Autoresearch implementation for testing

README.md

@@ -1,4 +1,42 @@
 # vesselDetection
-### Ship detection using Machine Learning methods for the course Digital Processing of Image(Дигитално Процесирање на Слика)
 
-This is a simple ship detection model, made using pre-trained YOLO with COCO weights.
+Ship detection using YOLO for the course Digital Processing of Image (Дигитално Процесирање на Слика).
+
+This repo now includes a lightweight `autoresearch`-style workflow adapted from `karpathy/autoresearch`: the idea is to let an AI agent iterate on `train.py`, run short fixed-budget experiments, and keep only changes that improve validation quality.
+
+## Files that matter
+
+- `prepare.py` - fixed utilities for dataset checks, runtime overrides, and metric extraction
+- `train.py` - the single training file the agent edits
+- `program.md` - instructions for the research agent
+
+## Metric
+
+The primary objective is `metrics/mAP50-95(B)` from Ultralytics validation results. Higher is better.
+
+## Setup
+
+Install dependencies with `uv`, make sure the dataset YAML exists at `ships-aerial-images/data.yaml`, then run:
+
+```bash
+uv sync
+```
+
+## Training
+
+Run the baseline or any experiment with:
+
+```bash
+uv run train.py
+```
+
+By default, the training script uses a fixed 5-minute budget through the Ultralytics `time` argument and prints a compact summary at the end so an agent can compare runs automatically.
+
+## Autoresearch loop
+
+1. Create a fresh branch such as `autoresearch/mar24`
+2. Read `program.md`
+3. Run a baseline with `uv run train.py > run.log 2>&1`
+4. Iterate only on `train.py`
+5. Log outcomes to `results.tsv`
+6. Keep only commits that improve `metrics/mAP50-95(B)`