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_11-54-31_mcity-rtx-4090 /events.out.tfevents.1739379829.mcity-rtx-4090.1816145.1
- Xet hash:
- e768ef25e3dc77c970ea36f82170cfb0da2f4673dbcdd90c97bb6766ffd1ea41
- Size of remote file:
- 359 Bytes
- SHA256:
- 7e2dbe245b5b3582c9877d04b81f5c2e3ea2aba4edf42ed81cfcc702c4e4006e
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