Instructions to use DeepLearner101/ImageNetSelectedSubsetBasedModel-Training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSelectedSubsetBasedModel-Training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSelectedSubsetBasedModel-Training") 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-Training") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSelectedSubsetBasedModel-Training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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best_hyperparameters.json
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{"lr": 0.0003409306266224327, "weight_decay": 7.14984769591941e-05, "dropout_rate": 0.6694155998994442, "l1_factor": 1.7217660746598644e-06, "epochs": 12, "epsilon_range": [0.001, 0.007, 0.002], "step_size": 10, "gamma": 0.1, "early_stopping_tolerance": 5, "training_batch_size":
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{"lr": 0.0003409306266224327, "weight_decay": 7.14984769591941e-05, "dropout_rate": 0.6694155998994442, "l1_factor": 1.7217660746598644e-06, "epochs": 12, "epsilon_range": [0.001, 0.007, 0.002], "step_size": 10, "gamma": 0.1, "early_stopping_tolerance": 5, "training_batch_size": 64, "validation_batch_size": 50}
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