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 /Feb07_15-33-35_mcity-rtx-4090 /events.out.tfevents.1738960416.mcity-rtx-4090.153757.0
- Xet hash:
- 3c6b3a3e6646f67346b9c9e5e3546bfdfd65aa0766889ecdde91dbb2255222bb
- Size of remote file:
- 53.1 kB
- SHA256:
- d767b41d7df4d976ff43951f2af4a1b023f2086ca191c3e647510a9a88850136
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