Skyfusion Flood Reg

--- language: - en tags: - object-detection - aerial-imagery - computer-vision - pytorch datasets: - kailaspsudheer/tiny-object-detection --- # PiattiDetector — Aerial Object Detection (SkyFusion) Lightweight anchor-free object detector for aerial imagery. Detects **aircraft, ships and vehicles** using a CenterNet-inspired approach with heatmap + size + offset regression heads. Developed as part of a Machine Learning course (Sapienza, January 2026). Code: [github.com/JavideuS/aerial-vision](https://github.com/JavideuS/aerial-vision) · `regression/` ## Architecture Custom ResNet backbone (3 residual blocks, 8× downsampling) + three detection heads: | Head | Output | Purpose | |---|---|---| | Heatmap | `[B, C, H/8, W/8]` | Object center confidence per class | | Size | `[B, 2, H/8, W/8]` | Bounding box width and height | | Offset | `[B, 2, H/8, W/8]` | Sub-pixel center refinement | ~2.1M parameters. Input: 512×512 px. ## Dataset [SkyFusion / Tiny Object Detection](https://www.kaggle.com/datasets/kailaspsudheer/tiny-object-detection) — 3000 aerial images, 640×640 px, COCO format. Classes: **Aircraft**, **Ship**, **Vehicle**. ## Usage ```python from huggingface_hub import hf_hub_download import torch from modelv2 import PiattiObjectDetector # clone the repo first ckpt_path = hf_hub_download( repo_id="JavideuS/skyfusion-flood-reg", filename="PiattiDetector-2111k.pth" ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = PiattiObjectDetector(num_classes=3).to(device) ckpt = torch.load(ckpt_path, map_location=device) model.load_state_dict(ckpt['model_state_dict']) model.eval() ``` For full inference with bounding box decoding and visualization see `main.py`. ## Limitations - Trained on a small dataset (3000 images); may not generalise to aerial imagery with significantly different sensors or altitudes - No NMS post-processing in the provided inference script

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