https://github.com/kleinyuan/tf-ft
Implementation on tensorflow fine tuning of generic CNN based model
https://github.com/kleinyuan/tf-ft
alexnet convolutional-neural-networks fine-tuning fine-tuning-cnns tensorflow vgg
Last synced: about 1 month ago
JSON representation
Implementation on tensorflow fine tuning of generic CNN based model
- Host: GitHub
- URL: https://github.com/kleinyuan/tf-ft
- Owner: KleinYuan
- License: mit
- Created: 2017-10-07T00:24:09.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-10-19T18:56:21.000Z (over 7 years ago)
- Last Synced: 2025-03-26T05:33:12.050Z (about 2 months ago)
- Topics: alexnet, convolutional-neural-networks, fine-tuning, fine-tuning-cnns, tensorflow, vgg
- Language: Python
- Size: 104 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Intro
A project for generic fine-tuning/extending
(freeze CNN layers and connect with your own customzied FC layers)
pre-trained/existing models, such as AlexNet, VGG-16, ... (More CNN based).
# Manual
Step1. Put your datasets somewhere and train.csv under `/data/alexnet_finetune`
Step2. train.csv should be the format with first column to be the locations of images (I put an example in the folder)
Step3. Run:
```
# Download Pre-trained model
bash setup.sh
export PYTHONPATH='.'
# You can also take advantages of the Makefile, which actually inspires me dockerize this project if I have time
python apps/finetune_alexnet_train.py
```
# Keys
One dependencies we live on is the pre-trained [weights](http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy) from BVLC.
That said you don't wanna mess up with the name scope of those layers you wanna freeze. So be careful.
How we load the weights (`/services/weights_load_services`) should provide you enough information.
You are free to add as many layers as you like and just be aware that `conv1 ~ conv5` and `fc6~fc8` are those layers (as well as the NAME) you can load a pre-trained weights.
I try very hard to implement what I thought is the best practice for tensorflow -- separate:
- [X] architecture model (models/alexnet.py)
- [X] computation model (models/finetune_graph.py)
- [X] trainer model (models/train.py)
- [X] training app (apps/finetune_alexnet_train.py)
so that you can independently change any part of those without impacting other components.
A better example will be my anther repo -- [generic CNN in tensorflow](https://github.com/KleinYuan/cnn), which may have a better idea of what I am trying to do.
Since for this project, you need to sacrifice some graceful implementation due to the constraints of the pre-trained weights organization.
# references
1. [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
2. [Fine Tuning AlexNet on Tensorflow Example](https://kratzert.github.io/2017/02/24/finetuning-alexnet-with-tensorflow.html#finetune)
3. [AlexNet Explainations in details](http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf)