Instructions to use raks87/resnet-18-finetuned-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raks87/resnet-18-finetuned-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="raks87/resnet-18-finetuned-cifar10") 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("raks87/resnet-18-finetuned-cifar10") model = AutoModelForImageClassification.from_pretrained("raks87/resnet-18-finetuned-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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| { | |
| "epoch": 2.99, | |
| "eval_accuracy": 0.10166666666666667, | |
| "eval_loss": NaN, | |
| "eval_runtime": 135.6867, | |
| "eval_samples_per_second": 110.549, | |
| "eval_steps_per_second": 3.456, | |
| "total_flos": 1.0585264325663785e+18, | |
| "train_loss": 0.0, | |
| "train_runtime": 811.0273, | |
| "train_samples_per_second": 129.465, | |
| "train_steps_per_second": 1.01 | |
| } |