WORK IN PROGRESS
Solutions for https://www.kaggle.com/c/dogs-vs-cats and https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition competition.
Here is table with results, but hyperparameters of neural nets can drastically affect accuracy, so feel free to try it out, reproduce results and improve them!
| Name | Acc. test | finetuned Acc. test. | Train time | Forward pass time | On disk model size | Year | Paper |
|---|---|---|---|---|---|---|---|
| AlexNet | 93.65% | 97.98% | 35m | 3.01 ms | 227.5Mb | 2012 | link |
| SqeezeNet v1.1 | 92.46% | 98.87% | ~2h | 3.91 ms | 2.9Mb | 2016 | link |
| GoogLeNet | 94.62% | 99.58% | 50m | 11.73 ms | 41.3Mb | 2014 | link |
| VGG-16 | 96.51% | 99.40% | 5h20m | 15.41 ms | 537.1Mb | 2014 | link |
| VGG-19 | 97.42% | 99.48% | 25h50m | 19.23 ms | 558.3Mb | 2014 | link |
| Network-In-Network | 93.65% | 98.49% | ~2h | 3.17 ms | 26.3Mb | 2014 | link |
| ResNet-50 | 95.84% | 99.52% | 18h | 24.91 ms | 94.3Mb | 2015 | link |
| ResNet-101 | 96.39% | 99.48% | 1d 20h | 40.95 ms | 170.5Mb | 2015 | link |
Test accuracy was measured on train-test split 80%-20%.
1.learning_from_scratch is folder with models that were trained in NVIDIA DIGITS with Caffe backend.
2.finetuning is folder with models that were finetuned from models trained on ImageNet.
3.feature_extraction is folder where CNN used only as feature extractor and SVM was used for training.
Tested on system with following configuration:
Ubuntu version:
`lsb_release -a`
Ubuntu 14.04.4 LTS
`uname -a`
Linux myuser-computer 3.19.0-61-generic #69~14.04.1-Ubuntu SMP Thu Jun 9 09:09:13 UTC 2016 x86_64 x86_64 x86_64 GNU/Linux
gcc version:
`gcc --version`
gcc (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4
DIGITS version:
`./digits-devserver --version`
4.1-dev
Caffe version:
`git status`
branch caffe-0.15
`git log -n 1`
commit e638c0b1cb19afff50d830ce87cc1898f18568fd
Author: Sergei Nikolaev <[email protected]>
Date: Wed Aug 31 14:32:28 2016 -0700
Mark 0.15.13
CPU:
`cat /proc/cpuinfo | grep "model name"`
Intel(R) Core(TM)2 Duo CPU E8500 @ 3.16GHz
GPU:
`nvidia-smi`
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.44 Driver Version: 367.44 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1070 On | 0000:01:00.0 On | N/A |
| 27% 38C P8 10W / 151W | 150MiB / 8108MiB | 0% Default |
+-------------------------------+----------------------+----------------------+