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@@ -36,3 +36,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 exports/project_fenris_v0.1.0_fp32.engine filter=lfs diff=lfs merge=lfs -text
 exports/project_fenris_v0.1.0_fp16.engine filter=lfs diff=lfs merge=lfs -text
 exports/project_fenris_v0.1.0.onnx.data filter=lfs diff=lfs merge=lfs -text
+onnx/fenris_v0.1.0.onnx.data filter=lfs diff=lfs merge=lfs -text
+tensorrt/fenris_v0.1.0_fp16.engine filter=lfs diff=lfs merge=lfs -text
+tensorrt/fenris_v0.1.0_fp32.engine filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,130 @@
+---
+tags:
+  - robotics
+  - anima
+  - fenris
+  - infrared
+  - object-detection
+  - yolo
+  - irstd
+  - robot-flow-labs
+library_name: pytorch
+pipeline_tag: object-detection
+license: apache-2.0
+---
+
+# FENRIS -- AA-YOLO: Anomaly-Aware YOLO for Infrared Small Target Detection
+
+Part of the [ANIMA Perception Suite](https://robotflowlabs.com) by Robot Flow Labs.
+
+## Paper
+
+**AA-YOLO: Anomaly-Aware YOLO for Infrared Small Target Detection**
+arXiv: [2510.04741](https://arxiv.org/abs/2510.04741)
+
+## Architecture
+
+AA-YOLO introduces an Anomaly-Aware Detection Head (AADH) that reformulates IR target detection as a statistical anomaly detection problem. The AADH adds only +0.2M parameters and +5% FLOPs to any YOLO backbone. Key components:
+
+1. **Spatial Filtering Block** -- Two Conv1d layers (3x3) with BatchNorm/ReLU, compressing to C=8 channels
+2. **Statistical Anomaly Test** -- Multivariate hypothesis test computing `-ln(F_mu2)` anomaly scores
+3. **Scaled Sigmoid Activation** -- Temperature-scaled sigmoid (alpha=0.001) for sharp anomaly response
+4. **Backbone-agnostic** -- Drop-in head for YOLOv5/v7/v9 backbones
+
+This checkpoint uses the **AA-YOLOv7t** backbone (YOLOv8n architecture as v7-tiny proxy).
+
+## Training Summary
+
+| Parameter | Value |
+|-----------|-------|
+| Dataset | SIRST (427 images: 256 train / 85 val / 86 test) |
+| Backbone | AA-YOLOv7t (YOLOv8n + AADH) |
+| Epochs | 100 |
+| Optimizer | SGD (lr=0.01, momentum=0.937, weight_decay=0.0005) |
+| Scheduler | Cosine annealing, 3 epoch warmup |
+| Precision | FP16 mixed precision |
+| Best val_loss | 1.0874 (epoch 73) |
+| Final train_loss | 1.0715 |
+| Backbone freeze | First 10 epochs |
+| Hardware | NVIDIA L4 (23GB) |
+| Training time | ~2 min (100 epochs) |
+
+### Loss Curve
+
+- Epoch 0: train=2.60, val=2.50
+- Epoch 25: train=1.14, val=1.15
+- Epoch 50: train=1.09, val=1.10
+- Epoch 73 (best): train=1.08, val=1.09
+- Epoch 99: train=1.07, val=1.09
+
+## Exported Formats
+
+| Format | File | Size | Use Case |
+|--------|------|------|----------|
+| PyTorch (.pth) | `pytorch/fenris_v0.1.0.pth` | 12 MB | Training, fine-tuning |
+| SafeTensors | `pytorch/fenris_v0.1.0.safetensors` | 12 MB | Fast loading, safe |
+| ONNX (opset 18) | `onnx/fenris_v0.1.0_allinone.onnx` | 12.3 MB | Cross-platform inference |
+| TensorRT FP32 | `tensorrt/fenris_v0.1.0_fp32.engine` | ~17 MB | Full precision inference |
+| TensorRT FP16 | `tensorrt/fenris_v0.1.0_fp16.engine` | ~8 MB | Edge deployment (Jetson/L4) |
+| Training checkpoint | `checkpoints/best.pth` | 24 MB | Resume training (includes optimizer state) |
+
+## Quick Start
+
+```python
+import torch
+from safetensors.torch import load_file
+
+# Load SafeTensors (recommended)
+state_dict = load_file("pytorch/fenris_v0.1.0.safetensors")
+
+# Or load PyTorch
+state_dict = torch.load("pytorch/fenris_v0.1.0.pth", map_location="cpu")
+```
+
+### ONNX Inference
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+session = ort.InferenceSession("onnx/fenris_v0.1.0_allinone.onnx")
+input_data = np.random.randn(1, 3, 256, 256).astype(np.float32)
+outputs = session.run(None, {"ir_input": input_data})
+```
+
+### TensorRT Inference
+
+```python
+import tensorrt as trt
+
+runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
+with open("tensorrt/fenris_v0.1.0_fp16.engine", "rb") as f:
+    engine = runtime.deserialize_cuda_engine(f.read())
+```
+
+## Datasets
+
+- **SIRST** (427 images, 256x256) -- Primary benchmark
+- **IRSTD-1k** (1,000 images, 512x512) -- Extended evaluation
+- **VEDAI** (1,200 images, IR satellite) -- Cross-domain evaluation
+
+## Target Metrics (Paper)
+
+| Metric | Target | Dataset |
+|--------|--------|---------|
+| F1 | >= 98.1% | SIRST |
+| AP_s | >= 79.8% | SIRST |
+| AP | >= 72.0% | IRSTD-1k |
+| AP | >= 65.0% | VEDAI |
+
+## ANIMA Module
+
+- **Module name:** fenris
+- **Category:** perception.foundation
+- **Container:** `ghcr.io/robotflow-labs/anima-fenris:0.1.0`
+- **API port:** 8080
+- **Health:** `/health`, `/ready`, `/info`
+
+## License
+
+Apache 2.0 -- Robot Flow Labs / AIFLOW LABS LIMITED

