Instructions to use waelhasan/resnet-18-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waelhasan/resnet-18-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="waelhasan/resnet-18-v3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("waelhasan/resnet-18-v3") model = AutoModelForImageClassification.from_pretrained("waelhasan/resnet-18-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 391 Bytes
cc5f5ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"crop_pct": 0.875,
"data_format": "channels_first",
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "ConvNextImageProcessorFast",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
}
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