Zero-Shot Object Detection
Transformers
PyTorch
TensorBoard
deformable_detr
object-detection
Generated from Trainer
zero-shot
Instructions to use mcity-data-engine/fisheye8k_facebook_deformable-detr-detic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_facebook_deformable-detr-detic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="mcity-data-engine/fisheye8k_facebook_deformable-detr-detic")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_facebook_deformable-detr-detic") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_facebook_deformable-detr-detic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10502e6faefd2611439a273472a2ab55e71efa29c2ea0a4ecc04dc0025f6f300
- Size of remote file:
- 5.56 kB
- SHA256:
- 64404eed00262911baac470ecee6e814ae0a2a4d89858fbc9822df1abab4c544
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.