flyyufelix/cnn_finetune 排名第 12,499，有 941 个星标。
同类别比较
flyyufelix/cnn_finetune 排名第 12,499，此处有 8 个同类条目；ml4a/ml4a.github.io 排名第 12,509。
同类别比较| 名称 | 排名 | 指标 |
|---|
| lansinuote/Huggingface_Toturials | 12,453 | - 语言:
- Jupyter Notebook
- 星标数:
- 948
- 分叉数:
- 247
- 简介:
- bert-base-chinese example
|
|---|
| tdpetrou/Machine-Learning-Books-With-Python | 12,475 | - 语言:
- Jupyter Notebook
- 星标数:
- 945
- 分叉数:
- 569
- 简介:
- Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python
|
|---|
| jocicmarko/ultrasound-nerve-segmentation | 12,480 | - 语言:
- Python
- 星标数:
- 944
- 分叉数:
- 325
- 简介:
- Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras
|
|---|
| youssefHosni/Practical-Machine-Learning | 12,482 | - 语言:
- Jupyter Notebook
- 星标数:
- 944
- 分叉数:
- 202
- 简介:
- Practical machine learning notebook & articles covers the machine learning end to end life cycle.
|
|---|
| ml4a/ml4a.github.io | 12,509 | - 语言:
- HTML
- 星标数:
- 939
- 分叉数:
- 157
- 简介:
- machine learning for artists
|
|---|
| Machine-Learning-Tokyo/DL-workshop-series | 12,527 | - 语言:
- Jupyter Notebook
- 星标数:
- 936
- 分叉数:
- 251
- 简介:
- Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)
|
|---|
| ML-course/master | 12,535 | - 语言:
- Jupyter Notebook
- 星标数:
- 935
- 分叉数:
- 366
- 简介:
- A machine learning course using Python, Jupyter Notebooks, and OpenML
|
|---|
| keyvank/femtoGPT | 12,538 | - 语言:
- Rust
- 星标数:
- 935
- 分叉数:
- 69
- 简介:
- Pure Rust implementation of a minimal Generative Pretrained Transformer
|
|---|
关键参数与规格
关键参数与规格| 参数 | 值 |
|---|
| 语言 | Python |
|---|
| 类别 | Tutorials |
|---|
| 子类别 | ML/AI fundamentals |
|---|
| 星标数 | 941 |
|---|
| 分叉数 | 399 |
|---|
| 贡献者 | 1 |
|---|
| 创建日期 | 2016-10-12 |
|---|
| 更新日期 | 2026-09-16 |
|---|
| 1 天星标数 | 0 |
|---|
| 7 天星标数 | 0 |
|---|
| 排名 | 12,499 |
|---|
| 上次排名 | 12,424 |
|---|
| 排名变化 | -75 |
|---|
| 7 天星标排名 | 13,456 |
|---|
| 上次 7 天星标排名 | 13,467 |
|---|
| 7 天星标排名变化 | 11 |
|---|
| 总星标排名 | 11,684 |
|---|
| 上次总星标排名 | 11,666 |
|---|
| 总星标排名变化 | -18 |
|---|
README 摘录
Fine-tune Convolutional Neural Network in Keras with ImageNet Pretrained Models The reason to create this repo is that there are not many online resources that provide sample codes for performing fine-tuning, and that there is not a centralized place where we can easily download ImageNet pretrained models for common ConvNet architectures such as VGG, Inception, ResNet, and DenseNet. This repo serves to fill this gap by providing working examples of fine-tuning on Cifar10 dataset with ImageNet pretrained
models on popular ConvNet implementations. See this for a comprehensive treatment of fine-tuning Deep Learning Models in Keras Usage For illustration purpose, let's say you want to perform fine-tuning with VGG-16. First, download the ImageNet pretrained weights for VGG-16 to the directory. The schema and sample code for fine-tuning on Cifar10 can be found in . Run the file: The code will automatically download Cifar10 dataset and performs fine-tuning with VGG-16. Please be aware
that it might take some time (up to minutes) for the model to compile and load the ImageNet weights. Finetune with your own dataset If you wish to perform fine-tuning on your own dataset, you have to replace the module which loads the Cifar10 dataset with your own load_data() module to load your own dataset.
常见问题
记录了多少星标和分叉？
flyyufelix/cnn_finetune 有 941 个星标和 399 个分叉。
仓库排名是多少？
flyyufelix/cnn_finetune 排名第 12,499。
记录的语言和类别是什么？
flyyufelix/cnn_finetune 有 941 个星标，语言为 Python，类别为 Tutorials。
哪个仓库排名最接近？
flyyufelix/cnn_finetune 与 ml4a/ml4a.github.io 比较，后者排名第 12,509，有 939 个星标。
Fine-tune Convolutional Neural Network in Keras with ImageNet Pretrained Models
The reason to create this repo is that there are not many online resources that provide sample codes for performing fine-tuning, and that there is not a centralized place where we can easily download ImageNet pretrained models for common ConvNet architectures such as VGG, Inception, ResNet, and DenseNet. This repo serves to fill this gap by providing working examples of fine-tuning on Cifar10 dataset with ImageNet pretrained models on popular ConvNet implementations.
See this for a comprehensive treatment of fine-tuning Deep Learning Models in Keras
Usage
For illustration purpose, let's say you want to perform fine-tuning with VGG-16. First, download the ImageNet pretrained weights for VGG-16 to the imagenet_models directory. The schema and sample code for fine-tuning on Cifar10 can be found in vgg16.py. Run the file:
python vgg16.py
The code will automatically download Cifar10 dataset and performs fine-tuning with VGG-16. Please be aware that it might take some time (up to minutes) for the model to compile and load the ImageNet weights.
Finetune with your own dataset
If you wish to perform fine-tuning on your own dataset, you have to replace the module which loads the Cifar10 dataset with your own load_data() module to load your own dataset.
X_train, Y_train, X_valid, Y_valid = load_data()
In particular, the following image preprocessing step have to be performed to get the format of the dataset compatible with the pretrained models:
# For Tensorflow
# Switch RGB to BGR order
x = x[:, :, :, ::-1]
# Subtract ImageNet mean pixel
x[:, :, :, 0] -= 103.939
x[:, :, :, 1] -= 116.779
x[:, :, :, 2] -= 123.68
# For Theano
# Switch RGB to BGR order
x = x[:, ::-1, :, :]
# Subtract ImageNet mean pixel
x[:, 0, :, :] -= 103.939
x[:, 1, :, :] -= 116.779
x[:, 2, :, :] -= 123.68
ImageNet Pretrained Models
Requirements
- Keras
1.2.2 2.0.5
- Theano 0.8.2 or TensorFlow
0.12.0 1.2.1
Updates
- Keras 2.0.5 and TensorFlow 1.2.1 are supported