Instructions to use hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection") model = AutoModelForObjectDetection.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection")
model = AutoModelForObjectDetection.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection")