RefDiffNet: A11_CA prebackbone demo (clean history, no images in git)

.gitattributes

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+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+*.pyc
+.gradio/
+*.log
+.DS_Store
+# Full YOLO checkpoint optional locally; Space ships prebackbone_a11_ca.pt only
+weights/best.pt
+examples/

README.md

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+---
+title: RefDiffNet
+emoji: 🔬
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: "4.44.0"
+app_file: app.py
+pinned: false
+license: agpl-3.0
+---
+
+# RefDiffNet — PCB Reference–Defect Enrichment (A11_CA)
+
+**Space:** [vinayedula/RefDiffNet](https://huggingface.co/spaces/vinayedula/RefDiffNet)
+
+Gradio demo for the **A11_CA** prebackbone trained with YOLO12n on HR-IPCB.
+
+- **Inputs:** defect PCB image + golden reference image  
+- **Output:** enriched image (`enriched = defect + α · gate · delta`)
+
+Weights: `weights/prebackbone_a11_ca.pt` (~few MB, prebackbone only — not full `best.pt`)
+
+## Deploy to Hugging Face Spaces
+
+### 1. Prepare the Space folder locally
+
+From the VYOLO repo root:
+
+```bash
+cd /mnt/data/vinay/work/VYOLO
+bash hf_prebackbone_demo/prepare_space.sh
+```
+
+This will:
+
+- Copy the custom **Ultralytics** fork into `hf_prebackbone_demo/vendor/`
+- Extract `prebackbone_a11_ca.pt` from `best.pt` (small file for the Space)
+- Copy example image pairs into `hf_prebackbone_demo/examples/`
+
+### 2. Create a new Space on Hugging Face
+
+1. Go to [huggingface.co/new-space](https://huggingface.co/new-space)
+2. Choose **Gradio** SDK
+3. Clone the empty Space repo locally
+
+### 3. Push to [vinayedula/RefDiffNet](https://huggingface.co/spaces/vinayedula/RefDiffNet)
+
+```bash
+cd hf_prebackbone_demo
+bash deploy_to_refdiffnet.sh
+```
+
+Or manually:
+
+```bash
+git clone https://huggingface.co/spaces/vinayedula/RefDiffNet
+cd RefDiffNet
+# copy app.py, vendor/, weights/prebackbone_a11_ca.pt, examples/, etc.
+git add . && git commit -m "RefDiffNet demo" && git push
+```
+
+> **Note:** `prebackbone_a11_ca.pt` is only a few MB (no LFS needed). Full `best.pt` is optional.
+
+### 4. Space settings (recommended)
+
+| Setting | Value |
+|---------|--------|
+| Hardware | CPU Basic (works) or **GPU** for faster loads |
+| Secrets | Optional: `HF_TOKEN` if weights are in a private model repo |
+
+### Alternative: host weights on the Hub
+
+Upload `prebackbone_a11_ca.pt` to a model repo, then set:
+
+```
+HF_MODEL_REPO=YOUR_USER/YOUR_MODEL
+```
+
+The app downloads `prebackbone_a11_ca.pt` from that repo.
+
+## Run locally
+
+```bash
+cd hf_prebackbone_demo
+pip install -r requirements.txt
+pip install -e ../ultralytics   # custom fork with A11_CA
+python extract_prebackbone_weights.py --ckpt /path/to/best.pt
+export PREBACKBONE_ONLY_WEIGHTS=weights/prebackbone_a11_ca.pt
+python app.py
+```
+
+Open http://localhost:7860
+
+## Environment variables
+
+| Variable | Description |
+|----------|-------------|
+| `PREBACKBONE_ONLY_WEIGHTS` | Path to `prebackbone_a11_ca.pt` |
+| `PREBACKBONE_FULL_CKPT` | Full `best.pt` (only for one-time extraction) |
+| `HF_MODEL_REPO` | Hub repo with `prebackbone_a11_ca.pt` |
+| `PREBACKBONE_IMGSZ` | Letterbox size (default `640`) |
+| `ULTRALYTICS_ROOT` | Path to ultralytics repo if not vendored |
+| `PORT` | Gradio port (default `7860`) |

app.py

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+"""Gradio demo: PCB defect + golden reference -> enriched image (A11_CA prebackbone)."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+from prebackbone_infer import enrich_pair, get_enricher
+
+EXAMPLES_DIR = Path(__file__).resolve().parent / "examples"
+TITLE = "RefDiffNet — PCB Reference–Defect Enrichment"
+DESCRIPTION = """
+Upload a **defect PCB image** and its **golden reference** (same board, no defect).
+The trained A11_CA prebackbone fuses them into an **enriched** image used by YOLO detection.
+
+Formula: `enriched = defect + α · gate · delta`
+"""
+
+
+def _examples() -> list[list[str]]:
+    if not EXAMPLES_DIR.exists():
+        return []
+    inputs = sorted(EXAMPLES_DIR.glob("*_input.jpg"))
+    out: list[list[str]] = []
+    for inp in inputs:
+        ref = EXAMPLES_DIR / inp.name.replace("_input.", "_reference.")
+        if ref.exists():
+            out.append([str(inp), str(ref)])
+    return out
+
+
+def run_demo(defect_img, reference_img):
+    if defect_img is None or reference_img is None:
+        raise gr.Error("Please upload both defect and reference images.")
+
+    defect_rgb, ref_rgb, enriched_rgb = enrich_pair(defect_img, reference_img)
+
+    # Side-by-side comparison for the demo panel
+    h = max(defect_rgb.shape[0], ref_rgb.shape[0], enriched_rgb.shape[0])
+
+    def _pad(im: np.ndarray) -> np.ndarray:
+        if im.shape[0] == h:
+            return im
+        pad = h - im.shape[0]
+        return np.pad(im, ((0, pad), (0, 0), (0, 0)), mode="constant", constant_values=114)
+
+    row = np.concatenate([_pad(defect_rgb), _pad(ref_rgb), _pad(enriched_rgb)], axis=1)
+    return enriched_rgb, row
+
+
+def _preload_model():
+    try:
+        get_enricher()
+    except FileNotFoundError as e:
+        print(f"[warn] Model not loaded yet: {e}")
+
+
+if __name__ == "__main__":
+    _preload_model()
+
+    with gr.Blocks(title=TITLE) as demo:
+        gr.Markdown(f"# {TITLE}")
+        gr.Markdown(DESCRIPTION)
+
+        with gr.Row():
+            defect_in = gr.Image(label="Defect image (input)", type="numpy", image_mode="RGB")
+            ref_in = gr.Image(label="Golden reference", type="numpy", image_mode="RGB")
+
+        run_btn = gr.Button("Generate enriched image", variant="primary")
+        with gr.Row():
+            enriched_out = gr.Image(label="Enriched output", type="numpy", image_mode="RGB")
+            compare_out = gr.Image(
+                label="Defect | Reference | Enriched",
+                type="numpy",
+                image_mode="RGB",
+            )
+
+        run_btn.click(fn=run_demo, inputs=[defect_in, ref_in], outputs=[enriched_out, compare_out])
+
+        ex = _examples()
+        if ex:
+            gr.Examples(examples=ex, inputs=[defect_in, ref_in], label="Example pairs")
+
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=int(os.environ.get("PORT", "7860")),
+        share=os.environ.get("GRADIO_SHARE", "").lower() in {"1", "true", "yes"},
+    )

deploy_to_refdiffnet.sh

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+#!/usr/bin/env bash
+# Push hf_prebackbone_demo to https://huggingface.co/spaces/vinayedula/RefDiffNet
+set -euo pipefail
+cd "$(dirname "$0")"
+
+SPACE_REMOTE="${SPACE_REMOTE:-https://huggingface.co/spaces/vinayedula/RefDiffNet}"
+
+if [[ ! -f weights/prebackbone_a11_ca.pt ]]; then
+  echo "Run: python extract_prebackbone_weights.py --ckpt /path/to/best.pt"
+  exit 1
+fi
+
+git init
+git lfs install 2>/dev/null || true
+
+git add \
+  app.py \
+  prebackbone_infer.py \
+  extract_prebackbone_weights.py \
+  deploy_to_refdiffnet.sh \
+  prepare_space.sh \
+  requirements.txt \
+  setup.sh \
+  README.md \
+  .gitattributes \
+  .gitignore \
+  vendor/ \
+  examples/ \
+  weights/prebackbone_a11_ca.pt
+
+# Commit only if there are staged changes
+if ! git diff --cached --quiet; then
+  git commit -m "RefDiffNet: A11_CA prebackbone enrichment demo"
+elif ! git rev-parse HEAD >/dev/null 2>&1; then
+  echo "ERROR: No commit to push. Check git add / files."
+  exit 1
+else
+  echo "No new changes; pushing existing commit."
+fi
+
+# HF Spaces use 'main' (git init often creates 'master')
+git branch -M main
+
+if git remote get-url origin &>/dev/null; then
+  git remote set-url origin "${SPACE_REMOTE}"
+else
+  git remote add origin "${SPACE_REMOTE}"
+fi
+
+echo "Pushing branch 'main' to ${SPACE_REMOTE}"
+echo "HF token as password: https://huggingface.co/settings/tokens"
+git push -u origin main

extract_prebackbone_weights.py

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+#!/usr/bin/env python3
+"""One-time: extract prebackbone weights from a full YOLO best.pt checkpoint."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+import torch
+
+# Allow running before vendor is installed
+_HERE = Path(__file__).resolve().parent
+for base in (_HERE / "vendor", _HERE.parent / "ultralytics"):
+    if (base / "ultralytics" / "__init__.py").exists():
+        sys.path.insert(0, str(base))
+        break
+
+
+def _clean_state_dict(state: dict) -> dict:
+    """Drop thop profiling keys (total_ops, total_params) not in nn.Module."""
+    skip = ("total_ops", "total_params")
+    return {k: v for k, v in state.items() if not any(k == s or k.endswith(f".{s}") for s in skip)}
+
+
+def extract(
+    full_ckpt: Path,
+    out_path: Path,
+    prebackbone_name: str | None = None,
+) -> Path:
+    from ultralytics.nn.tasks import torch_safe_load
+
+    ckpt, _ = torch_safe_load(str(full_ckpt))
+    train_args = ckpt.get("train_args") or {}
+    name = (prebackbone_name or train_args.get("prebackbone") or "A11_CA").upper()
+    channels = int(train_args.get("channels", 3) or 3)
+
+    model = (ckpt.get("ema") or ckpt.get("model")).float()
+    pb = getattr(model, "prebackbone", None)
+    if pb is None:
+        raise RuntimeError(f"No prebackbone in {full_ckpt}")
+
+    state = _clean_state_dict(pb.state_dict())
+
+    # Save only tensors + metadata (no YOLO backbone/head)
+    payload = {
+        "prebackbone": name,
+        "channels": channels,
+        "state_dict": state,
+        "source_checkpoint": str(full_ckpt.resolve()),
+    }
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(payload, out_path)
+
+    n_params = sum(t.numel() for t in payload["state_dict"].values())
+    size_mb = out_path.stat().st_size / (1024 * 1024)
+    print(f"Saved {out_path} ({size_mb:.2f} MB, {n_params:,} parameters, type={name})")
+    return out_path
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description="Extract prebackbone-only weights from best.pt")
+    p.add_argument(
+        "--ckpt",
+        type=Path,
+        default=_HERE / "weights" / "best.pt",
+        help="Full YOLO checkpoint (best.pt)",
+    )
+    p.add_argument(
+        "--out",
+        type=Path,
+        default=_HERE / "weights" / "prebackbone_a11_ca.pt",
+        help="Output path for prebackbone-only weights",
+    )
+    p.add_argument("--name", type=str, default=None, help="Override prebackbone type (default: from train_args)")
+    args = p.parse_args()
+    if not args.ckpt.exists():
+        raise SystemExit(f"Checkpoint not found: {args.ckpt}")
+    extract(args.ckpt, args.out, args.name)
+
+
+if __name__ == "__main__":
+    main()

fix_hf_binaries.sh

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+#!/usr/bin/env bash
+# Fix HF push rejection: binary files must use Git LFS (images + weights).
+set -euo pipefail
+cd "$(dirname "$0")"
+
+unset GIT_ASKPASS SSH_ASKPASS
+
+echo "==> Installing Git LFS tracking for images and weights"
+git lfs install
+git lfs track "*.jpg" "*.jpeg" "*.png" "*.webp" "*.pt" "*.pth"
+
+echo "==> Removing unneeded vendor binaries (not used by prebackbone demo)"
+rm -rf vendor/ultralytics/assets
+rm -f vendor/ultralytics/*.pt
+
+git rm -rf --cached vendor/ultralytics/assets 2>/dev/null || true
+git rm -f --cached vendor/ultralytics/*.pt 2>/dev/null || true
+
+echo "==> Re-staging images via LFS"
+git rm -r --cached examples 2>/dev/null || true
+git add .gitattributes
+git add examples/
+git add weights/prebackbone_a11_ca.pt
+git add -A
+
+if ! git diff --cached --quiet; then
+  git commit -m "Track binaries with Git LFS; drop unused vendor assets"
+fi
+
+echo "==> Force push (run: FORCE_PUSH=1 bash git_hf_sync.sh)"
+echo "    Or: unset GIT_ASKPASS && git push -u origin main --force"

git_hf_sync.sh

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+#!/usr/bin/env bash
+# Sync to Hugging Face Space — bypasses Cursor's broken GIT_ASKPASS.
+set -euo pipefail
+cd "$(dirname "$0")"
+
+unset GIT_ASKPASS SSH_ASKPASS
+
+SPACE_REMOTE="${SPACE_REMOTE:-https://huggingface.co/spaces/vinayedula/RefDiffNet}"
+USER="${HF_USER:-vinayedula}"
+FORCE="${FORCE_PUSH:-0}"
+
+if ! command -v hf >/dev/null 2>&1; then
+  echo "ERROR: 'hf' not found. Run: pip install -U huggingface_hub"
+  exit 1
+fi
+
+TOKEN="$(hf auth token 2>/dev/null | tr -d '[:space:]')"
+if [[ -z "${TOKEN}" ]]; then
+  echo "ERROR: No HF token. Run: hf auth login"
+  exit 1
+fi
+
+CRED_FILE="${HOME}/.git-credentials"
+LINE="https://${USER}:${TOKEN}@huggingface.co"
+if [[ -f "${CRED_FILE}" ]] && grep -q "huggingface.co" "${CRED_FILE}" 2>/dev/null; then
+  grep -v "huggingface.co" "${CRED_FILE}" > "${CRED_FILE}.tmp" || true
+  mv "${CRED_FILE}.tmp" "${CRED_FILE}"
+fi
+echo "${LINE}" >> "${CRED_FILE}"
+chmod 600 "${CRED_FILE}"
+
+git branch -M main 2>/dev/null || true
+git remote set-url origin "${SPACE_REMOTE}"
+
+# Clean up a stuck rebase from a previous attempt
+if [[ -d .git/rebase-merge ]] || [[ -d .git/rebase-apply ]]; then
+  echo "==> Aborting in-progress rebase..."
+  git rebase --abort 2>/dev/null || true
+fi
+
+git add -A
+if ! git diff --cached --quiet 2>/dev/null; then
+  git commit -m "RefDiffNet demo" || true
+fi
+
+if [[ "${FORCE}" == "1" ]]; then
+  echo "==> Force pushing to ${SPACE_REMOTE}"
+  git push -u origin main --force
+else
+  echo "==> Pulling remote..."
+  if git pull origin main --rebase --allow-unrelated-histories; then
+    echo "==> Pushing to ${SPACE_REMOTE}"
+    git push -u origin main
+  else
+    echo ""
+    echo "Pull/rebase failed (conflicts?). Options:"
+    echo "  1) Resolve conflicts, then: git rebase --continue && git push -u origin main"
+    echo "  2) Overwrite remote Space: FORCE_PUSH=1 bash git_hf_sync.sh"
+    exit 1
+  fi
+fi
+
+echo "==> Done: https://huggingface.co/spaces/${USER}/RefDiffNet"

