Evaluation

.gitignore

@@ -0,0 +1,4 @@
+.venv/
+__pycache__/
+data_eval/
+data_results/

Makefile

@@ -1,4 +1,4 @@
-.PHONY: help docker-build docker-run local-install local-run local-test local-test-integration eval-photo-v1 eval-dance-v1
+.PHONY: help docker-build docker-run local-install local-run local-test local-test-integration eval-photo-v1 eval-dance-v1 eval-photo-matrix eval-dance-matrix data-photos data-dance
 
 help:
 	@echo "---------------------------------------------------"
@@ -9,8 +9,12 @@ help:
 	@echo "  local-run               Run API locally on :7860"
 	@echo "  local-test              Run unit tests"
 	@echo "  local-test-integration  Run integration tests"
-	@echo "  eval-photo-v1           Run eval on personal-photos-lite-v1"
-	@echo "  eval-dance-v1           Run eval on scene-dance-formation-group-v1"
+	@echo "  eval-photo              Run eval on personal-photos-lite-v1"
+	@echo "  eval-dance              Run eval on scene-dance-formation-group-v1"
+	@echo "  eval-photo-matrix       Run eval for all personal-photos label sets"
+	@echo "  eval-dance-matrix       Run eval for all dance label sets"
+	@echo "  data-photos             Download + normalize photo eval dataset"
+	@echo "  data-dance              Download + normalize dance eval dataset"
 
 docker-build:
 	docker build -t photo-classification .
@@ -30,14 +34,34 @@ local-test:
 local-test-integration:
 	uv run pytest -q -m integration
 
-eval-photo-v1:
+eval-photo:
 	uv run python scripts/classify_dataset.py \
 		--label-set label-dataset/personal-photos-lite-v1.json \
-		--images label-dataset \
-		--csv results/personal-photos-lite-v1.csv
+		--images data_eval/photos/normalized \
+		--csv data_results/personal-photos-lite-v1.csv
 
-eval-dance-v1:
+eval-dance:
 	uv run python scripts/classify_dataset.py \
 		--label-set label-dataset/scene-dance-formation-group-v1.json \
-		--images label-dataset \
-		--csv results/scene-dance-formation-group-v1.csv
+		--images data_eval/dance/normalized \
+		--csv data_results/scene-dance-formation-group-v1.csv
+
+eval-photo-matrix:
+	uv run python scripts/eval_matrix.py \
+		--label-sets "label-dataset/personal-photos-*.json" \
+		--images data_eval/photos/normalized \
+		--out-dir data_results \
+		--summary-csv data_results/personal-photos-summary.csv
+
+eval-dance-matrix:
+	uv run python scripts/eval_matrix.py \
+		--label-sets "label-dataset/scene-dance-*.json" \
+		--images data_eval/dance/normalized \
+		--out-dir data_results \
+		--summary-csv data_results/dance-summary.csv
+
+data-photos:
+	uv run python scripts/dataset_prep.py --out data_eval --target photos --n 50 --normalize
+
+data-dance:
+	uv run python scripts/dataset_prep.py --out data_eval --target dance --n 50 --normalize

