TRAINING_GUIDE.md

CD-Models Training Guide

Prerequisites

• Python 3.10 is recommended.
• CUDA/PyTorch requirements vary by model. Check each model directory or cloned repo requirements file before training:
  • BiFA/requirements.txt
  • CDMamba/requirement.txt
  • Change3D/requirements.txt
  • ChangeFormer/requirements.txt
  • RSM-CD/requirements.txt
• Set DATA_ROOT before using central configs:

export DATA_ROOT=/path/to/datasets

All central configs use ${DATA_ROOT} and avoid hardcoded absolute dataset paths.

Dataset Setup

The shared loader expects split folders under each dataset root. Folder names are configured in configs/datasets/*.yaml.

LEVIR-CD+:

$DATA_ROOT/LEVIR-CD-plus-256/
  train/A train/B train/Mask
  val/A   val/B   val/Mask
  test/A  test/B  test/Mask

WHU-CD:

$DATA_ROOT/WHU-CD/
  train/A train/B train/OUT
  val/A   val/B   val/OUT
  test/A  test/B  test/OUT

WildFireS2:

$DATA_ROOT/WildFireS2/
  train/A train/B train/label
  val/A   val/B   val/label
  test/A  test/B  test/label

Download sources should be confirmed from the original dataset pages before publication. Keep raw data outside this repo and point DATA_ROOT at the prepared benchmark folders.

Pretrained Weights

Model	Backbone	Download URL	Local Path
change3d	X3D-L	Original Change3D README/model release	Change3D/model/X3D_L.pyth
ifnet / dsifn	VGG-16	Torchvision or original DSIFN README	model_repos/DSIFN/pretrained/vgg16.pth
changemamba	VMamba	ChangeMamba README Zenodo/HuggingFace link	model_repos/ChangeMamba/pretrained/vmamba_tiny.pth
changer	ResNet-18	open-cd / torchvision ResNet-18	model_repos/open-cd/pretrained/resnet18.pth
tinycd	EfficientNet-B4	TinyCD README	model_repos/TinyCD/pretrained/efficientnet_b4.pth

Wrappers print a clear required-weight block when a required file is missing.

Automatic Weight Downloads

All pretrained backbone weights are downloaded automatically on first training run. VMamba weights for ChangeMamba are downloaded from Zenodo record 14037770 by utils/weight_downloader.py.

Model	Backbone	Download Source	Auto?	Est. Size
ChangeMamba	VMamba-Tiny	Zenodo records/14037770	yes	~86 MB
ChangeMamba	VMamba-Small	Zenodo records/14037770	yes	~178 MB
ChangeMamba	VMamba-Base	Zenodo records/14037770	yes	~391 MB
Changer	ResNet-18	PyTorch Hub / torchvision	yes	~45 MB
DSAMNet	ResNet-18	PyTorch Hub / torchvision	yes	~45 MB
TinyCD	EfficientNet-B4	timm / torchvision backend	yes	~75 MB
HANet	ResNet-50	PyTorch Hub / torchvision	yes	~98 MB
CGNet	ResNet-50	PyTorch Hub / torchvision	yes	~98 MB
DSIFN	VGG-16	PyTorch Hub / torchvision	yes	~528 MB
ELGC-Net	MiT-b0	timm / HuggingFace	yes	~15 MB
ChangeFormer	MiT-b1	timm / HuggingFace	yes	~28 MB

To pre-download all managed weights before training:

python utils/weight_downloader.py --prefetch-all

Quick Start

Train one model on one dataset:

python run_training.py --model bifa --dataset wildfire_s2

Train all models on one dataset:

python run_training.py --model all --dataset wildfire_s2 --dry-run

Run evaluation only:

python run_training.py --model bifa --dataset wildfire_s2 --eval-only

Adding A New Model

1. Confirm the official GitHub URL and license.
2. Clone into model_repos/<model_name>.
3. Read the cloned README, configs, model code, and training code.
4. Add configs/models/<model_name>.yaml.
5. Add or update train/train_<model_name>.py.
6. Register the model in configs/models/registry.yaml.
7. Ensure the wrapper accepts --dataset, --gpu, --resume, --eval-only, and --dry-run.
8. Save final metrics with utils.results_writer.save_metrics().

Results

Per-run metrics are written to:

results/<model>/<dataset>/metrics_test.json

The aggregate table is regenerated at:

results/comparison_table.csv

Columns: Model, Dataset, F1, IoU, OA, Precision, Recall, Kappa.

Known Issues

Existing WildFire Wrappers

Partially resolved. Central wrappers now accept every configured dataset and validate the requested dataset through CDDataset. The original nested research scripts still contain their own model-specific data loops, so full non-WildFire training requires each upstream loop to be run against compatible prepared data or rewritten in detail.

ChangeMamba

Resolved for setup. Cloned to model_repos/ChangeMamba. Requires running python model_repos/ChangeMamba/install_deps.py once before training. VMamba pretrained weights are downloaded automatically from Zenodo record 14037770 on first training run.

Changer

Resolved for setup. Uses open-cd cloned to model_repos/open-cd. It requires the same mmengine/mmcv family of dependencies as open-cd. ResNet-18 weights download automatically through torchvision on first run.

DSAMNet

Resolved for setup. Cloned from liumency/DSAMNet. The original repo exposes loss/BCL.py and loss/DiceLoss.py; wrappers must use that metric-learning loss path rather than replacing it with BCE+Dice. Labels are expected as binary 0/1 maps; CDDataset converts 255-valued masks to 1.

TinyCD

Resolved for setup. Cloned from AndreaCodegoni/Tiny_model_4_CD. EfficientNet-B4 weights download automatically through the weight downloader/timm warmup. TinyCD is RGB-only; run_training.py skips tinycd/wildfire_s2 by default.

HANet / CGNet

Resolved for setup. Cloned from ChengxiHAN/HANet-CD and ChengxiHAN/CGNet-CD. Both repos use similar custom training utilities and include dataset helpers under utils/. ResNet-50 weights are warmed automatically through torchvision.