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project_ipg
ilessio-aiflowlab/project_ipg
project_ipg is a object detection model from ilessio-aiflowlab. Use it when you need objects located in an image. It is set up for pytorch. The card lists the license as apache-2.0.
Adversarial patches trained against YOLOv5l6 on MS COCO 2017 using the IPG method (arXiv:2508.10946).
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Updated Apr 4, 2026
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At a glance
- Task
- Object Detection
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Apr 4, 2026
- SHA
- 8a6ffa4b
Model card
From the Hugging Face model README
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.
- Task
- Object Detection
- Library
- pytorch
- License
- apache-2.0
- Created
- Mar 28, 2026
- Updated
- Apr 4, 2026