README.md

@@ -161,13 +161,58 @@ local images and capture timings:
 uv run python scripts/classify_dataset.py \
   --label-set label-dataset/personal-photos-lite-v1.json \
   --images /path/to/images \
-  --csv results/summary.csv
+  --csv data_results/summary.csv
 ```
 
 Makefile shortcuts:
 
 - `make eval-photo-v1`
 - `make eval-dance-v1`
+- `make eval-photo-matrix`
+- `make eval-dance-matrix`
+
+Matrix eval (multiple label sets against the same images):
+
+```bash
+uv run python scripts/eval_matrix.py \
+  --label-sets "label-dataset/personal-photos-*.json" \
+  --images data_eval/photos/normalized \
+  --out-dir data_results \
+  --summary-csv data_results/personal-photos-summary.csv
+```
+
+## Eval datasets (download schema)
+
+We use a simple, reproducible layout for evaluation datasets created by `scripts/dataset_prep.py`:
+
+```
+data_eval/
+  photos/
+    raw/         # downloaded originals
+    normalized/  # normalized JPEGs
+  dance/
+    raw/         # downloaded originals
+    normalized/  # normalized JPEGs
+```
+
+Download and normalize (recommended):
+
+```bash
+uv run python scripts/dataset_prep.py --out data_eval --target photos --n 50 --normalize
+uv run python scripts/dataset_prep.py --out data_eval --target dance --n 50 --normalize
+```
+
+Reset existing files and start fresh:
+
+```bash
+uv run python scripts/dataset_prep.py --out data_eval --target photos --n 50 --normalize --reset
+```
+
+Normalize your own folder into the same schema:
+
+```bash
+uv run python scripts/dataset_prep.py --normalize-only --in-dir /path/to/images --out data_eval/photos
+```
 
 ## Project layout
 

pyproject.toml

@@ -20,8 +20,11 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
   "click==8.1.7",
+  "datasets==2.21.0",
   "httpx==0.27.2",
   "pytest==8.3.2",
+  "requests==2.32.3",
+  "tqdm==4.66.5",
 ]
 
 [tool.setuptools]

scripts/classify_dataset.py

@@ -5,6 +5,7 @@ import base64
 import csv
 import json
 from dataclasses import dataclass
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Iterable
 
@@ -21,7 +22,9 @@ class Config:
     top_k: int
     activate: bool
     limit: int
+    out_dir: Path
     csv_path: Path | None
+    summary: bool
 
 
 def iter_images(paths: list[Path]) -> Iterable[Path]:
@@ -63,11 +66,15 @@ def classify_one(client: httpx.Client, label_set_hash: str, image: Path, domain_
 
 
 def to_row(image: Path, data: dict) -> dict[str, str]:
+    domain_hits = data.get("domain_hits", [])
+    label_hits = data.get("label_hits", [])
     return {
         "image": str(image),
         "label_set_hash": data.get("label_set_hash", ""),
         "model_id": data.get("model_id", ""),
         "chosen_domains": "|".join(data.get("chosen_domains", [])),
+        "domain_hits": "|".join(f"{d.get('id')}:{d.get('score'):.4f}" for d in domain_hits),
+        "label_hits": "|".join(f"{l.get('id')}:{l.get('score'):.4f}" for l in label_hits),
         "elapsed_ms": str(data.get("elapsed_ms", "")),
         "elapsed_domain_ms": str(data.get("elapsed_domain_ms", "")),
         "elapsed_labels_ms": str(data.get("elapsed_labels_ms", "")),
@@ -79,18 +86,54 @@ def write_csv(path: Path, rows: list[dict[str, str]]) -> None:
     with path.open("w", newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(
             f,
-            fieldnames=[
-                "image",
-                "label_set_hash",
-                "model_id",
-                "chosen_domains",
-                "elapsed_ms",
-                "elapsed_domain_ms",
-                "elapsed_labels_ms",
-            ],
-        )
+                fieldnames=[
+                    "image",
+                    "label_set_hash",
+                    "model_id",
+                    "chosen_domains",
+                    "domain_hits",
