Instructions to use SpotLab/YOLOv8Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpotLab/YOLOv8Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="SpotLab/YOLOv8Detection")# pip install -U transformers accelerate # Load model directly from transformers import Yolov8ForObjectDetection model = Yolov8ForObjectDetection.from_pretrained("SpotLab/YOLOv8Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from SpotLab/YOLOv8Detection: direct link, hf CLI and curl.
- Browser
- Download file 406 Bytes
-
https://huggingface.co/SpotLab/YOLOv8Detection/resolve/151de84930ad90bef896ec00b78db563f064bece/preprocessor_config.json
- Command line
-
hf download hf://SpotLab/YOLOv8Detection@151de84930ad90bef896ec00b78db563f064bece/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/SpotLab/YOLOv8Detection/resolve/151de84930ad90bef896ec00b78db563f064bece/preprocessor_config.json
406 Bytes
| { | |
| "crop_size": { | |
| "height": 640, | |
| "width": 640 | |
| }, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_processor_type": "MobileNetV2ImageProcessor", | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 256 | |
| } | |
| } | |