TRAINING_REPORT.md

@@ -0,0 +1,86 @@
+# TRAINING REPORT -- FENRIS v0.1.0
+
+## Summary
+
+| Field | Value |
+|-------|-------|
+| Module | FENRIS (AA-YOLO) |
+| Version | 0.1.0 |
+| Date | 2026-04-02 |
+| Status | Training Complete |
+
+## Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Model | AA-YOLOv7t (YOLOv8n backbone + AADH heads) |
+| Dataset | SIRST (427 images: 256 train / 85 val / 86 test) |
+| Resolution | 256x256 |
+| Epochs | 100 |
+| Optimizer | SGD (lr=0.01, momentum=0.937, wd=0.0005) |
+| Scheduler | Cosine annealing, 3 epoch linear warmup |
+| Precision | FP16 mixed precision (CUDA) |
+| Gradient clip | max_norm=10.0 |
+| Batch size | 16 |
+| Backbone freeze | First 10 epochs |
+| Augmentation | Mosaic, HSV (h=0.015, v=0.4), horizontal flip (0.5) |
+| Seed | 42 |
+| Pretrained | COCO weights |
+
+## Hardware
+
+| Resource | Spec |
+|----------|------|
+| GPU | NVIDIA L4 (23GB VRAM) |
+| CUDA | 12.4 |
+| PyTorch | 2.6.0 |
+| Training time | ~2 minutes (100 epochs) |
+
+## Results
+
+### Training Metrics
+
+| Epoch | train_loss | val_loss | lr_backbone | lr_head |
+|-------|-----------|----------|-------------|---------|
+| 0 | 2.604 | 2.501 | 0.001 | 0.010 |
+| 10 | 1.167 | 1.184 | 0.010 | 0.010 |
+| 25 | 1.110 | 1.118 | 0.009 | 0.009 |
+| 50 | 1.094 | 1.102 | 0.005 | 0.005 |
+| 73 (best) | 1.077 | 1.087 | 0.000 | 0.002 |
+| 99 (last) | 1.072 | 1.090 | 0.000 | 0.000 |
+
+### Loss Decomposition (Best Epoch 73)
+
+| Component | Train | Val |
+|-----------|-------|-----|
+| obj_loss | 0.0103 | 0.0101 |
+| cls_loss | 0.0103 | 0.0102 |
+| reg_loss | 1.0567 | 1.0670 |
+| **total** | **1.077** | **1.087** |
+
+## Export Pipeline
+
+| Format | File | Size | Status |
+|--------|------|------|--------|
+| PyTorch (.pth) | fenris_v0.1.0.pth | 12.0 MB | Done |
+| SafeTensors | fenris_v0.1.0.safetensors | 11.9 MB | Done |
+| ONNX (opset 18) | fenris_v0.1.0_allinone.onnx | 12.3 MB | Done |
+| TensorRT FP32 | fenris_v0.1.0_fp32.engine | ~17 MB | Done |
+| TensorRT FP16 | fenris_v0.1.0_fp16.engine | ~8 MB | Done |
+
+## Artifact Locations
+
+```
+/mnt/artifacts-datai/checkpoints/project_fenris/best.pt     -- best checkpoint (epoch 73)
+/mnt/artifacts-datai/checkpoints/project_fenris/last.pt     -- last checkpoint (epoch 99)
+/mnt/artifacts-datai/models/project_fenris/                 -- pth + safetensors
+/mnt/artifacts-datai/exports/project_fenris/                -- ONNX + TRT engines
+/mnt/artifacts-datai/logs/project_fenris/                   -- training logs
+```
+
+## Next Steps
+
+1. Evaluate on TEST split (86 images) -- compute F1, AP_s vs paper targets
+2. Train additional backbones (YOLOv5n, YOLOv5s, YOLOv9t)
+3. Download VEDAI + IRSTD-1k for multi-dataset evaluation
+4. Frugal learning experiment (25 images)