+                    "label_hits",
+                    "elapsed_ms",
+                    "elapsed_domain_ms",
+                    "elapsed_labels_ms",
+                ],
+            )
+            writer.writeheader()
+            writer.writerows(rows)
+
+
+def _percentile(values: list[int], q: float) -> int:
+    if not values:
+        return 0
+    values = sorted(values)
+    idx = int(round((len(values) - 1) * q))
+    return values[idx]
+
+
+def write_summary(path: Path, rows: list[dict[str, str]]) -> None:
+    times: list[int] = []
+    for row in rows:
+        try:
+            times.append(int(row["elapsed_ms"]))
+        except Exception:
+            continue
+    summary = {
+        "count": str(len(times)),
+        "avg_elapsed_ms": str(int(sum(times) / max(1, len(times)))),
+        "p50_elapsed_ms": str(_percentile(times, 0.50)),
+        "p90_elapsed_ms": str(_percentile(times, 0.90)),
+        "p95_elapsed_ms": str(_percentile(times, 0.95)),
+        "p99_elapsed_ms": str(_percentile(times, 0.99)),
+    }
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=list(summary.keys()))
         writer.writeheader()
-        writer.writerows(rows)
+        writer.writerow(summary)
+
+
+def _timestamp() -> str:
+    return datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
 
 
 def run(cfg: Config) -> int:
@@ -113,6 +156,15 @@ def run(cfg: Config) -> int:
 
     if cfg.csv_path and rows:
         write_csv(cfg.csv_path, rows)
+    elif rows:
+        ts = _timestamp()
+        out_path = cfg.out_dir / f"{cfg.label_set.stem}_{ts}.csv"
+        write_csv(out_path, rows)
+
+    if cfg.summary:
+        ts = _timestamp()
+        summary_path = cfg.out_dir / f"{cfg.label_set.stem}_summary_{ts}.csv"
+        write_summary(summary_path, rows)
 
     return 0
 
@@ -125,7 +177,9 @@ def run(cfg: Config) -> int:
 @click.option("--top-k", default=5, show_default=True, type=int)
 @click.option("--activate", is_flag=True, default=False)
 @click.option("--limit", default=0, show_default=True, type=int)
+@click.option("--out-dir", default="data_results", show_default=True, type=click.Path(path_type=Path))
 @click.option("--csv", "csv_path", type=click.Path(path_type=Path))
+@click.option("--summary", is_flag=True, default=False)
 def cli(
     api: str,
     label_set: Path,
@@ -134,7 +188,9 @@ def cli(
     top_k: int,
     activate: bool,
     limit: int,
+    out_dir: Path,
     csv_path: Path | None,
+    summary: bool,
 ) -> None:
     cfg = Config(
         api=api,
@@ -144,7 +200,9 @@ def cli(
         top_k=top_k,
         activate=activate,
         limit=limit,
+        out_dir=out_dir,
         csv_path=csv_path,
+        summary=summary,
     )
     raise SystemExit(run(cfg))
 

scripts/dataset_prep.py

@@ -0,0 +1,280 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import io
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, Optional
+
+import click
+import requests
+from PIL import Image, ImageOps
+from tqdm import tqdm
+
+
+@dataclass(frozen=True)
+class Config:
+    out_dir: Path
+    target: str
+    n: int
+    seed: int
+    normalize: bool
+    max_side: int
+    jpeg_quality: int
+    normalize_only: bool
+    in_dir: Optional[Path]
+    reset: bool
+
+
+def ensure_dir(path: Path) -> None:
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def normalize_image_bytes(img_bytes: bytes, max_side: int, jpeg_quality: int) -> bytes:
+    with Image.open(io.BytesIO(img_bytes)) as im:
+        im = ImageOps.exif_transpose(im)
+        im = im.convert("RGB")
+
+        w, h = im.size
+        scale = max_side / float(max(w, h))
+        if scale < 1.0:
+            new_w = max(1, int(round(w * scale)))
+            new_h = max(1, int(round(h * scale)))
