There are two types of ResNet in Deep Residual Learning for Image Recognition, by Kaiming He et al. One for ImageNet and another for CIFAR-10.
I had implemented the ResNet-50/101/152 (ImageNet one) by Python with Tensorflow in this repo. You can train my ResNet-50/101/152 without pretrain weights or load the pretrain weights of ImageNet. I had trained and tested my ResNet-50/101/152 on Kaggle Plant Seedings Classification.
- Operating System:
- Arch Linux 4.20.7-1
- Memory
- 64GB
- CUDA:
- CUDA V10.0.130
- CUDNN:
- CUDNN 7.0.5-2
- GPU:
- GTX 1070 8G
- Nvidia driver:
- 390.25
- Python:
- python 3.6.4
- Python package:
- tqdm, bs4, opencv-python, pydensecrf, cython...
- Tensorflow:
- tensorflow-gpu 1.5.0
./ResNet/
├── dataset
├── models
└── pretrain_models
./train/
├── Black-grass
├── Charlock
├── Cleavers
├── Common Chickweed
├── Common wheat
├── Fat Hen
├── Loose Silky-bent
├── Maize
├── Scentless Mayweed
├── Shepherds Purse
├── Small-flowered Cranesbill
└── Sugar beet
- Put train.zip and test.zip (Plant seeding dataset) into {PATH}/ResNet-PreAct/ResNet/dataset/
mv {PATH}/train.zip {PATH}/ResNet-PreAct/ResNet/dataset/
mv {PATH}/test.zip {PATH}/ResNet-PreAct/ResNet/dataset/
- Unzip train.zip and test.zip to the {PATH}/ResNet-PreAct/ResNet/dataset
unzip train.zip
unzip test.zip
- Resize and split the dataset into train, val and test
python ./dataset/resize.py
- Pretrain model
- Move files from resnet_v2_50_val to '{PATH}/models/resnet_v2_50'
- Run test
python test.py --layers 50 --batch 1 --recover 272 - Run train (See Training for more details)
python train.py --layers 50 --batch 16 --val 0 --recover 272 --pretrain 0 - Performance
| Key | Value |
|---|---|
| Method | ResNet50-Preact |
| Language | Python with Tensorflow |
| Pretrain weight | ImageNet |
| Dataset | 20% to validation |
| Best epoch | 272 |
| Validation set accuracy | 96.72% |
| Submission time | 2019/1/18 |
| Result | 96.72% |
- tensorflow/models
- Put resnet_v2_*.ckpt in 'pretrain_models'
- Set parameters
- set number of layers for ResNet
- set loading pretrain weights of ImageNet or not
- set adding validation set into training set or not
- Check Usage for other parameters
python train.py --layers [50|101|152] --pretrain [0|1] --val [0|1]
- For example
python train.py --layers 50 --pretrain 1 --val 0
python val.py --layer [50|101|152] --recover [0+]
- For example
python val.py --layer 50 --recover 299
- Result will be stored as '.csv' file in {PATH}/ResNet/dataset/
python test.py --layers [50|101|152] --batch 1 --recover [0+]
- For example
python test.py --layers 50 --batch 1 --recover 299
- Put the images of training set into {PATH}/ResNet/train/{class_name}/
- If you have testing set, put it into {PATH}/ResNet/test/
- Modify the class name in resize.py
- Resize and split the dataset into train, val, test
- Compute the accuracy of the result
- May be called by train.py and val.py
- Load and preprocess dataset
- May be called by train.py, val.py and test.py
- Parse the command line argument
- May be called by train.py, val.py and test.py
- The implementation of the network
- Test the testing set and store the result in the 'dataset' as .csv file
usage: test.py [-h] [--layers LAYERS] [--classes CLASSES] [--batch BATCH]
[--recover RECOVER]
optional arguments:
-h, --help show this help message and exit
--layers LAYERS select number of layers from [50, 101, 152] (default:
101)
--classes CLASSES number of classes (default: 12)
--batch BATCH batch size (default: 16)
--recover RECOVER recover weights (default: 0)
- Train the network
usage: train.py [-h] [--layers LAYERS] [--classes CLASSES] [--batch BATCH]
[--epoch EPOCH] [--lr LR] [--pretrain PRETRAIN] [--save SAVE]
[--recover RECOVER] [--val VAL]
optional arguments:
-h, --help show this help message and exit
--layers LAYERS select number of layers from [50, 101, 152] (default:
101)
--classes CLASSES number of classes (default: 12)
--batch BATCH batch size (default: 16)
--epoch EPOCH max epoch (default: 300)
--lr LR learning rate (default: 0.001)
--pretrain PRETRAIN load pretrain weights or not. [0, 1]=[False, True]
(default: 1)
--save SAVE save step for saver (default: 1)
--recover RECOVER recover weights and continue training ot not. [-1,
0+]=[False, True] (default: -1)
--val VAL add validation set into training set or not. [0,
1]=[False, True] (default: 0)
- Test the validation set
usage: val.py [-h] [--layers LAYERS] [--classes CLASSES] [--batch BATCH]
[--recover RECOVER]
optional arguments:
-h, --help show this help message and exit
--layers LAYERS select number of layers from [50, 101, 152] (default:
101)
--classes CLASSES number of classes (default: 12)
--batch BATCH batch size (default: 16)
--recover RECOVER recover weights (default: 0)