Instructions to use DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning") 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-TransferLearning") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
2fa962d
1
Parent(s): 9637a0e
Update best_hyperparameters.json
Browse files
best_hyperparameters.json
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"lr": 0.000435096, "weight_decay": 0.00720087, "dropout_rate": 0.554455, "l1_factor": 1.71474e-06, "epochs": 15, "step_size": 5, "gamma": 0.7, "early_stopping_tolerance": 5, "training_batch_size": 64, "validation_batch_size": 50, "epsilon_range":
|
|
|
|
| 1 |
+
{"lr": 0.000435096, "weight_decay": 0.00720087, "dropout_rate": 0.554455, "l1_factor": 1.71474e-06, "epochs": 15, "step_size": 5, "gamma": 0.7, "early_stopping_tolerance": 5, "training_batch_size": 64, "validation_batch_size": 50, "epsilon_range": [0.001, 0.007, 0.002]}
|