kapil
feat: implement core RustAutoScoreEngine framework including data loading, model architecture, training loop, inference, and GUI server
8e03aff
use burn::backend::wgpu::WgpuDevice;
use burn::backend::Wgpu;
use rust_auto_score_engine::args::{AppArgs, Command};
use rust_auto_score_engine::server::start_gui;
use rust_auto_score_engine::tests::test_model;
use rust_auto_score_engine::train::{train, TrainingConfig};
fn main() {
let app_args = AppArgs::parse();
let device = WgpuDevice::default();
match app_args.command {
Command::Gui => {
println!("🌐 [Burn-DartVision] Starting Professional Dashboard...");
tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()
.unwrap()
.block_on(start_gui(device));
}
Command::Test { img_path } => {
test_model(device, &img_path);
}
Command::Train => {
println!("πŸš€ [Burn-DartVision] Starting Full Project Training...");
let dataset_path = "dataset/labels.json";
let config = TrainingConfig {
num_epochs: 50,
batch_size: 1,
lr: 1e-3,
};
train::<burn::backend::Autodiff<Wgpu>>(device, dataset_path, config);
}
}
}