6b8e0fe95fc43459b2a6727eec895950939e2ec4c772cdd06ec45d040e7
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