metadata
license: apache-2.0
tags:
- adversarial-patches
- object-detection
- yolov5
- security
- red-team
- anima
library_name: pytorch
pipeline_tag: object-detection
IPG — Incremental Patch Generation
Adversarial patches trained against YOLOv5l6 on MS COCO 2017 using the IPG method (arXiv:2508.10946).
Model Description
This repository contains 25 adversarial patches (64×64 pixels) optimized to reduce object detection confidence using the hiding attack objective.
Available Formats
| Format | Directory | Description |
|---|---|---|
| PyTorch (.pt) | pth/ |
Full checkpoint with training history |
| SafeTensors | safetensors/ |
Optimized tensor format |
| Combined SafeTensors | all_patches.safetensors |
All patches in one file |
| ONNX | onnx/ |
Cross-platform inference |
| TensorRT FP16 | trt_fp16/ |
NVIDIA optimized (half precision) |
| TensorRT FP32 | trt_fp32/ |
NVIDIA optimized (full precision) |
Usage
from safetensors.torch import load_file
# Load single patch
data = load_file("safetensors/patch_00.safetensors")
patch = data["patch"] # [3, 64, 64]
# Load all patches
all_data = load_file("all_patches.safetensors")
patches = [all_data[f"patch_{i:02d}"] for i in range(25)]
Training Details
- Detector: YOLOv5l6u
- Dataset: MS COCO 2017 (20% accessible subset, ~23K images)
- Patches: 25 × 200 epochs
- Patch size: 64×64
- Optimizer: Adam, lr=0.2, StepLR(50, γ=0.266)
- Hardware: NVIDIA L4 23GB
- Training time: 11.7h
Part of ANIMA
This module is part of the ANIMA security layer for adversarial robustness testing.
Built with ANIMA by Robot Flow Labs.