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DEF-dhif – AI Model by ilessio-aiflowlab | AlphaNeural AI

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ilessio-aiflowlab/
DEF-dhif

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small-target-detection

dynamic-convolution

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DEF-dhif: Dynamic High-frequency Convolution for IR Small Target Detection

ANIMA defense module implementing DHiF (Dynamic High-frequency Filter) convolution for infrared small target detection (IRSTD).

Architecture

UNet backbone with DHiF convolution in bottleneck
DHiF generates input-dependent dynamic operators for high-frequency enhancement
Trained on NUAA-SIRST dataset (255 train / 85 test images)

Paper

Title: Dynamic High-frequency Convolution for Infrared Small Target Detection
ArXiv: 2602.02969

Exports

model.pth — PyTorch checkpoint
model.safetensors — Safetensors weights
model.onnx — ONNX (opset 17, dynamic axes)
model_fp16.plan — TensorRT FP16
model_fp32.plan — TensorRT FP32

Usage

python

import torch
from safetensors.torch import load_file
from def_dhif.models.unet_dhif import UNetDHiF

model = UNetDHiF(in_channels=1, out_channels=1, base_ch=64)
state = load_file("model.safetensors")
model.load_state_dict(state)
model.eval()

# Single-channel IR image, padded to 512x512
x = torch.randn(1, 1, 512, 512)
pred = model(x)  # Binary segmentation mask

Built with ANIMA by Robot Flow Labs