Instructions to use vickygupta1996/VGG16_Sign_Verification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use vickygupta1996/VGG16_Sign_Verification with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("vickygupta1996/VGG16_Sign_Verification") - Notebooks
- Google Colab
- Kaggle
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Download SignatureDetection/ultralytics/docs/reference/utils/torch_utils.md from vickygupta1996/VGG16_Sign_Verification: direct link, hf CLI and curl.
- Browser
- Download file 2.6 kB
-
https://huggingface.co/vickygupta1996/VGG16_Sign_Verification/resolve/main/SignatureDetection/ultralytics/docs/reference/utils/torch_utils.md
- Command line
-
hf download hf://vickygupta1996/VGG16_Sign_Verification/SignatureDetection/ultralytics/docs/reference/utils/torch_utils.md
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curl -L -o torch_utils.md https://huggingface.co/vickygupta1996/VGG16_Sign_Verification/resolve/main/SignatureDetection/ultralytics/docs/reference/utils/torch_utils.md
2.6 kB
metadata
description: >-
Explore Ultralytics-tailored torch utility features like Model EMA, early
stopping, smart inference, image scaling, get_flops, and many more.
keywords: >-
Ultralytics, Torch Utils, Model EMA, Early Stopping, Smart Inference, Get CPU
Info, Time Sync, Fuse Deconv and bn, Get num params, Get FLOPs, Scale img,
Copy attr, Intersect dicts, De_parallel, Init seeds, Profile
Reference for ultralytics/utils/torch_utils.py
!!! note
Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/torch_utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/torch_utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!