FloralHercules/WAMI_Methods_Model_Public

MDF-Det and reproduced-baseline model weights

This directory is prepared for a future Hugging Face model repository. It is kept outside the GitHub source-code clone and must not be added to the MDF-Det Git repository.

Contents

Directory	Files	Purpose
MDF-Det/	Binary classifier, normalization, SA-TD regression model, regression normalization and SPGF-V4	Complete revised MDF-Det inference
Dual-CNN/	Binary classifier, regression model and their normalization files	Original two-CNN baseline inference
baselines/ClusterNet/	best.pt	ClusterNet + FoveaNet reproduction
baselines/HMRN/	best.pt	HMRN reproduction
baselines/HM-Net/	best.pt	HM-Net reproduction
baselines/CATLoss/	best.pt	CATLoss reproduction

TTE-KH is intentionally excluded. Only the selected best checkpoints are included; latest checkpoints, optimizer backups, ablation weights, caches and experimental results are omitted.

Source-code mapping

After downloading, either update each command-line/configuration checkpoint path or copy the files to the expected local locations:

• MDF-Det binary classifier and normalization correspond to Models/BinaryClassification/saved_model_2.model and saved_image_norm_2.model.
• MDF-Det SA-TD files correspond to regression_spatial_attention.h5 and regression_norm_params.npz.
• MDF-Det SPGF corresponds to models/semantic_prior_v4_six_aoi_excluded/best_semantic_prior_v4.h5.
• Reproduced PyTorch baselines use the checkpoint paths in their JSON inference configurations under the public baselines/ source directory.

Integrity

MANIFEST.csv records the byte size and SHA-256 digest of every weight file. Verify downloaded files before inference. The current bundle contains 13 model artifacts and is approximately 419.63 MiB.

Dataset

The WPAFB imagery and annotations are not part of this bundle. For research dataset access, contact likangqiushi20@nudt.edu.cn.