prebackbone_infer.py

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+"""Prebackbone enrichment inference (A11_CA) — loads prebackbone weights only."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Any
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+IMGSZ = int(os.environ.get("PREBACKBONE_IMGSZ", "640"))
+PREBACKBONE_ONLY_NAME = "prebackbone_a11_ca.pt"
+
+
+def _resolve_ultralytics_root() -> Path | None:
+    here = Path(__file__).resolve().parent
+    for base in (
+        here / "vendor",
+        here.parent / "ultralytics",
+        Path(os.environ.get("ULTRALYTICS_ROOT", "")).expanduser(),
+    ):
+        if not base:
+            continue
+        if (base / "ultralytics" / "__init__.py").exists():
+            return base
+        if base.name == "ultralytics" and (base / "__init__.py").exists():
+            return base.parent
+    return None
+
+
+def _ensure_ultralytics_import() -> None:
+    import sys
+
+    root = _resolve_ultralytics_root()
+    if root is not None:
+        root_str = str(root.resolve())
+        if root_str not in sys.path:
+            sys.path.insert(0, root_str)
+
+
+def _here() -> Path:
+    return Path(__file__).resolve().parent
+
+
+def _prebackbone_only_path() -> Path:
+    env = os.environ.get("PREBACKBONE_ONLY_WEIGHTS", "").strip()
+    if env:
+        return Path(env).expanduser()
+    return _here() / "weights" / PREBACKBONE_ONLY_NAME
+
+
+def _full_checkpoint_path() -> Path | None:
+    """Fallback full YOLO ckpt — only used to auto-extract prebackbone weights."""
+    env = os.environ.get("PREBACKBONE_FULL_CKPT", "").strip()
+    if env:
+        p = Path(env).expanduser()
+        return p if p.exists() else None
+    for candidate in (
+        _here() / "weights" / "best.pt",
+        _here().parent
+        / "ultralytics"
+        / "Proposed"
+        / "yolo12_training"
+        / "HRIPCB_Results"
+        / "yolo12n_hripcb_200epochs_batch16"
+        / "weights"
+        / "best.pt",
+    ):
+        if candidate.exists():
+            return candidate
+    return None
+
+
+def _download_hf_file(repo_id: str, filename: str) -> Path:
+    from huggingface_hub import hf_hub_download
+
+    dest_dir = _here() / "weights"
+    dest_dir.mkdir(parents=True, exist_ok=True)
+    return Path(hf_hub_download(repo_id=repo_id, filename=filename, local_dir=str(dest_dir)))
+
+
+def _resolve_weights_path() -> Path:
+    """Prefer small prebackbone-only file; optional HF hub download."""
+    pb_only = _prebackbone_only_path()
+    if pb_only.exists():
+        return pb_only
+
+    hf_repo = os.environ.get("HF_MODEL_REPO", "").strip()
+    if hf_repo:
+        try:
+            return _download_hf_file(hf_repo, PREBACKBONE_ONLY_NAME)
+        except Exception:
+            pass  # try full ckpt filename below
+        env_weights = os.environ.get("PREBACKBONE_WEIGHTS", PREBACKBONE_ONLY_NAME)
+        return _download_hf_file(hf_repo, env_weights)
+
+    env = os.environ.get("PREBACKBONE_WEIGHTS", "").strip()
+    if env and Path(env).expanduser().exists():
+        return Path(env).expanduser()
+
+    return pb_only
+
+
+def _maybe_extract_from_full_ckpt(pb_only_path: Path) -> Path:
+    """If only best.pt exists, extract prebackbone tensors once."""
+    if pb_only_path.exists():
+        return pb_only_path
+    full = _full_checkpoint_path()
+    if full is None:
+        return pb_only_path
+    from extract_prebackbone_weights import extract
+
+    print(f"[prebackbone] Extracting weights from {full} -> {pb_only_path}")
+    return extract(full, pb_only_path)
+
+
+def _filter_state_dict(state: dict, module: torch.nn.Module) -> dict:
+    """Keep only keys that belong to the module (drops thop total_ops/total_params)."""
+    expected = set(module.state_dict().keys())
+    filtered = {k: v for k, v in state.items() if k in expected}
+    if len(filtered) < len(expected):
+        missing = expected - set(filtered.keys())
+        raise RuntimeError(f"Prebackbone weights missing keys: {sorted(missing)[:8]}...")
+    return filtered
+
+
+def _load_prebackbone_module(weights_path: Path, device: torch.device) -> torch.nn.Module:
+    _ensure_ultralytics_import()
+    from ultralytics.nn.prebackbone import build_prebackbone
+
+    if not weights_path.exists():
+        weights_path = _maybe_extract_from_full_ckpt(weights_path)
+
+    if not weights_path.exists():
+        raise FileNotFoundError(
+            f"Prebackbone weights not found: {weights_path}\n"
+            "Run: python extract_prebackbone_weights.py --ckpt weights/best.pt\n"
+            "Or set PREBACKBONE_ONLY_WEIGHTS / HF_MODEL_REPO."
+        )
+
+    try:
+        payload = torch.load(weights_path, map_location="cpu", weights_only=True)
+    except TypeError:
+        payload = torch.load(weights_path, map_location="cpu")
+
+    if isinstance(payload, dict) and "state_dict" in payload:
+        name = str(payload.get("prebackbone", "A11_CA")).upper()
+        channels = int(payload.get("channels", 3))
+        state = payload["state_dict"]
+    else:
+        name, channels, state = "A11_CA", 3, payload
+
+    module = build_prebackbone(name, channels=channels)
+    if module is None:
+        raise RuntimeError(f"build_prebackbone({name}) returned None")
+    state = _filter_state_dict(state, module)
+    missing, unexpected = module.load_state_dict(state, strict=True)
+    if missing or unexpected:
+        raise RuntimeError(f"State dict mismatch: missing={missing}, unexpected={unexpected}")
+    return module.to(device).eval()
+
+
+def _bgr_to_rgb(im_bgr: np.ndarray) -> np.ndarray:
+    return cv2.cvtColor(im_bgr, cv2.COLOR_BGR2RGB)
+
+
+def _rgb_to_bgr(im_rgb: np.ndarray) -> np.ndarray:
+    return cv2.cvtColor(im_rgb, cv2.COLOR_RGB2BGR)
+
+
+def _letterbox_pair_bgr(
+    defect_bgr: np.ndarray,
+    golden_bgr: np.ndarray,
+    new_shape: tuple[int, int] = (IMGSZ, IMGSZ),
+) -> tuple[np.ndarray, np.ndarray]:
+    shape = defect_bgr.shape[:2]
+    if golden_bgr.shape[:2] != shape:
+        golden_bgr = cv2.resize(golden_bgr, (shape[1], shape[0]), interpolation=cv2.INTER_LINEAR)
+
+    new_h, new_w = new_shape
+    r = min(new_h / shape[0], new_w / shape[1])
+    new_unpad = (round(shape[1] * r), round(shape[0] * r))
+    dw, dh = (new_w - new_unpad[0]) / 2, (new_h - new_unpad[1]) / 2
+
+    if shape[::-1] != new_unpad:
+        defect_bgr = cv2.resize(defect_bgr, new_unpad, interpolation=cv2.INTER_LINEAR)
+        golden_bgr = cv2.resize(golden_bgr, new_unpad, interpolation=cv2.INTER_LINEAR)
+
+    top, bottom = round(dh - 0.1), round(dh + 0.1)
+    left, right = round(dw - 0.1), round(dw + 0.1)
+    pad = (114, 114, 114)
+    defect_bgr = cv2.copyMakeBorder(defect_bgr, top, bottom, left, right, cv2.BORDER_CONSTANT, value=pad)
+    golden_bgr = cv2.copyMakeBorder(golden_bgr, top, bottom, left, right, cv2.BORDER_CONSTANT, value=pad)
+    return defect_bgr, golden_bgr
+
+
+def _img_to_tensor_rgb(im_rgb: np.ndarray, device: torch.device) -> torch.Tensor:
+    x = torch.from_numpy(im_rgb).to(device=device)
+    return x.permute(2, 0, 1).contiguous().float().unsqueeze(0) / 255.0
+
+
+def _tensor_to_rgb_u8(x: torch.Tensor) -> np.ndarray:
+    if x.ndim == 4:
+        x = x[0]
+    x = x.detach().float().clamp(0.0, 1.0).cpu()
+    return (x.permute(1, 2, 0).numpy() * 255.0).round().astype(np.uint8)
+
+
+def _load_image_rgb(image: str | Path | Image.Image | np.ndarray) -> np.ndarray:
+    if isinstance(image, Image.Image):
+        return np.array(image.convert("RGB"))
+    if isinstance(image, np.ndarray):
+        arr = image
+        if arr.ndim == 2:
+            return np.stack([arr, arr, arr], axis=-1)
+        if arr.shape[2] == 4:
+            return arr[..., :3]
+        return arr[..., :3] if arr.shape[2] >= 3 else arr
+    path = Path(image)
+    bgr = cv2.imread(str(path))
+    if bgr is None:
+        raise FileNotFoundError(f"Unable to read image: {path}")
+    return _bgr_to_rgb(bgr)
+
+
+class PreBackboneEnricher:
+    """Runs A11_CA prebackbone only (no YOLO backbone/head loaded)."""
+
+    def __init__(self, weights: str | Path | None = None, device: str | None = None):
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = torch.device(device)
+        self.weights = Path(weights) if weights else _resolve_weights_path()
+        self.prebackbone = _load_prebackbone_module(self.weights, self.device)
+        self.imgsz = IMGSZ
+
+    @torch.inference_mode()
+    def enrich(
+        self,
+        defect: str | Path | Image.Image | np.ndarray,
+        reference: str | Path | Image.Image | np.ndarray,
+        *,
+        letterbox: bool = True,
+        return_reference: bool = False,
+    ) -> np.ndarray | tuple[np.ndarray, np.ndarray, np.ndarray]:
+        defect_rgb0 = _load_image_rgb(defect)
+        golden_rgb0 = _load_image_rgb(reference)
+
+        defect_bgr0 = _rgb_to_bgr(defect_rgb0)
+        golden_bgr0 = _rgb_to_bgr(golden_rgb0)
+
+        if letterbox:
+            defect_bgr, golden_bgr = _letterbox_pair_bgr(defect_bgr0, golden_bgr0, (self.imgsz, self.imgsz))
+        else:
+            if defect_bgr0.shape != golden_bgr0.shape:
+                golden_bgr0 = cv2.resize(
+                    golden_bgr0, (defect_bgr0.shape[1], defect_bgr0.shape[0]), interpolation=cv2.INTER_LINEAR
+                )
+            defect_bgr, golden_bgr = defect_bgr0, golden_bgr0
+
+        defect_rgb = _bgr_to_rgb(defect_bgr)
+        golden_rgb = _bgr_to_rgb(golden_bgr)
+
+        defect_t = _img_to_tensor_rgb(defect_rgb, self.device)
+        golden_t = _img_to_tensor_rgb(golden_rgb, self.device)
+        _ = self.prebackbone(defect_t, golden_t)
+        dbg: dict[str, Any] = getattr(self.prebackbone, "_debug", {})
+        enriched = dbg.get("enriched")
+        if enriched is None:
+            raise RuntimeError("Prebackbone did not populate _debug['enriched'].")
+
+        enriched_rgb = _tensor_to_rgb_u8(enriched)
+        if return_reference:
+            return defect_rgb, golden_rgb, enriched_rgb
+        return enriched_rgb
+
+
+_enricher: PreBackboneEnricher | None = None
+
+
+def get_enricher() -> PreBackboneEnricher:
+    global _enricher
+    if _enricher is None:
+        _enricher = PreBackboneEnricher()
+    return _enricher
+
+
+def enrich_pair(
+    defect: str | Path | Image.Image | np.ndarray,
+    reference: str | Path | Image.Image | np.ndarray,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    return get_enricher().enrich(defect, reference, return_reference=True)  # type: ignore[return-value]

