https://github.com/frgfm/torchhawk
A benchmark on popular deep learning tricks on computer vision tasks with famous datasets
https://github.com/frgfm/torchhawk
computer-vision deep-learning image-classification mnist pytorch
Last synced: 2 months ago
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A benchmark on popular deep learning tricks on computer vision tasks with famous datasets
- Host: GitHub
- URL: https://github.com/frgfm/torchhawk
- Owner: frgfm
- License: mit
- Created: 2019-06-10T09:26:05.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-10T15:17:03.000Z (over 5 years ago)
- Last Synced: 2024-12-29T17:41:31.370Z (4 months ago)
- Topics: computer-vision, deep-learning, image-classification, mnist, pytorch
- Language: Python
- Size: 166 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Torch Hawk
A benchmark on popular deep learning tricks on computer vision tasks with famous datasets.
## Installation
This project was developed in Python 3.7 with PyTorch 1.1. If you have a previous version of PyTorch, please consider switching `torch.utils.tensorboard` dependency to [tensorboardX](https://github.com/lanpa/tensorboardX).
```bash
git clone https://github.com/frgfm/TorchHawk.git
cd TorchHawk
pip install -r requirements.txt
mkdir img_classification/logs
```
## Usage
How to train your model
### Start your training
```bash
python img_classification/main.py lenet5 --dataset mnist -n 10 --lr 5e-4 --batch_size 8 --optimizer adam --drop_rate 0.3 --workers 8
```
Depending on your seed, you should get similar results to:
```bash
Data transformations:
Compose(
RandomRotation(degrees=(-30, 30), resample=False, expand=False)
ToTensor()
Normalize(mean=(0.1307,), std=(0.3081,))
)
Epoch 1/10 - Validation loss: 0.08775 (Acc@1: 96.97%)
Validation loss decreased inf --> 0.08775: saving state...
Epoch 2/10 - Validation loss: 0.05491 (Acc@1: 98.20%)
Validation loss decreased 0.08775 --> 0.05491: saving state...
Epoch 3/10 - Validation loss: 0.06731 (Acc@1: 97.83%)
Epoch 2: reducing learning rate of group 0 to 2.5000e-04.
Epoch 4/10 - Validation loss: 0.04157 (Acc@1: 98.69%)
Validation loss decreased 0.05491 --> 0.04157: saving state...
Epoch 5/10 - Validation loss: 0.03381 (Acc@1: 98.85%)
Validation loss decreased 0.04157 --> 0.03381: saving state...
Epoch 6/10 - Validation loss: 0.03288 (Acc@1: 98.97%)
Validation loss decreased 0.03381 --> 0.03288: saving state...
Epoch 7/10 - Validation loss: 0.03146 (Acc@1: 99.07%)
Validation loss decreased 0.03288 --> 0.03146: saving state...
Epoch 8/10 - Validation loss: 0.03196 (Acc@1: 99.03%)
Epoch 7: reducing learning rate of group 0 to 1.2500e-04.
Epoch 9/10 - Validation loss: 0.02716 (Acc@1: 99.26%)
Validation loss decreased 0.03146 --> 0.02716: saving state...
Epoch 10/10 - Validation loss: 0.02684 (Acc@1: 99.22%)
Validation loss decreased 0.02716 --> 0.02684: saving state...
```
### Running the tensorboard interface
Start the tensorboard server locally to visualize your training losses:
```bash
tensorboard --logdir=training-outputs/logs
```
Then open a new tab in your browser and navigate to `:6006` to monitor your training.
## Submitting a request / Reporting an issue
Regarding issues, use the following format for the title:
> [Topic] Your Issue name
Example:
> [State saving] Add a feature to automatically save and load model states