Instructions to use mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50") - Notebooks
- Google Colab
- Kaggle
fisheye8k_microsoft_conditional-detr-resnet-50 / runs /Feb06_18-34-29_mcity-rtx-4090 /events.out.tfevents.1738889068.mcity-rtx-4090.112767.1
- Xet hash:
- 9a8890fd5e4740098a7cb90c4ce9a8a377408ad8c60b5a5a4f7c02f182756bb4
- Size of remote file:
- 364 Bytes
- SHA256:
- 04a61d578699fcf3fdefbb1bb1a26a39949789ccea1a8e317f858070263ae1eb
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