push_clean_space.sh

@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# One clean commit with NO .jpg in git history (HF rejects raw binaries in any commit).
+set -euo pipefail
+cd "$(dirname "$0")"
+
+unset GIT_ASKPASS SSH_ASKPASS
+
+if [[ ! -f weights/prebackbone_a11_ca.pt ]]; then
+  echo "ERROR: weights/prebackbone_a11_ca.pt missing. Run extract_prebackbone_weights.py first."
+  exit 1
+fi
+
+if command -v hf >/dev/null 2>&1; then
+  TOKEN="$(hf auth token 2>/dev/null | tr -d '[:space:]')"
+  if [[ -n "${TOKEN}" ]]; then
+    CRED_FILE="${HOME}/.git-credentials"
+    grep -v "huggingface.co" "${CRED_FILE}" 2>/dev/null > "${CRED_FILE}.tmp" || true
+    [[ -f "${CRED_FILE}.tmp" ]] && mv "${CRED_FILE}.tmp" "${CRED_FILE}"
+    echo "https://vinayedula:${TOKEN}@huggingface.co" >> "${CRED_FILE}"
+    chmod 600 "${CRED_FILE}"
+  fi
+fi
+
+git lfs install
+git lfs track "*.pt" "*.pth"
+
+grep -q "^examples/" .gitignore 2>/dev/null || echo "examples/" >> .gitignore
+
+git checkout --orphan main-clean
+git rm -rf --cached . 2>/dev/null || true
+
+git add \
+  .gitattributes \
+  .gitignore \
+  app.py \
+  prebackbone_infer.py \
+  extract_prebackbone_weights.py \
+  requirements.txt \
+  setup.sh \
+  README.md \
+  deploy_to_refdiffnet.sh \
+  git_hf_sync.sh \
+  fix_hf_binaries.sh \
+  push_clean_space.sh \
+  vendor/ \
+  weights/prebackbone_a11_ca.pt
+
+git commit -m "RefDiffNet: A11_CA prebackbone demo (clean history, no images in git)"
+
+git branch -M main
+git remote set-url origin https://huggingface.co/spaces/vinayedula/RefDiffNet
+
+echo "==> Force pushing clean history..."
+git push -f origin main
+
+echo "==> Done: https://huggingface.co/spaces/vinayedula/RefDiffNet"

requirements.txt

@@ -0,0 +1,13 @@
+gradio>=4.44.0
+torch>=2.0.0
+torchvision>=0.15.0
+opencv-python-headless>=4.8.0
+pillow>=10.0.0
+numpy>=1.23.0
+pyyaml>=6.0
+huggingface_hub>=0.23.0
+matplotlib>=3.7.0
+scipy>=1.10.0
+psutil>=5.9.0
+polars>=0.20.0
+ultralytics-thop>=2.0.18

setup.sh

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+# Hugging Face Spaces: install vendored custom Ultralytics before app starts.
+set -euo pipefail
+cd "$(dirname "$0")"
+if [[ -d vendor ]]; then
+  pip install -q -e ./vendor
+  echo "Installed custom ultralytics from ./vendor"
+else
+  echo "WARN: vendor/ missing — run prepare_space.sh before deploying"
+fi

vendor/pyproject.toml

@@ -0,0 +1,194 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Overview:
+# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.
+# It defines essential project metadata, dependencies, and settings used to develop and deploy the library.
+
+# Key Sections:
+# - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel).
+# - [project]: Includes details like name, version, description, authors, dependencies and more.
+# - [project.optional-dependencies]: Provides additional, optional packages for extended features.
+# - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project.
+
+# Installation:
+# The Ultralytics library can be installed using the command: 'pip install ultralytics'
+# For development purposes, you can install the package in editable mode with: 'pip install -e .'
+# This approach allows for real-time code modifications without the need for re-installation.
+
+# Documentation:
+# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com
+
+[build-system]
+requires = ["setuptools>=70.0.0,<=82.0.1", "wheel"]
+build-backend = "setuptools.build_meta"
+
+# Project settings -----------------------------------------------------------------------------------------------------
+[project]
+name = "ultralytics"
+dynamic = ["version"]
+description = "Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
+readme = "README.md"
+requires-python = ">=3.8"
+license = { "text" = "AGPL-3.0" }
+keywords = ["machine-learning", "deep-learning", "computer-vision", "ML", "DL", "AI", "YOLO", "YOLOv3", "YOLOv5", "YOLOv8", "YOLOv9", "YOLOv10", "YOLO11", "HUB", "Ultralytics"]
+authors = [
+    { name = "Glenn Jocher", email = "glenn.jocher@ultralytics.com" },
+    { name = "Jing Qiu", email = "jing.qiu@ultralytics.com" },
+]
+maintainers = [
+    { name = "Ultralytics", email = "hello@ultralytics.com" },
+]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Education",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Software Development",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Image Recognition",
+    "Operating System :: POSIX :: Linux",
+    "Operating System :: MacOS",
+    "Operating System :: Microsoft :: Windows",
+]
+
+# Required dependencies ------------------------------------------------------------------------------------------------
+dependencies = [
+    "numpy>=1.23.0",
+    "matplotlib>=3.3.0",
+    "opencv-python>=4.6.0",
+    "pillow>=7.1.2",
+    "pyyaml>=5.3.1",
+    "requests>=2.23.0",
+    "scipy>=1.4.1",
+    "torch>=1.8.0",
+    "torch>=1.8.0,!=2.4.0; sys_platform == 'win32'", # Windows CPU errors w/ 2.4.0 https://github.com/ultralytics/ultralytics/issues/15049
+    "torchvision>=0.9.0",
+    "psutil>=5.8.0", # system utilization
+    "polars>=0.20.0",
+    "ultralytics-thop>=2.0.18", # FLOPs computation https://github.com/ultralytics/thop
+]
+
+# Optional dependencies ------------------------------------------------------------------------------------------------
+[project.optional-dependencies]
+dev = [
+    "ipython",
+    "pytest",
+    "pytest-cov",
+    "coverage[toml]",
+    "zensical>=0.0.15; python_version >= '3.10'",
+    "mkdocs-ultralytics-plugin>=0.2.4", # for meta descriptions and images, dates and authors
+    "minijinja>=2.0.0", # render docs macros without mkdocs-macros-plugin
+]
+export = [
+    "numpy<2.0.0", # TF 2.20 compatibility
+    "onnx>=1.12.0; platform_system != 'Darwin'", # ONNX export
+    "onnx>=1.12.0,<1.18.0; platform_system == 'Darwin'",  # TF inference hanging on MacOS (tested up to onnx==1.20.0)
+    "onnxslim>=0.1.82",
+    "coremltools>=9.0; platform_system != 'Windows' and python_version <= '3.13'", # CoreML supported on macOS and Linux
+    "scikit-learn>=1.3.2; platform_system != 'Windows' and python_version <= '3.13'", # CoreML k-means quantization
+    "openvino>=2024.0.0",  # OpenVINO export
+    "tensorflow>=2.0.0,<=2.19.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
+    "tensorflowjs>=2.0.0", # TF.js export, automatically installs tensorflow
+    "tensorstore>=0.1.63; platform_machine == 'aarch64' and python_version >= '3.9'", # for TF Raspberry Pi exports
+    "h5py!=3.11.0; platform_machine == 'aarch64'", # fix h5py build issues due to missing aarch64 wheels in 3.11 release
+    "setuptools<=81.0.0",  # pin due to >=82.0.0 breaking tensorflow.js package
+    "packaging>=26.0; platform_machine == 'aarch64' and platform_system == 'Linux' and python_version >= '3.9'", # IMX export bug
+]
+solutions = [
+    "shapely>=2.0.0",    # shapely for point and polygon data matching
+    "streamlit>=1.51.0; python_version >= '3.10'",    # for live inference on web browser, i.e `yolo streamlit-predict`
+    "streamlit>=1.29.0,<1.51.0; python_version < '3.10' and (python_version < '3.9' or platform_machine != 'aarch64' or platform_system != 'Linux')",
+    "flask>=3.0.1",  # for similarity search solution
+]
+logging = [
+    "wandb", # https://docs.ultralytics.com/integrations/weights-biases/
+    "tensorboard",  # https://docs.ultralytics.com/integrations/tensorboard/
+    "mlflow",  # https://docs.ultralytics.com/integrations/mlflow/
+]
+extra = [
+    "ipython", # interactive notebook
+    "albumentations>=1.4.6", # training augmentations
+    "faster-coco-eval>=1.6.7", # COCO mAP
+]
+typing = [
+    "scipy-stubs>=1.14.1.4; python_version >= '3.10'",
+    "types-pillow",
+    "types-psutil",
+    "types-pyyaml",
+    "types-requests",
+    "types-shapely",
+]
+
+[project.urls]
+"Homepage" = "https://ultralytics.com"
+"Source" = "https://github.com/ultralytics/ultralytics"
+"Documentation" = "https://docs.ultralytics.com"
+"Bug Reports" = "https://github.com/ultralytics/ultralytics/issues"
+"Changelog" = "https://github.com/ultralytics/ultralytics/releases"
+
+[project.scripts]
+yolo = "ultralytics.cfg:entrypoint"
+ultralytics = "ultralytics.cfg:entrypoint"
+
+# Tools settings -------------------------------------------------------------------------------------------------------
+[tool.setuptools]  # configuration specific to the `setuptools` build backend.
+packages = { find = { where = ["."], include = ["ultralytics", "ultralytics.*"] } }
+# Tests included below for checking Conda builds in https://github.com/conda-forge/ultralytics-feedstock
+package-data = { "ultralytics" = ["**/*.yaml", "**/*.sh", "../tests/*.py"], "ultralytics.assets" = ["*.jpg"], "ultralytics.solutions.templates" = ["*.html"]}
+
+[tool.setuptools.dynamic]
+version = { attr = "ultralytics.__version__" }
+
+[tool.pytest.ini_options]
+addopts = "--doctest-modules --durations=30 --color=yes"
+markers = [
+    "slow: skip slow tests unless --slow is set",
+]
+norecursedirs = [".git", "dist", "build"]
+
+[tool.coverage.run]
+source = ["ultralytics/"]
+data_file = "tests/.coverage"
+omit = ["ultralytics/utils/callbacks/*"]
+
+[tool.isort]
+line_length = 120
+multi_line_output = 0
+
+[tool.yapf]
+based_on_style = "pep8"
+spaces_before_comment = 2
+column_limit = 120
+coalesce_brackets = true
+spaces_around_power_operator = true
+space_between_ending_comma_and_closing_bracket = true
+split_before_closing_bracket = false
+split_before_first_argument = false
+
+[tool.ruff]
+line-length = 120
+
+[tool.ruff.format]
+docstring-code-format = true
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.docformatter]
+wrap-summaries = 120
+wrap-descriptions = 120
+pre-summary-newline = true
+close-quotes-on-newline = true
+in-place = true
+
+[tool.codespell]
+ignore-words-list = "grey,writeable,finalY,RepResNet,Idenfy,WIT,Smoot,EHR,ROUGE,ALS,iTerm,Carmel,FPR,Hach,Calle,ore,COO,MOT,crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall,skelton,goin"
+skip = "*.pt,*.pth,*.torchscript,*.onnx,*.tflite,*.pb,*.bin,*.param,*.mlmodel,*.engine,*.npy,*.data*,*.csv,*pnnx*,*venv*,*translat*,*lock*,__pycache__*,*.ico,*.jpg,*.png,*.webp,*.avif,*.mp4,*.mov,/runs,/.git,./docs/??/*.md,./docs/mkdocs_??.yml"

