bunsen::kits::bimm

Bunsen/Burn Image Models — an incremental port of the timm (Torch Image Models) ecosystem to burn.

bimm is the home for full image-recognition model families inside bunsen. Each model lives next to its prefab configurations and pretrained-weight loaders, so picking one up is closer to “pick a prefab, fetch weights, init” than “wire together a stack of blocks.”

API: https://docs.rs/bunsen/latest/bunsen/kits/bimm/

Current models

ResNet

The classic ResNet family (arXiv:1512.03385), with prefab configurations and a pretrained-weight loader that pulls from the torchvision checkpoints.

A representative usage:

#![allow(unused)]
fn main() {
use bunsen::{
    cache::DiskCacheConfig,
    kits::bimm::resnet::{PREFAB_RESNET_MAP, ResNet},
};
use burn::backend::Flex;

let device = Default::default();

let prefab = PREFAB_RESNET_MAP.expect_lookup_prefab("resnet18");

let weights = prefab
    .expect_lookup_pretrained_weights("tv_in1k")
    .fetch_weights(&DiskCacheConfig::default())
    .expect("Failed to fetch weights");

let model: ResNet<Flex> = prefab
    .to_config()
    .to_structure()
    .init(&device)
    .load_pytorch_weights(weights)
    .expect("Failed to load weights")
    // Re-head to 10 classes:
    .with_classes(10)
    // Stochastic block drops for training:
    .with_stochastic_drop_block(0.2)
    // Stochastic depth for training:
    .with_stochastic_path_depth(0.1);
}

Swin Transformer V2

The Swin Transformer V2 family (reference implementation), implemented in terms of windowed self-attention blocks, patch merging, and relative-position biases.

See the bunsen::kits::bimm::swin::v2 module for the full configuration API.