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 /Feb14_17-20-07_mcity-rtx-4090 /events.out.tfevents.1739571607.mcity-rtx-4090.1139582.0
- Xet hash:
- 91905c0105bad2b7226c80ea0ef442e6c331423c987e92a4d399e35e9dd032af
- Size of remote file:
- 8.54 kB
- SHA256:
- f27b5b69ad51b3610c3b7829c83462e14fd2e4e824f658b5a7bf44c804d7d15f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.