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 /Feb12_20-50-19_mcity-rtx-4090 /events.out.tfevents.1739411420.mcity-rtx-4090.11129.12
- Xet hash:
- 4a379f1614929d011863e1ab59646f2689112db53fbd556e88d3c1d75fe688cb
- Size of remote file:
- 24.2 kB
- SHA256:
- 0e6ccf387d27468ed409e3d75c1659581b2a27945c0ed35ae0b1bce1ee2d5f15
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