vendor/ultralytics.egg-info/PKG-INFO

@@ -0,0 +1,88 @@
+Metadata-Version: 2.4
+Name: ultralytics
+Version: 8.4.21
+Summary: Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.
+Author-email: Glenn Jocher <glenn.jocher@ultralytics.com>, Jing Qiu <jing.qiu@ultralytics.com>
+Maintainer-email: Ultralytics <hello@ultralytics.com>
+License: AGPL-3.0
+Project-URL: Homepage, https://ultralytics.com
+Project-URL: Source, https://github.com/ultralytics/ultralytics
+Project-URL: Documentation, https://docs.ultralytics.com
+Project-URL: Bug Reports, https://github.com/ultralytics/ultralytics/issues
+Project-URL: Changelog, https://github.com/ultralytics/ultralytics/releases
+Keywords: machine-learning,deep-learning,computer-vision,ML,DL,AI,YOLO,YOLOv3,YOLOv5,YOLOv8,YOLOv9,YOLOv10,YOLO11,HUB,Ultralytics
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Software Development
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Scientific/Engineering :: Image Recognition
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Operating System :: MacOS
+Classifier: Operating System :: Microsoft :: Windows
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+Requires-Dist: numpy>=1.23.0
+Requires-Dist: matplotlib>=3.3.0
+Requires-Dist: opencv-python>=4.6.0
+Requires-Dist: pillow>=7.1.2
+Requires-Dist: pyyaml>=5.3.1
+Requires-Dist: requests>=2.23.0
+Requires-Dist: scipy>=1.4.1
+Requires-Dist: torch>=1.8.0
+Requires-Dist: torch!=2.4.0,>=1.8.0; sys_platform == "win32"
+Requires-Dist: torchvision>=0.9.0
+Requires-Dist: psutil>=5.8.0
+Requires-Dist: polars>=0.20.0
+Requires-Dist: ultralytics-thop>=2.0.18
+Provides-Extra: dev
+Requires-Dist: ipython; extra == "dev"
+Requires-Dist: pytest; extra == "dev"
+Requires-Dist: pytest-cov; extra == "dev"
+Requires-Dist: coverage[toml]; extra == "dev"
+Requires-Dist: zensical>=0.0.15; python_version >= "3.10" and extra == "dev"
+Requires-Dist: mkdocs-ultralytics-plugin>=0.2.4; extra == "dev"
+Requires-Dist: minijinja>=2.0.0; extra == "dev"
+Provides-Extra: export
+Requires-Dist: numpy<2.0.0; extra == "export"
+Requires-Dist: onnx>=1.12.0; platform_system != "Darwin" and extra == "export"
+Requires-Dist: onnx<1.18.0,>=1.12.0; platform_system == "Darwin" and extra == "export"
+Requires-Dist: onnxslim>=0.1.82; extra == "export"
+Requires-Dist: coremltools>=9.0; (platform_system != "Windows" and python_version <= "3.13") and extra == "export"
+Requires-Dist: scikit-learn>=1.3.2; (platform_system != "Windows" and python_version <= "3.13") and extra == "export"
+Requires-Dist: openvino>=2024.0.0; extra == "export"
+Requires-Dist: tensorflow<=2.19.0,>=2.0.0; extra == "export"
+Requires-Dist: tensorflowjs>=2.0.0; extra == "export"
+Requires-Dist: tensorstore>=0.1.63; (platform_machine == "aarch64" and python_version >= "3.9") and extra == "export"
+Requires-Dist: h5py!=3.11.0; platform_machine == "aarch64" and extra == "export"
+Requires-Dist: setuptools<=81.0.0; extra == "export"
+Requires-Dist: packaging>=26.0; (platform_machine == "aarch64" and platform_system == "Linux" and python_version >= "3.9") and extra == "export"
+Provides-Extra: solutions
+Requires-Dist: shapely>=2.0.0; extra == "solutions"
+Requires-Dist: streamlit>=1.51.0; python_version >= "3.10" and extra == "solutions"
+Requires-Dist: streamlit<1.51.0,>=1.29.0; (python_version < "3.10" and (python_version < "3.9" or platform_machine != "aarch64" or platform_system != "Linux")) and extra == "solutions"
+Requires-Dist: flask>=3.0.1; extra == "solutions"
+Provides-Extra: logging
+Requires-Dist: wandb; extra == "logging"
+Requires-Dist: tensorboard; extra == "logging"
+Requires-Dist: mlflow; extra == "logging"
+Provides-Extra: extra
+Requires-Dist: ipython; extra == "extra"
+Requires-Dist: albumentations>=1.4.6; extra == "extra"
+Requires-Dist: faster-coco-eval>=1.6.7; extra == "extra"
+Provides-Extra: typing
+Requires-Dist: scipy-stubs>=1.14.1.4; python_version >= "3.10" and extra == "typing"
+Requires-Dist: types-pillow; extra == "typing"
+Requires-Dist: types-psutil; extra == "typing"
+Requires-Dist: types-pyyaml; extra == "typing"
+Requires-Dist: types-requests; extra == "typing"
+Requires-Dist: types-shapely; extra == "typing"

