Instructions to use DeepLearner101/CIFARSelectedSubsetBasedModel-Training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/CIFARSelectedSubsetBasedModel-Training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/CIFARSelectedSubsetBasedModel-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/CIFARSelectedSubsetBasedModel-Training") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/CIFARSelectedSubsetBasedModel-Training") - Notebooks
- Google Colab
- Kaggle
Commit ·
3842506
1
Parent(s): 01b314d
Upload hyperparameters_tuning_results.csv with huggingface_hub
Browse files
hyperparameters_tuning_results.csv
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date,timestamp,pid,hostname,node_ip,config/lr,config/weight_decay,config/dropout_rate,config/l1_factor,config/epochs,config/epsilon_range,config/step_size,config/gamma,config/early_stopping_tolerance,config/training_batch_size,config/validation_batch_size
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date,timestamp,pid,hostname,node_ip,config/lr,config/weight_decay,config/dropout_rate,config/l1_factor,config/epochs,config/epsilon_range,config/step_size,config/gamma,config/early_stopping_tolerance,config/training_batch_size,config/validation_batch_size
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2023-12-29_17-35-37,1703871337.0,1969.0,fa6814cfd524,172.28.0.12,9.144795387004634e-05,0.0037716196445668065,0.4398696715775855,4.7646498400358045e-06,15.0,"(0.001, 0.007, 0.002)",5.0,0.3,5.0,32.0,32.0
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