| Crates.io | bimm |
| lib.rs | bimm |
| version | 0.3.7 |
| created_at | 2025-06-26 09:31:27.313926+00 |
| updated_at | 2025-09-23 23:23:21.171159+00 |
| description | burn image models |
| homepage | |
| repository | https://github.com/crutcher/bimm |
| max_upload_size | |
| id | 1727086 |
| size | 590,710 |
This is a Rust crate for image models, inspired by the Python timm package.
Examples of loading pretrained model:
use burn::backend::Wgpu;
use bimm::cache::disk::DiskCacheConfig;
use bimm::models::resnet::{PREFAB_RESNET_MAP, ResNet};
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<Wgpu> = prefab
.to_config()
.to_structure()
.init(&device)
.load_pytorch_weights(weights)
.expect("Failed to load weights")
// re-head the model to 10 classes:
.with_classes(10)
// Enable (drop_block_prob) stochastic block drops for training:
.with_stochastic_drop_block(0.2)
// Enable (drop_path_prob) stochastic depth for training:
.with_stochastic_path_depth(0.1);
Conv2d + BatchNorm2d block.Conv2d + Normalization + Activation block.ResNetDropBlock3d / drop_block_3d support.DropBlock2d / drop_block_2d support.burn dependency to 0.18.0.