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43 lines
1.4 KiB
Markdown
43 lines
1.4 KiB
Markdown
# vesselDetection
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Ship detection using YOLO for the course Digital Processing of Image (Дигитално Процесирање на Слика).
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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.
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## Files that matter
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- `prepare.py` - fixed utilities for dataset checks, runtime overrides, and metric extraction
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- `train.py` - the single training file the agent edits
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- `program.md` - instructions for the research agent
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## Metric
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The primary objective is `metrics/mAP50-95(B)` from Ultralytics validation results. Higher is better.
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## Setup
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Install dependencies with `uv`, make sure the dataset YAML exists at `ships-aerial-images/data.yaml`, then run:
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```bash
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uv sync
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```
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## Training
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Run the baseline or any experiment with:
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```bash
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uv run train.py
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```
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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.
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## Autoresearch loop
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1. Create a fresh branch such as `autoresearch/mar24`
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2. Read `program.md`
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3. Run a baseline with `uv run train.py > run.log 2>&1`
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4. Iterate only on `train.py`
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5. Log outcomes to `results.tsv`
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6. Keep only commits that improve `metrics/mAP50-95(B)`
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