Instructions to use merve/rfdetr-roadsign-agree2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use merve/rfdetr-roadsign-agree2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="merve/rfdetr-roadsign-agree2")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("merve/rfdetr-roadsign-agree2") model = AutoModelForObjectDetection.from_pretrained("merve/rfdetr-roadsign-agree2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 442 Bytes
64c3128 06f2306 64c3128 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"do_convert_annotations": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"format": "coco_detection",
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "RfDetrImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 560,
"width": 560
},
"use_fast": true
}
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