+            im = im.resize((new_w, new_h), Image.Resampling.LANCZOS)
+
+        out = io.BytesIO()
+        im.save(out, format="JPEG", quality=jpeg_quality, optimize=True, progressive=True)
+        return out.getvalue()
+
+
+def download_url(url: str, timeout_s: float = 20.0) -> Optional[bytes]:
+    try:
+        resp = requests.get(url, timeout=timeout_s)
+        resp.raise_for_status()
+        return resp.content
+    except Exception:
+        return None
+
+
+def save_bytes(path: Path, data: bytes) -> None:
+    ensure_dir(path.parent)
+    path.write_bytes(data)
+
+
+def iter_images(paths: Iterable[Path]) -> Iterable[Path]:
+    exts = {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".tif", ".tiff"}
+    for path in paths:
+        if path.is_dir():
+            for p in sorted(path.rglob("*")):
+                if p.is_file() and p.suffix.lower() in exts:
+                    yield p
+        elif path.is_file() and path.suffix.lower() in exts:
+            yield path
+
+
+def normalize_existing(in_dir: Path, out_dir: Path, max_side: int, jpeg_quality: int) -> None:
+    files = list(iter_images([in_dir]))
+    ensure_dir(out_dir)
+
+    for p in tqdm(files, desc=f"Normalizing {in_dir.name}"):
+        try:
+            raw = p.read_bytes()
+            norm = normalize_image_bytes(raw, max_side=max_side, jpeg_quality=jpeg_quality)
+            rel = p.relative_to(in_dir)
+            out_path = (out_dir / rel).with_suffix(".jpg")
+            save_bytes(out_path, norm)
+        except Exception:
+            continue
+
+    print(f"Normalized images written to: {out_dir}")
+
+
+def _next_index(dir_path: Path, prefix: str) -> int:
+    if not dir_path.exists():
+        return 0
+    max_idx = -1
+    for p in dir_path.glob(f"{prefix}_*.bin"):
+        stem = p.stem
+        try:
+            idx = int(stem.split("_")[-1])
+        except ValueError:
+            continue
+        max_idx = max(max_idx, idx)
+    return max_idx + 1
+
+
+def download_photos_open_images(
+    out_dir: Path,
+    n: int,
+    seed: int,
+    normalize: bool,
+    max_side: int,
+    jpeg_quality: int,
+    reset: bool,
+) -> None:
+    from datasets import load_dataset
+
+    ds = load_dataset("bitmind/open-images-v7", split="train", streaming=True)
+
+    saved = 0
+
+    raw_dir = out_dir / "photos" / "raw"
+    norm_dir = out_dir / "photos" / "normalized"
+    if reset:
+        for p in raw_dir.glob("openimages_*.bin"):
+            p.unlink()
+        for p in norm_dir.glob("openimages_*.jpg"):
+            p.unlink()
+    start_idx = _next_index(raw_dir, "openimages")
+
+    for row in tqdm(ds, desc="Streaming Open Images V7"):
+        if saved >= n:
+            break
+        url = row.get("image_url") or row.get("url") or row.get("imageUrl") or row.get("ImageURL")
+        if not url:
+            continue
+
+        img = download_url(url)
+        if not img:
+            continue
+
+        idx = start_idx + saved
+        raw_name = f"openimages_{idx:06d}.bin"
+        save_bytes(raw_dir / raw_name, img)
+
+        if normalize:
+            norm = normalize_image_bytes(img, max_side=max_side, jpeg_quality=jpeg_quality)
+            norm_name = f"openimages_{idx:06d}.jpg"
+            save_bytes(norm_dir / norm_name, norm)
+        saved += 1
+
+    print(f"Saved {saved} images to: {raw_dir}")
+    if normalize:
+        print(f"Normalized images written to: {norm_dir}")
+
+
+def download_dance_x_dance(
+    out_dir: Path,
+    n: int,
+    seed: int,
+    normalize: bool,
+    max_side: int,
+    jpeg_quality: int,
+    reset: bool,
+) -> None:
+    from datasets import load_dataset
+
+    ds = load_dataset("MCG-NJU/X-Dance", split="train", streaming=True)
+
+    raw_dir = out_dir / "dance" / "raw"
+    norm_dir = out_dir / "dance" / "normalized"
