Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use embunna/resnet-18-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use embunna/resnet-18-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="embunna/resnet-18-finetuned") 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("embunna/resnet-18-finetuned") model = AutoModelForImageClassification.from_pretrained("embunna/resnet-18-finetuned") - Notebooks
- Google Colab
- Kaggle
File size: 621 Bytes
a0b8be6 | 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 28 29 30 31 32 33 34 35 36 37 38 | {
"_valid_processor_keys": [
"images",
"do_resize",
"size",
"crop_pct",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format"
],
"crop_pct": 0.875,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "ConvNextImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
}
|