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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Parent(s): e39ba67
Add pytorch_model_0124.pth and related files
Browse files- pytorch_model_0124.pth +3 -0
- training_metrics_0124.json +0 -0
pytorch_model_0124.pth
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oid sha256:582e45989b7724052451188194e061bdd7ecf87088d30f0d6dfc28d2fbdc3b9c
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training_metrics_0124.json
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