Instructions to use ConradHw/pole_detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConradHw/pole_detect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ConradHw/pole_detect")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ConradHw/pole_detect") model = AutoModelForObjectDetection.from_pretrained("ConradHw/pole_detect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ConradHw/pole_detect: direct link, hf CLI and curl.
- Browser
- Download file 293 Bytes
-
https://huggingface.co/ConradHw/pole_detect/resolve/main/config.json
- Command line
-
hf download hf://ConradHw/pole_detect/config.json
-
curl -L -o config.json https://huggingface.co/ConradHw/pole_detect/resolve/main/config.json
293 Bytes
| { | |
| "_name_or_path": "ConradHw/pole_detect", | |
| "architectures": [ | |
| "YolosForObjectDetection" | |
| ], | |
| "id2label": { | |
| "0": "pole" | |
| }, | |
| "image_size": [ | |
| 512, | |
| 864 | |
| ], | |
| "label2id": { | |
| "pole": 0 | |
| }, | |
| "model_type": "yolos", | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 12 | |
| } |