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schema: anima.module/v1
module:
name: project_setter
codename: SETTER
wave: 10
theme: WARDOG
tier: T2
score: 37
paper:
title: "SFFNet: Synergistic Feature Fusion for UAV Image Detection"
arxiv_id: "2604.03176"
arxiv_url: https://arxiv.org/abs/2604.03176
github: https://github.com/CQNU-ZhangLab/SFFNet
status: partial # upstream code is incomplete; we reimplement from paper
backbone:
framework: ultralytics
model: yolo26m
custom_yaml: configs/models/yolo26m-sff.yaml
compute:
backends: [cuda, mlx, cpu]
default: cuda
min_vram_gb: 14
target_vram_gb: 18
datasets:
provenance: configs/datasets/provenance.json
required:
- VisDrone
optional:
- UAVDT
- SeaDronesSee
- UAVid
- MegaUAV-1.8M
- NighthawkMegaHiRes
artifacts:
project_dir: /mnt/artifacts-datai/project_setter
checkpoints: /mnt/artifacts-datai/checkpoints/project_setter
logs: /mnt/artifacts-datai/logs/project_setter
tensorboard: /mnt/artifacts-datai/tensorboard/project_setter
exports: /mnt/artifacts-datai/exports/project_setter
exports:
formats: [pth, safetensors, onnx, trt_fp16, trt_fp32]
hf_repo: ilessio-aiflowlab/project_setter
docker:
cuda:
dockerfile: docker/Dockerfile.cuda
image: robotflowlabs/anima-setter:cuda
mlx:
dockerfile: docker/Dockerfile.mlx
image: robotflowlabs/anima-setter:mlx
serve:
entrypoint: anima_setter.serve:build_app
port: 8080
env:
ANIMA_BACKEND: cuda
ANIMA_MODEL_PATH: /models/setter.pt
healthcheck: GET /health
ros2:
contract: src/anima_setter/ros2_contract.py
topics:
input:
- name: /setter/image_in
type: sensor_msgs/Image
output:
- name: /setter/detections
type: setter_msgs/DetectionArray
- name: /setter/status
type: setter_msgs/Status
entrypoints:
cli: anima_setter.cli:main
train: scripts/train_sff.py
preflight: scripts/preflight_datasets.py
siblings:
count: 33
wave_theme: "War Dog Breeds — UAV/Drone defense"