Instructions to use DeepLearner101/ResNetMixedDataSetFromScratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ResNetMixedDataSetFromScratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ResNetMixedDataSetFromScratch") 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("DeepLearner101/ResNetMixedDataSetFromScratch") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ResNetMixedDataSetFromScratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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best_hyperparameters.json
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{"lr": 0.0006052479464566044, "momentum": 0.887635182126238, "weight_decay": 0.0008281115733515546, "dropout_rate": 0.42607811019124237, "l1_factor": 0.0008970211875956824, "epochs": 12, "epsilon_range": [0.003, 0.125, 0.005], "step_size": 7, "gamma": 0.3, "early_stopping_tolerance": 6, "batch_size": 64}
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