vendor/ultralytics.egg-info/SOURCES.txt

@@ -0,0 +1,308 @@
+pyproject.toml
+ultralytics/__init__.py
+ultralytics/py.typed
+ultralytics.egg-info/PKG-INFO
+ultralytics.egg-info/SOURCES.txt
+ultralytics.egg-info/dependency_links.txt
+ultralytics.egg-info/entry_points.txt
+ultralytics.egg-info/requires.txt
+ultralytics.egg-info/top_level.txt
+ultralytics/assets/bus.jpg
+ultralytics/assets/zidane.jpg
+ultralytics/cfg/__init__.py
+ultralytics/cfg/default.yaml
+ultralytics/cfg/datasets/Argoverse.yaml
+ultralytics/cfg/datasets/DOTAv1.5.yaml
+ultralytics/cfg/datasets/DOTAv1.yaml
+ultralytics/cfg/datasets/GlobalWheat2020.yaml
+ultralytics/cfg/datasets/HomeObjects-3K.yaml
+ultralytics/cfg/datasets/ImageNet.yaml
+ultralytics/cfg/datasets/Objects365.yaml
+ultralytics/cfg/datasets/SKU-110K.yaml
+ultralytics/cfg/datasets/TT100K.yaml
+ultralytics/cfg/datasets/VOC.yaml
+ultralytics/cfg/datasets/VisDrone.yaml
+ultralytics/cfg/datasets/african-wildlife.yaml
+ultralytics/cfg/datasets/brain-tumor.yaml
+ultralytics/cfg/datasets/carparts-seg.yaml
+ultralytics/cfg/datasets/coco-pose.yaml
+ultralytics/cfg/datasets/coco.yaml
+ultralytics/cfg/datasets/coco12-formats.yaml
+ultralytics/cfg/datasets/coco128-seg.yaml
+ultralytics/cfg/datasets/coco128.yaml
+ultralytics/cfg/datasets/coco8-grayscale.yaml
+ultralytics/cfg/datasets/coco8-multispectral.yaml
+ultralytics/cfg/datasets/coco8-pose.yaml
+ultralytics/cfg/datasets/coco8-seg.yaml
+ultralytics/cfg/datasets/coco8.yaml
+ultralytics/cfg/datasets/construction-ppe.yaml
+ultralytics/cfg/datasets/crack-seg.yaml
+ultralytics/cfg/datasets/dog-pose.yaml
+ultralytics/cfg/datasets/dota8-multispectral.yaml
+ultralytics/cfg/datasets/dota8.yaml
+ultralytics/cfg/datasets/hand-keypoints.yaml
+ultralytics/cfg/datasets/kitti.yaml
+ultralytics/cfg/datasets/lvis.yaml
+ultralytics/cfg/datasets/medical-pills.yaml
+ultralytics/cfg/datasets/open-images-v7.yaml
+ultralytics/cfg/datasets/package-seg.yaml
+ultralytics/cfg/datasets/signature.yaml
+ultralytics/cfg/datasets/tiger-pose.yaml
+ultralytics/cfg/datasets/xView.yaml
+ultralytics/cfg/models/11/yolo11-cls-resnet18.yaml
+ultralytics/cfg/models/11/yolo11-cls.yaml
+ultralytics/cfg/models/11/yolo11-obb.yaml
+ultralytics/cfg/models/11/yolo11-pose.yaml
+ultralytics/cfg/models/11/yolo11-seg.yaml
+ultralytics/cfg/models/11/yolo11.yaml
+ultralytics/cfg/models/11/yoloe-11-seg.yaml
+ultralytics/cfg/models/11/yoloe-11.yaml
+ultralytics/cfg/models/12/yolo12-cls.yaml
+ultralytics/cfg/models/12/yolo12-obb.yaml
+ultralytics/cfg/models/12/yolo12-pose.yaml
+ultralytics/cfg/models/12/yolo12-seg.yaml
+ultralytics/cfg/models/12/yolo12.yaml
+ultralytics/cfg/models/26/yolo26-cls.yaml
+ultralytics/cfg/models/26/yolo26-obb.yaml
+ultralytics/cfg/models/26/yolo26-p2.yaml
+ultralytics/cfg/models/26/yolo26-p6.yaml
+ultralytics/cfg/models/26/yolo26-pose.yaml
+ultralytics/cfg/models/26/yolo26-seg.yaml
+ultralytics/cfg/models/26/yolo26.yaml
+ultralytics/cfg/models/26/yoloe-26-seg.yaml
+ultralytics/cfg/models/26/yoloe-26.yaml
+ultralytics/cfg/models/rt-detr/rtdetr-l.yaml
+ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
+ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
+ultralytics/cfg/models/rt-detr/rtdetr-x.yaml
+ultralytics/cfg/models/v10/yolov10b.yaml
+ultralytics/cfg/models/v10/yolov10l.yaml
+ultralytics/cfg/models/v10/yolov10m.yaml
+ultralytics/cfg/models/v10/yolov10n.yaml
+ultralytics/cfg/models/v10/yolov10s.yaml
+ultralytics/cfg/models/v10/yolov10x.yaml
+ultralytics/cfg/models/v3/yolov3-spp.yaml
+ultralytics/cfg/models/v3/yolov3-tiny.yaml
+ultralytics/cfg/models/v3/yolov3.yaml
+ultralytics/cfg/models/v5/yolov5-p6.yaml
+ultralytics/cfg/models/v5/yolov5.yaml
+ultralytics/cfg/models/v6/yolov6.yaml
+ultralytics/cfg/models/v8/yoloe-v8-seg.yaml
+ultralytics/cfg/models/v8/yoloe-v8.yaml
+ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
+ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
+ultralytics/cfg/models/v8/yolov8-cls.yaml
+ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
+ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
+ultralytics/cfg/models/v8/yolov8-ghost.yaml
+ultralytics/cfg/models/v8/yolov8-obb.yaml
+ultralytics/cfg/models/v8/yolov8-p2.yaml
+ultralytics/cfg/models/v8/yolov8-p6.yaml
+ultralytics/cfg/models/v8/yolov8-pose-p6.yaml
+ultralytics/cfg/models/v8/yolov8-pose.yaml
+ultralytics/cfg/models/v8/yolov8-rtdetr.yaml
+ultralytics/cfg/models/v8/yolov8-seg-p6.yaml
+ultralytics/cfg/models/v8/yolov8-seg.yaml
+ultralytics/cfg/models/v8/yolov8-world.yaml
+ultralytics/cfg/models/v8/yolov8-worldv2.yaml
+ultralytics/cfg/models/v8/yolov8.yaml
+ultralytics/cfg/models/v9/yolov9c-seg.yaml
+ultralytics/cfg/models/v9/yolov9c.yaml
+ultralytics/cfg/models/v9/yolov9e-seg.yaml
+ultralytics/cfg/models/v9/yolov9e.yaml
+ultralytics/cfg/models/v9/yolov9m.yaml
+ultralytics/cfg/models/v9/yolov9s.yaml
+ultralytics/cfg/models/v9/yolov9t.yaml
+ultralytics/cfg/trackers/botsort.yaml
+ultralytics/cfg/trackers/bytetrack.yaml
+ultralytics/data/__init__.py
+ultralytics/data/annotator.py
+ultralytics/data/augment.py
+ultralytics/data/base.py
+ultralytics/data/build.py
+ultralytics/data/converter.py
+ultralytics/data/dataset.py
+ultralytics/data/loaders.py
+ultralytics/data/split.py
+ultralytics/data/split_dota.py
+ultralytics/data/utils.py
+ultralytics/data/scripts/download_weights.sh
+ultralytics/data/scripts/get_coco.sh
+ultralytics/data/scripts/get_coco128.sh
+ultralytics/data/scripts/get_imagenet.sh
+ultralytics/engine/__init__.py
+ultralytics/engine/exporter.py
+ultralytics/engine/model.py
+ultralytics/engine/predictor.py
+ultralytics/engine/results.py
+ultralytics/engine/trainer.py
+ultralytics/engine/tuner.py
+ultralytics/engine/validator.py
+ultralytics/hub/__init__.py
+ultralytics/hub/auth.py
+ultralytics/hub/session.py
+ultralytics/hub/utils.py
+ultralytics/hub/google/__init__.py
+ultralytics/models/__init__.py
+ultralytics/models/fastsam/__init__.py
+ultralytics/models/fastsam/model.py
+ultralytics/models/fastsam/predict.py
+ultralytics/models/fastsam/utils.py
+ultralytics/models/fastsam/val.py
+ultralytics/models/nas/__init__.py
+ultralytics/models/nas/model.py
+ultralytics/models/nas/predict.py
+ultralytics/models/nas/val.py
+ultralytics/models/rtdetr/__init__.py
+ultralytics/models/rtdetr/model.py
+ultralytics/models/rtdetr/predict.py
+ultralytics/models/rtdetr/train.py
+ultralytics/models/rtdetr/val.py
+ultralytics/models/sam/__init__.py
+ultralytics/models/sam/amg.py
+ultralytics/models/sam/build.py
+ultralytics/models/sam/build_sam3.py
+ultralytics/models/sam/model.py
+ultralytics/models/sam/predict.py
+ultralytics/models/sam/modules/__init__.py
+ultralytics/models/sam/modules/blocks.py
+ultralytics/models/sam/modules/decoders.py
+ultralytics/models/sam/modules/encoders.py
+ultralytics/models/sam/modules/memory_attention.py
+ultralytics/models/sam/modules/sam.py
+ultralytics/models/sam/modules/tiny_encoder.py
+ultralytics/models/sam/modules/transformer.py
+ultralytics/models/sam/modules/utils.py
+ultralytics/models/sam/sam3/__init__.py
+ultralytics/models/sam/sam3/decoder.py
+ultralytics/models/sam/sam3/encoder.py
+ultralytics/models/sam/sam3/geometry_encoders.py
+ultralytics/models/sam/sam3/maskformer_segmentation.py
+ultralytics/models/sam/sam3/model_misc.py
+ultralytics/models/sam/sam3/necks.py
+ultralytics/models/sam/sam3/sam3_image.py
+ultralytics/models/sam/sam3/text_encoder_ve.py
+ultralytics/models/sam/sam3/vitdet.py
+ultralytics/models/sam/sam3/vl_combiner.py
+ultralytics/models/utils/__init__.py
+ultralytics/models/utils/loss.py
+ultralytics/models/utils/ops.py
+ultralytics/models/yolo/__init__.py
+ultralytics/models/yolo/model.py
+ultralytics/models/yolo/classify/__init__.py
+ultralytics/models/yolo/classify/predict.py
+ultralytics/models/yolo/classify/train.py
+ultralytics/models/yolo/classify/val.py
+ultralytics/models/yolo/detect/__init__.py
+ultralytics/models/yolo/detect/predict.py
+ultralytics/models/yolo/detect/train.py
+ultralytics/models/yolo/detect/val.py
+ultralytics/models/yolo/obb/__init__.py
+ultralytics/models/yolo/obb/predict.py
+ultralytics/models/yolo/obb/train.py
+ultralytics/models/yolo/obb/val.py
+ultralytics/models/yolo/pose/__init__.py
+ultralytics/models/yolo/pose/predict.py
+ultralytics/models/yolo/pose/train.py
+ultralytics/models/yolo/pose/val.py
+ultralytics/models/yolo/segment/__init__.py
+ultralytics/models/yolo/segment/predict.py
+ultralytics/models/yolo/segment/train.py
+ultralytics/models/yolo/segment/val.py
+ultralytics/models/yolo/world/__init__.py
+ultralytics/models/yolo/world/train.py
+ultralytics/models/yolo/world/train_world.py
+ultralytics/models/yolo/yoloe/__init__.py
+ultralytics/models/yolo/yoloe/predict.py
+ultralytics/models/yolo/yoloe/train.py
+ultralytics/models/yolo/yoloe/train_seg.py
+ultralytics/models/yolo/yoloe/val.py
+ultralytics/nn/__init__.py
+ultralytics/nn/ablation.py
+ultralytics/nn/autobackend.py
+ultralytics/nn/prebackbone.py
+ultralytics/nn/prebackboneB.py
+ultralytics/nn/tasks.py
+ultralytics/nn/text_model.py
+ultralytics/nn/modules/__init__.py
+ultralytics/nn/modules/activation.py
+ultralytics/nn/modules/block.py
+ultralytics/nn/modules/conv.py
+ultralytics/nn/modules/head.py
+ultralytics/nn/modules/transformer.py
+ultralytics/nn/modules/utils.py
+ultralytics/optim/__init__.py
+ultralytics/optim/muon.py
+ultralytics/solutions/__init__.py
+ultralytics/solutions/ai_gym.py
+ultralytics/solutions/analytics.py
+ultralytics/solutions/config.py
+ultralytics/solutions/distance_calculation.py
+ultralytics/solutions/heatmap.py
+ultralytics/solutions/instance_segmentation.py
+ultralytics/solutions/object_blurrer.py
+ultralytics/solutions/object_counter.py
+ultralytics/solutions/object_cropper.py
+ultralytics/solutions/parking_management.py
+ultralytics/solutions/queue_management.py
+ultralytics/solutions/region_counter.py
+ultralytics/solutions/security_alarm.py
+ultralytics/solutions/similarity_search.py
+ultralytics/solutions/solutions.py
+ultralytics/solutions/speed_estimation.py
+ultralytics/solutions/streamlit_inference.py
+ultralytics/solutions/trackzone.py
+ultralytics/solutions/vision_eye.py
+ultralytics/solutions/templates/similarity-search.html
+ultralytics/trackers/__init__.py
+ultralytics/trackers/basetrack.py
+ultralytics/trackers/bot_sort.py
+ultralytics/trackers/byte_tracker.py
+ultralytics/trackers/track.py
+ultralytics/trackers/utils/__init__.py
+ultralytics/trackers/utils/gmc.py
+ultralytics/trackers/utils/kalman_filter.py
+ultralytics/trackers/utils/matching.py
+ultralytics/utils/__init__.py
+ultralytics/utils/autobatch.py
+ultralytics/utils/autodevice.py
+ultralytics/utils/benchmarks.py
+ultralytics/utils/checks.py
+ultralytics/utils/cpu.py
+ultralytics/utils/dist.py
+ultralytics/utils/downloads.py
+ultralytics/utils/errors.py
+ultralytics/utils/events.py
+ultralytics/utils/files.py
+ultralytics/utils/git.py
+ultralytics/utils/instance.py
+ultralytics/utils/logger.py
+ultralytics/utils/loss.py
+ultralytics/utils/metrics.py
+ultralytics/utils/nms.py
+ultralytics/utils/ops.py
+ultralytics/utils/patches.py
+ultralytics/utils/plotting.py
+ultralytics/utils/tal.py
+ultralytics/utils/torch_utils.py
+ultralytics/utils/tqdm.py
+ultralytics/utils/triton.py
+ultralytics/utils/tuner.py
+ultralytics/utils/uploads.py
+ultralytics/utils/callbacks/__init__.py
+ultralytics/utils/callbacks/base.py
+ultralytics/utils/callbacks/clearml.py
+ultralytics/utils/callbacks/comet.py
+ultralytics/utils/callbacks/dvc.py
+ultralytics/utils/callbacks/hub.py
+ultralytics/utils/callbacks/mlflow.py
+ultralytics/utils/callbacks/neptune.py
+ultralytics/utils/callbacks/platform.py
+ultralytics/utils/callbacks/raytune.py
+ultralytics/utils/callbacks/tensorboard.py
+ultralytics/utils/callbacks/wb.py
+ultralytics/utils/export/__init__.py
+ultralytics/utils/export/engine.py
+ultralytics/utils/export/executorch.py
+ultralytics/utils/export/imx.py
+ultralytics/utils/export/tensorflow.py

