https://github.com/andresmpa/dl-vision
Deep Learning Vision Algorithms
https://github.com/andresmpa/dl-vision
alexnet computer-vision deep-learning resnetx18 vggx16 vggx19
Last synced: 7 months ago
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Deep Learning Vision Algorithms
- Host: GitHub
- URL: https://github.com/andresmpa/dl-vision
- Owner: AndresMpa
- License: mit
- Created: 2023-12-16T02:39:41.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-31T03:53:04.000Z (almost 2 years ago)
- Last Synced: 2025-01-26T18:47:30.571Z (8 months ago)
- Topics: alexnet, computer-vision, deep-learning, resnetx18, vggx16, vggx19
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Learning Vision Algorithms
## What is this?
You're seeing a computer vision test field, under the presented architecture, you can easily
try some architecture such as AlexNet, VGGx16, VGGx19 and ResNetx18
## Usage
### To run virtual environments
#### Windows
```bath
python -m venv env
env\Scripts\activate
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install numpy matplotlib python-dotenv plyer
```
#### Linux
```bash
python -m venv env
source env/bin/activate
pip install torch torchvision torchaudio
pip install numpy matplotlib python-dotenv plyer
```
### Setting environment
.env file is required to setting the environment of this project, along with the virtual environment
this file sets each section of the project. Here is a example
```bash
# Architecture
NET_ARCH=AlexNet
USE_CUDA=1
# Using model
MODELS_PATH="models"
USE_MODEL=1
# Dataset
DATASET="CIFAR10"
DATA_PATH="./data"
BATCH_SIZE=8
# Image management
IMG_SIZE=224
IMG_START_INDEX=0
# Training
ITERATIONS=1
LEARNING_RATE=0.01
MOMENTUM_VALUE=0.8
CATCH_INTERVAL=5
# Loss
LOST_CRITERIA="CrossEntropyLoss"
# Management
RESULTS_PATH="results"
LOG_PATH="log"
AUTOCLEAR=0
```
#### Available architectures
Just write any of the following on the NET_ARCH env var
- AlexNet
- VGG16
- VGG19
- ResNet
> USE_CUDA=1 means that the host can and will use CUDA by default it uses the processor
### Available dataset
Data sets can be defined inside the .env file in the $DATASET$ variable, available dataset
work for image detection or segmentation from PyTorch documentation specification follow this
[link](https://pytorch.org/vision/stable/datasets.html#image-detection-or-segmentation) to see
other options
| Keyword | Size | Dataset |
| ---------- | ---- | ----------------------------------------------------------- |
| "CelebA" | 200K | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) |
| "CIFAR10" | 60K | [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html) |
| "CIFAR100" | 60K | [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html) |
> Note: The difference between CIFAR10 and CIFAR100 is the amount of classes, CIFAR10 contains 10
> while CIFAR100 contains 100 see "The CIFAR-100 dataset" specifications
`LOST_CRITERIA` means the lost function, available options are "BCELoss" and "CrossEntropyLoss"