+    if reset:
+        for p in raw_dir.glob("xdance_*.bin"):
+            p.unlink()
+        for p in norm_dir.glob("xdance_*.jpg"):
+            p.unlink()
+    start_idx = _next_index(raw_dir, "xdance")
+
+    saved = 0
+    for row in tqdm(ds, desc="Streaming X-Dance"):
+        if saved >= n:
+            break
+        img_obj = row.get("image")
+        img_bytes: Optional[bytes] = None
+        if img_obj is not None and hasattr(img_obj, "convert"):
+            out = io.BytesIO()
+            img_obj.convert("RGB").save(out, format="PNG")
+            img_bytes = out.getvalue()
+        else:
+            url = row.get("image_url") or row.get("url")
+            if url:
+                img_bytes = download_url(url)
+
+        if not img_bytes:
+            continue
+
+        idx = start_idx + saved
+        raw_name = f"xdance_{idx:06d}.bin"
+        save_bytes(raw_dir / raw_name, img_bytes)
+
+        if normalize:
+            norm = normalize_image_bytes(img_bytes, max_side=max_side, jpeg_quality=jpeg_quality)
+            norm_name = f"xdance_{idx:06d}.jpg"
+            save_bytes(norm_dir / norm_name, norm)
+        saved += 1
+
+    print(f"Saved {saved} images to: {raw_dir}")
+    if normalize:
+        print(f"Normalized images written to: {norm_dir}")
+
+
+def run(cfg: Config) -> None:
+    if cfg.normalize_only:
+        if not cfg.in_dir:
+            raise SystemExit("--in-dir is required with --normalize-only")
+        normalize_existing(cfg.in_dir, cfg.out_dir, cfg.max_side, cfg.jpeg_quality)
+        return
+
+    ensure_dir(cfg.out_dir)
+    if cfg.target == "photos":
+        download_photos_open_images(
+            out_dir=cfg.out_dir,
+            n=cfg.n,
+            seed=cfg.seed,
+            normalize=cfg.normalize,
+            max_side=cfg.max_side,
+            jpeg_quality=cfg.jpeg_quality,
+            reset=cfg.reset,
+        )
+    else:
+        download_dance_x_dance(
+            out_dir=cfg.out_dir,
+            n=cfg.n,
+            seed=cfg.seed,
+            normalize=cfg.normalize,
+            max_side=cfg.max_side,
+            jpeg_quality=cfg.jpeg_quality,
+            reset=cfg.reset,
+        )
+
+
+@click.command()
+@click.option("--out", "out_dir", required=True, type=click.Path(path_type=Path))
+@click.option("--target", type=click.Choice(["photos", "dance"], case_sensitive=False), required=True)
+@click.option("--n", default=500, show_default=True, type=int)
+@click.option("--seed", default=0, show_default=True, type=int)
+@click.option("--normalize", is_flag=True, default=False)
+@click.option("--max-side", default=512, show_default=True, type=int)
+@click.option("--jpeg-quality", default=92, show_default=True, type=int)
+@click.option("--normalize-only", is_flag=True, default=False)
+@click.option("--in-dir", type=click.Path(path_type=Path))
+@click.option("--reset", is_flag=True, default=False, help="Delete existing raw/normalized files before download.")
+def cli(
+    out_dir: Path,
+    target: str,
+    n: int,
+    seed: int,
+    normalize: bool,
+    max_side: int,
+    jpeg_quality: int,
+    normalize_only: bool,
+    in_dir: Optional[Path],
+    reset: bool,
+) -> None:
+    cfg = Config(
+        out_dir=out_dir,
+        target=target,
+        n=n,
+        seed=seed,
+        normalize=normalize,
+        max_side=max_side,
+        jpeg_quality=jpeg_quality,
+        normalize_only=normalize_only,
+        in_dir=in_dir,
+        reset=reset,
+    )
+    run(cfg)
+
+
+if __name__ == "__main__":
+    cli()

scripts/eval_matrix.py

@@ -0,0 +1,187 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import csv
+import json
+from dataclasses import dataclass
+import base64
+from pathlib import Path
+from typing import Iterable