vendor/ultralytics.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

vendor/ultralytics.egg-info/entry_points.txt

@@ -0,0 +1,3 @@
+[console_scripts]
+ultralytics = ultralytics.cfg:entrypoint
+yolo = ultralytics.cfg:entrypoint

vendor/ultralytics.egg-info/requires.txt

@@ -0,0 +1,83 @@
+numpy>=1.23.0
+matplotlib>=3.3.0
+opencv-python>=4.6.0
+pillow>=7.1.2
+pyyaml>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.8.0
+torchvision>=0.9.0
+psutil>=5.8.0
+polars>=0.20.0
+ultralytics-thop>=2.0.18
+
+[:sys_platform == "win32"]
+torch!=2.4.0,>=1.8.0
+
+[dev]
+ipython
+pytest
+pytest-cov
+coverage[toml]
+mkdocs-ultralytics-plugin>=0.2.4
+minijinja>=2.0.0
+
+[dev:python_version >= "3.10"]
+zensical>=0.0.15
+
+[export]
+numpy<2.0.0
+onnxslim>=0.1.82
+openvino>=2024.0.0
+tensorflow<=2.19.0,>=2.0.0
+tensorflowjs>=2.0.0
+setuptools<=81.0.0
+
+[export:platform_machine == "aarch64"]
+h5py!=3.11.0
+
+[export:platform_machine == "aarch64" and platform_system == "Linux" and python_version >= "3.9"]
+packaging>=26.0
+
+[export:platform_machine == "aarch64" and python_version >= "3.9"]
+tensorstore>=0.1.63
+
+[export:platform_system != "Darwin"]
+onnx>=1.12.0
+
+[export:platform_system != "Windows" and python_version <= "3.13"]
+coremltools>=9.0
+scikit-learn>=1.3.2
+
+[export:platform_system == "Darwin"]
+onnx<1.18.0,>=1.12.0
+
+[extra]
+ipython
+albumentations>=1.4.6
+faster-coco-eval>=1.6.7
+
+[logging]
+wandb
+tensorboard
+mlflow
+
+[solutions]
+shapely>=2.0.0
+flask>=3.0.1
+
+[solutions:python_version < "3.10" and (python_version < "3.9" or platform_machine != "aarch64" or platform_system != "Linux")]
+streamlit<1.51.0,>=1.29.0
+
+[solutions:python_version >= "3.10"]
+streamlit>=1.51.0
+
+[typing]
+types-pillow
+types-psutil
+types-pyyaml
+types-requests
+types-shapely
+
+[typing:python_version >= "3.10"]
+scipy-stubs>=1.14.1.4

vendor/ultralytics.egg-info/top_level.txt

@@ -0,0 +1 @@
+ultralytics

vendor/ultralytics/__init__.py

@@ -0,0 +1,48 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+__version__ = "8.4.21"
+
+import importlib
+import os
+from typing import TYPE_CHECKING
+
+# Set ENV variables (place before imports)
+if not os.environ.get("OMP_NUM_THREADS"):
+    os.environ["OMP_NUM_THREADS"] = "1"  # default for reduced CPU utilization during training
+
+from ultralytics.utils import ASSETS, SETTINGS
+from ultralytics.utils.checks import check_yolo as checks
+from ultralytics.utils.downloads import download
+
+settings = SETTINGS
+
+MODELS = ("YOLO", "YOLOWorld", "YOLOE", "NAS", "SAM", "FastSAM", "RTDETR")
+
+__all__ = (
+    "__version__",
+    "ASSETS",
+    *MODELS,
+    "checks",
+    "download",
+    "settings",
+)
+
+if TYPE_CHECKING:
+    # Enable hints for type checkers
+    from ultralytics.models import YOLO, YOLOWorld, YOLOE, NAS, SAM, FastSAM, RTDETR  # noqa
+
+
+def __getattr__(name: str):
+    """Lazy-import model classes on first access."""
+    if name in MODELS:
+        return getattr(importlib.import_module("ultralytics.models"), name)
+    raise AttributeError(f"module {__name__} has no attribute {name}")
+
+
+def __dir__():
+    """Extend dir() to include lazily available model names for IDE autocompletion."""
+    return sorted(set(globals()) | set(MODELS))
+
+
+if __name__ == "__main__":
+    print(__version__)

