Instructions to use DeepLearner101/ImageNetSubsetForFineTuningBasedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSubsetForFineTuningBasedModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSubsetForFineTuningBasedModel") 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/ImageNetSubsetForFineTuningBasedModel") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSubsetForFineTuningBasedModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d551bbe
Upload best_hyperparameters.json with huggingface_hub
Browse files
best_hyperparameters.json
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{"lr": 1.6417187141738818e-05, "weight_decay": 0.0005764799174384383, "dropout_rate": 0.17383792252322877, "l1_factor": 0.0019344316842131125, "epochs": 10, "epsilon_range": [0.001, 0.005, 0.002], "step_size": 7, "gamma": 0.1, "early_stopping_tolerance": 7, "batch_size": 32, "adversarial_training_frequency": 1}
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