Instructions to use DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning") 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/ImageNetSelectedSubsetBasedModel-FineTuning") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Upload best_hyperparameters.json with huggingface_hub
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best_hyperparameters.json
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{"lr": 0.00019625027721068008, "weight_decay": 1.3248928846670252e-05, "dropout_rate": 0.5954955366339605, "l1_factor": 1.154414886066787e-06, "epochs": 15, "epsilon_range": [0.001, 0.007, 0.002], "step_size": 5, "gamma": 0.3, "early_stopping_tolerance": 10, "training_batch_size": 32, "validation_batch_size": 50}
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