Instructions to use DeepLearner101/ResNet50_FGSM_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ResNet50_FGSM_FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ResNet50_FGSM_FT") 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/ResNet50_FGSM_FT") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ResNet50_FGSM_FT") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 8056704
Add pytorch_model_03.pth and related files
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pytorch_model_03.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee4143bc95128de0c4221cd7bb16c8be6219c4ecd5fa0eb7ebab23a865d5cdfa
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size 102544150
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pytorch_model_03.pth_config.json
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{"architectures": ["ResNetForImageClassification"], "model_type": "resnet", "num_labels": 1000}
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pytorch_model_03.pth_training_metrics.json
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""
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