+
+import click
+import httpx
+
+
+@dataclass(frozen=True)
+class Config:
+    api: str
+    label_sets: list[Path]
+    images: list[Path]
+    domain_top_n: int
+    top_k: int
+    out_dir: Path
+    summary_csv: Path | None
+
+
+def iter_images(paths: Iterable[Path]) -> Iterable[Path]:
+    exts = {".jpg", ".jpeg", ".png", ".webp"}
+    for path in paths:
+        if path.is_dir():
+            for p in sorted(path.rglob("*")):
+                if p.is_file() and p.suffix.lower() in exts:
+                    yield p
+        elif path.is_file() and path.suffix.lower() in exts:
+            yield path
+
+
+def expand_label_sets(paths: Iterable[str]) -> list[Path]:
+    out: list[Path] = []
+    for raw in paths:
+        p = Path(raw)
+        if any(ch in raw for ch in ["*", "?", "["]):
+            out.extend(sorted(Path().glob(raw)))
+        else:
+            out.append(p)
+    return [p for p in out if p.is_file()]
+
+
+def upload_label_set(client: httpx.Client, label_set: Path) -> str:
+    payload = json.loads(label_set.read_text())
+    r = client.post("/api/v1/label-sets", json=payload)
+    r.raise_for_status()
+    return r.json()["label_set_hash"]
+
+
+def classify_one(
+    client: httpx.Client,
+    label_set_hash: str,
+    image_b64: str,
+    domain_top_n: int,
+    top_k: int,
+) -> dict:
+    payload = {
+        "image_base64": image_b64,
+        "domain_top_n": domain_top_n,
+        "top_k": top_k,
+    }
+    r = client.post(f"/api/v1/classify?label_set_hash={label_set_hash}", json=payload)
+    r.raise_for_status()
+    return r.json()
+
+
+def to_row(label_set: Path, image: Path, data: dict) -> dict[str, str]:
+    return {
+        "label_set": label_set.name,
+        "image": str(image),
+        "label_set_hash": data.get("label_set_hash", ""),
+        "model_id": data.get("model_id", ""),
+        "chosen_domains": "|".join(data.get("chosen_domains", [])),
+        "elapsed_ms": str(data.get("elapsed_ms", "")),
+        "elapsed_domain_ms": str(data.get("elapsed_domain_ms", "")),
+        "elapsed_labels_ms": str(data.get("elapsed_labels_ms", "")),
+    }
+
+
+def write_csv(path: Path, rows: list[dict[str, str]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=[
+                "label_set",
+                "image",
+                "label_set_hash",
+                "model_id",
+                "chosen_domains",
+                "elapsed_ms",
+                "elapsed_domain_ms",
+                "elapsed_labels_ms",
+            ],
+        )
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def write_summary(path: Path, rows: list[dict[str, str]]) -> None:
+    summary: dict[str, list[int]] = {}
+    for row in rows:
+        label = row["label_set"]
+        try:
+            elapsed = int(row["elapsed_ms"])
+        except Exception:
+            continue
+        summary.setdefault(label, []).append(elapsed)
+
+    out_rows: list[dict[str, str]] = []
+    for label, times in summary.items():
+        avg = int(sum(times) / max(1, len(times)))
+        out_rows.append({"label_set": label, "count": str(len(times)), "avg_elapsed_ms": str(avg)})
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=["label_set", "count", "avg_elapsed_ms"])
+        writer.writeheader()
+        writer.writerows(out_rows)
+
+
+def run(cfg: Config) -> None:
+    images = list(iter_images(cfg.images))
+    if not images:
+        raise SystemExit("No images found.")
+    if not cfg.label_sets:
+        raise SystemExit("No label sets found.")
+
+    rows: list[dict[str, str]] = []
+    with httpx.Client(base_url=cfg.api, timeout=30) as client:
+        for label_set in cfg.label_sets:
+            label_set_hash = upload_label_set(client, label_set)
+            for image in images:
+                image_b64 = image.read_bytes()
+                data = classify_one(
+                    client,
+                    label_set_hash,
+                    image_b64=base64.b64encode(image_b64).decode("utf-8"),
+                    domain_top_n=cfg.domain_top_n,
+                    top_k=cfg.top_k,
+                )
+                print(json.dumps({"label_set": label_set.name, "image": str(image), "result": data}))
+                rows.append(to_row(label_set, image, data))
+
+    out_path = cfg.out_dir / "eval_matrix.csv"
+    write_csv(out_path, rows)
+    if cfg.summary_csv:
+        write_summary(cfg.summary_csv, rows)
+
+
+@click.command()
+@click.option("--api", default="http://localhost:7860", show_default=True)
+@click.option("--label-sets", "label_sets_raw", multiple=True, required=True)
+@click.option("--images", multiple=True, required=True, type=click.Path(path_type=Path))
+@click.option("--domain-top-n", default=2, show_default=True, type=int)
+@click.option("--top-k", default=5, show_default=True, type=int)
+@click.option("--out-dir", default="data_results", show_default=True, type=click.Path(path_type=Path))
+@click.option("--summary-csv", type=click.Path(path_type=Path))
+def cli(
+    api: str,
+    label_sets_raw: tuple[str, ...],
+    images: tuple[Path, ...],
+    domain_top_n: int,
+    top_k: int,
+    out_dir: Path,
+    summary_csv: Path | None,
+) -> None:
+    label_sets = expand_label_sets(label_sets_raw)
+    cfg = Config(
+        api=api,
+        label_sets=label_sets,
+        images=list(images),
+        domain_top_n=domain_top_n,
+        top_k=top_k,
+        out_dir=out_dir,
+        summary_csv=summary_csv,
+    )
+    run(cfg)
+
+
+if __name__ == "__main__":
+    cli()

src/photo_classification.egg-info/PKG-INFO

@@ -11,8 +11,12 @@ Requires-Dist: torch==2.3.1
 Requires-Dist: transformers==4.44.2
 Requires-Dist: uvicorn[standard]==0.30.6
 Provides-Extra: dev
+Requires-Dist: click==8.1.7; extra == "dev"
+Requires-Dist: datasets==2.21.0; extra == "dev"
 Requires-Dist: httpx==0.27.2; extra == "dev"
 Requires-Dist: pytest==8.3.2; extra == "dev"
+Requires-Dist: requests==2.32.3; extra == "dev"
+Requires-Dist: tqdm==4.66.5; extra == "dev"
 
 ---
 title: Photo Classification API
@@ -168,6 +172,46 @@ Error handling and HTTP boundaries:
   - `pytest -q`
   - `pytest -q -m integration`
 
+## Eval scripts
+
+Use the lightweight evaluator in `scripts/classify_dataset.py` to run a label set against
+local images and capture timings:
+
+```bash
+uv run python scripts/classify_dataset.py \
+  --label-set label-dataset/personal-photos-lite-v1.json \
+  --images /path/to/images \
+  --csv results/summary.csv
+```
+
+Makefile shortcuts:
+
+- `make eval-photo-v1`
+- `make eval-dance-v1`
+
+## Eval datasets (download schema)
+
+We use a simple, reproducible layout for evaluation datasets created by `scripts/dataset_prep.py`:
+
+```
+data_eval/
+  photos/   # normalized images from Open Images V7 sample
+  dance/    # normalized images from X-Dance sample
+```
+
+Download and normalize (recommended):
+
+```bash
+uv run python scripts/dataset_prep.py --out data_eval --target photos --n 800 --normalize
+uv run python scripts/dataset_prep.py --out data_eval --target dance --n 800 --normalize
+```
+
+Normalize your own folder into the same schema:
+
+```bash
+uv run python scripts/dataset_prep.py --normalize-only --in-dir /path/to/images --out data_eval/photos
+```
+
 ## Project layout
 
 ```

src/photo_classification.egg-info/requires.txt

@@ -6,5 +6,9 @@ transformers==4.44.2
 uvicorn[standard]==0.30.6
 
 [dev]
+click==8.1.7
+datasets==2.21.0
 httpx==0.27.2
 pytest==8.3.2
+requests==2.32.3
+tqdm==4.66.5

uv.lock

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