Instructions to use tfimm/resnet18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use tfimm/resnet18 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("tfimm/resnet18") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tfimm/resnet18: direct link, hf CLI and curl.
- Browser
- Download file 843 Bytes
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https://huggingface.co/tfimm/resnet18/resolve/main/README.md
- Command line
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hf download hf://tfimm/resnet18/README.md
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curl -L -o README.md https://huggingface.co/tfimm/resnet18/resolve/main/README.md
843 Bytes
metadata
tags:
- image-classification
license: apache-2.0
Model card for resnet18
A ResNet-B image classification model.
This model features:
- ReLU activations
- Single layer 7x7 convolution with pooling
- 1x1 convolution shortcut downsample
Trained on ImageNet-1k in timm using recipe template described below.
Recipe details:
- ResNet Strikes Back
A1recipe - LAMB optimizer with BCE loss
- Cosine LR schedule with warmup
Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
- Params (M): 11.7
- GMACs: 1.8
- Activations (M): 2.5
- Image size: train = 224 x 224, test = 288 x 288
- Papers:
- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385