Instructions to use ipd805/digit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ipd805/digit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ipd805/digit")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ipd805/digit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ipd805/digit: direct link, hf CLI and curl.
- Browser
- Download file 657 Bytes
-
https://huggingface.co/ipd805/digit/resolve/main/config.json
- Command line
-
hf download hf://ipd805/digit/config.json
-
curl -L -o config.json https://huggingface.co/ipd805/digit/resolve/main/config.json
657 Bytes
| { | |
| "model_type": "pytorch", | |
| "framework": "onnx", | |
| "task": "object-detection", | |
| "architecture": "YOLOv8", | |
| "format": "onnx", | |
| "num_labels": 2, | |
| "id2label": { "0": "digit", "1": "icon" }, | |
| "label2id": { "digit": 0, "icon": 1 }, | |
| "input_shape": [1, 3, 640, 640], | |
| "inference_framework": "onnxruntime", | |
| "preprocessor": { | |
| "type": "YOLOv8ImageProcessor", | |
| "size": { "height": 640, "width": 640 }, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "resize_method": "letterbox" | |
| }, | |
| "postprocessor": { | |
| "type": "YOLOv8Postprocessor", | |
| "confidence_threshold": 0.25, | |
| "iou_threshold": 0.45, | |
| "max_detections_per_image": 300 | |
| } | |
| } | |