anima_module.yaml

@@ -1,57 +1,37 @@
-schema_version: "1.0"
-
+schema_version: '1.0'
 module:
   name: fenris
   version: 0.1.0
-  display_name: "FENRIS — AA-YOLO Anomaly-Aware Infrared Small Target Detection"
-  description: >
-    Anomaly-aware YOLO for infrared small target detection with frugal
-    field adaptation (25 images minimum). Deployable on edge hardware.
-  category: perception.detection_ir
-  license: Apache-2.0
-  paper: "https://arxiv.org/abs/2510.04741"
-
+  display_name: FENRIS
+  description: FENRIS — Foundation perception module for the ANIMA robotics stack
+  category: perception.foundation
+  license: MIT
 capabilities:
   provides:
-    - type: detection
-      subtype: infrared_small_target
-    - type: anomaly_detection
-      subtype: thermal_anomaly
-
+  - type: perception
+    subtype: foundation
 interface:
-  inputs:
-    - name: ir_frame
-      ros2_type: sensor_msgs/msg/Image
-  outputs:
-    - name: detections
-      ros2_type: std_msgs/msg/String
-    - name: anomaly_map
-      ros2_type: sensor_msgs/msg/Image
-
+  inputs: []
+  outputs: []
 hardware:
   platforms:
-    - name: linux_x86
-      backends: [onnxruntime_cuda, onnxruntime_cpu]
-    - name: darwin_arm64
-      backends: [onnxruntime_cpu, mlx]
-
+  - name: x86_64
+    backends:
+    - cuda
 performance:
   profiles:
-    - platform: linux_x86
-      model: fenris-aayolo
-      backend: onnxruntime_cuda
-      fps: 30
-      latency_p50_ms: 33
-
+  - platform: x86_64_cuda
+    model: fenris-v1
+    backend: cuda
+    fps: 10
+    latency_p50_ms: 100
 safety:
   failure_mode: returns_empty
-  timeout_ms: 500
+  timeout_ms: 5000
   health_topic: /anima/fenris/health
-
 composability:
   pairs_well_with: []
   conflicts_with: []
-
 container:
   image: ghcr.io/robotflow-labs/anima-fenris:0.1.0
   port: 8080

checkpoints/best.pth

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+oid sha256:633c88da1177b276b24229d2515b85f03938f3567397b87743244b86dd07603e
+size 25009139

configs/base.yaml

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+project:
+  name: "fenris"
+  version: "0.1.0"
+  paper: "AA-YOLO: Anomaly-Aware YOLO for Infrared Small Target Detection"
+
+paths:
+  data_root: "data"
+  outputs_root: "outputs"
+  weights_root: "weights"
+  shared_datasets_root: "/mnt/forge-data/datasets"
+  seed: 42
+
+defaults:
+  model: "configs/model/aa_yolov7t.yaml"
+  train: "configs/train/default.yaml"
+  deploy: "configs/deploy/jetson.yaml"

configs/cuda_server.yaml

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+training:
+  device: "cuda"
+  precision: "fp16"
+  batch_size: 8
+  num_workers: 4
+  gradient_clip_norm: 10.0
+  checkpoint_interval: 1
+  val_interval: 1
+  log_interval: 10

configs/training.yaml

@@ -0,0 +1,31 @@
+training:
+  dataset: "sirst"
+  resolution: 256
+  optimizer: "sgd"
+  lr: 0.01
+  momentum: 0.937
+  weight_decay: 0.0005
+  scheduler: "cosine"
+  warmup_epochs: 3
+  epochs: 100
+  batch_size: 16
+  freeze_backbone_epochs: 10
+  precision: "fp16"
+  device: "auto"
+  gradient_clip_norm: 10.0
+  num_workers: 4
+  checkpoint_interval: 1
+  val_interval: 1
+  log_interval: 10
+  pretrain: "coco"
+  augmentation:
+    mosaic: true
+    mixup: 0.0
+    hsv_h: 0.015
+    hsv_s: 0.0
+    hsv_v: 0.4
+    flip_lr: 0.5
+  frugal_learning:
+    enabled: false
+    num_images: 25
+    strategy: "diverse"

logs/training_history.json

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