vendor/ultralytics/cfg/__init__.py

@@ -0,0 +1,1039 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+import ast
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any
+
+from ultralytics import __version__
+from ultralytics.utils import (
+    ASSETS,
+    DEFAULT_CFG,
+    DEFAULT_CFG_DICT,
+    DEFAULT_CFG_PATH,
+    FLOAT_OR_INT,
+    IS_VSCODE,
+    LOGGER,
+    RANK,
+    ROOT,
+    RUNS_DIR,
+    SETTINGS,
+    SETTINGS_FILE,
+    STR_OR_PATH,
+    TESTS_RUNNING,
+    YAML,
+    IterableSimpleNamespace,
+    checks,
+    colorstr,
+    deprecation_warn,
+    vscode_msg,
+)
+
+# Define valid solutions
+SOLUTION_MAP = {
+    "count": "ObjectCounter",
+    "crop": "ObjectCropper",
+    "blur": "ObjectBlurrer",
+    "workout": "AIGym",
+    "heatmap": "Heatmap",
+    "isegment": "InstanceSegmentation",
+    "visioneye": "VisionEye",
+    "speed": "SpeedEstimator",
+    "queue": "QueueManager",
+    "analytics": "Analytics",
+    "inference": "Inference",
+    "trackzone": "TrackZone",
+    "help": None,
+}
+
+# Define valid tasks and modes
+MODES = frozenset({"train", "val", "predict", "export", "track", "benchmark"})
+TASKS = frozenset({"detect", "segment", "classify", "pose", "obb"})
+TASK2DATA = {
+    "detect": "coco8.yaml",
+    "segment": "coco8-seg.yaml",
+    "classify": "imagenet10",
+    "pose": "coco8-pose.yaml",
+    "obb": "dota8.yaml",
+}
+TASK2MODEL = {
+    "detect": "yolo26n.pt",
+    "segment": "yolo26n-seg.pt",
+    "classify": "yolo26n-cls.pt",
+    "pose": "yolo26n-pose.pt",
+    "obb": "yolo26n-obb.pt",
+}
+TASK2METRIC = {
+    "detect": "metrics/mAP50-95(B)",
+    "segment": "metrics/mAP50-95(M)",
+    "classify": "metrics/accuracy_top1",
+    "pose": "metrics/mAP50-95(P)",
+    "obb": "metrics/mAP50-95(B)",
+}
+
+ARGV = sys.argv or ["", ""]  # sometimes sys.argv = []
+SOLUTIONS_HELP_MSG = f"""
+    Arguments received: {["yolo", *ARGV[1:]]!s}. Ultralytics 'yolo solutions' usage overview:
+
+        yolo solutions SOLUTION ARGS
+
+        Where SOLUTION (optional) is one of {list(SOLUTION_MAP.keys())[:-1]}
+              ARGS (optional) are any number of custom 'arg=value' pairs like 'show_in=True' that override defaults
+                  at https://docs.ultralytics.com/usage/cfg
+
+    1. Call object counting solution
+        yolo solutions count source="path/to/video.mp4" region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]"
+
+    2. Call heatmap solution
+        yolo solutions heatmap colormap=cv2.COLORMAP_PARULA model=yolo26n.pt
+
+    3. Call queue management solution
+        yolo solutions queue region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]" model=yolo26n.pt
+
+    4. Call workout monitoring solution for push-ups
+        yolo solutions workout model=yolo26n-pose.pt kpts=[6, 8, 10]
+
+    5. Generate analytical graphs
+        yolo solutions analytics analytics_type="pie"
+
+    6. Track objects within specific zones
+        yolo solutions trackzone source="path/to/video.mp4" region="[(150, 150), (1130, 150), (1130, 570), (150, 570)]"
+
+    7. Streamlit real-time webcam inference GUI
+        yolo streamlit-predict
+    """
+CLI_HELP_MSG = f"""
+    Arguments received: {["yolo", *ARGV[1:]]!s}. Ultralytics 'yolo' commands use the following syntax:
+
+        yolo TASK MODE ARGS
+
+        Where   TASK (optional) is one of {list(TASKS)}
+                MODE (required) is one of {list(MODES)}
+                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
+                    See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
+
+    1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
+        yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
+
+    2. Predict a YouTube video using a pretrained segmentation model at image size 320:
+        yolo predict model=yolo26n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
+
+    3. Validate a pretrained detection model at batch-size 1 and image size 640:
+        yolo val model=yolo26n.pt data=coco8.yaml batch=1 imgsz=640
+
+    4. Export a YOLO26n classification model to ONNX format at image size 224 by 128 (no TASK required)
+        yolo export model=yolo26n-cls.pt format=onnx imgsz=224,128
+
+    5. Ultralytics solutions usage
+        yolo solutions count or any of {list(SOLUTION_MAP.keys())[1:-1]} source="path/to/video.mp4"
+
+    6. Run special commands:
+        yolo help
+        yolo checks
+        yolo version
+        yolo settings
+        yolo copy-cfg
+        yolo cfg
+        yolo solutions help
+
+    Docs: https://docs.ultralytics.com
+    Solutions: https://docs.ultralytics.com/solutions/
+    Community: https://community.ultralytics.com
+    GitHub: https://github.com/ultralytics/ultralytics
+    """
+
+# Define keys for arg type checks
+CFG_FLOAT_KEYS = frozenset(
+    {  # integer or float arguments, i.e. x=2 and x=2.0
+        "warmup_epochs",
+        "box",
+        "cls",
+        "dfl",
+        "degrees",
+        "shear",
+        "time",
+        "workspace",
+        "batch",
+        "gaussian_noise_mean",
+    }
+)
+CFG_FRACTION_KEYS = frozenset(
+    {  # fractional float arguments with 0.0<=values<=1.0
+        "dropout",
+        "lr0",
+        "lrf",
+        "momentum",
+        "weight_decay",
+        "warmup_momentum",
+        "warmup_bias_lr",
+        "hsv_h",
+        "hsv_s",
+        "hsv_v",
+        "translate",
+        "scale",
+        "perspective",
+        "flipud",
+        "fliplr",
+        "bgr",
+        "mosaic",
+        "mixup",
+        "cutmix",
+        "copy_paste",
+        "gaussian_noise_p",
+        "conf",
+        "iou",
+        "fraction",
+        "multi_scale",
+        "deeppcb_safe_fliplr",
+        "deeppcb_safe_flipud",
+        "deeppcb_safe_crop_p",
+        "deeppcb_safe_crop_min_frac",
+        "deeppcb_safe_translate",
+        "deeppcb_safe_scale",
+    }
+)
+CFG_INT_KEYS = frozenset(
+    {  # integer-only arguments
+        "epochs",
+        "patience",
+        "workers",
+        "seed",
+        "close_mosaic",
+        "mask_ratio",
+        "max_det",
+        "vid_stride",
+        "line_width",
+        "nbs",
+        "save_period",
+    }
+)
+CFG_BOOL_KEYS = frozenset(
+    {  # boolean-only arguments
+        "save",
+        "exist_ok",
+        "verbose",
+        "deterministic",
+        "single_cls",
+        "rect",
+        "cos_lr",
+        "overlap_mask",
+        "val",
+        "save_json",
+        "half",
+        "dnn",
+        "plots",
+        "show",
+        "save_txt",
+        "save_conf",
+        "save_crop",
+        "save_frames",
+        "show_labels",
+        "show_conf",
+        "visualize",
+        "augment",
+        "agnostic_nms",
+        "retina_masks",
+        "show_boxes",
+        "keras",
+        "optimize",
+        "int8",
+        "dynamic",
+        "simplify",
+        "nms",
+        "profile",
+        "end2end",
+        "prebackbone_input_only_non_geo_aug",
+        "isdeeppcb",
+        "deeppcb_safe_aug",
+        "augment_train",
+    }
+)
+
+
+def cfg2dict(cfg: str | Path | dict | SimpleNamespace) -> dict:
+    """Convert a configuration object to a dictionary.
+
+    Args:
+        cfg (str | Path | dict | SimpleNamespace): Configuration object to be converted. Can be a file path, a string, a
+            dictionary, or a SimpleNamespace object.
+
+    Returns:
+        (dict): Configuration object in dictionary format.
+
+    Examples:
+        Convert a YAML file path to a dictionary:
+        >>> config_dict = cfg2dict("config.yaml")
+
+        Convert a SimpleNamespace to a dictionary:
+        >>> from types import SimpleNamespace
+        >>> config_sn = SimpleNamespace(param1="value1", param2="value2")
+        >>> config_dict = cfg2dict(config_sn)
+
+        Pass through an already existing dictionary:
+        >>> config_dict = cfg2dict({"param1": "value1", "param2": "value2"})
+
+    Notes:
+        - If cfg is a path or string, it's loaded as YAML and converted to a dictionary.
+        - If cfg is a SimpleNamespace object, it's converted to a dictionary using vars().
+        - If cfg is already a dictionary, it's returned unchanged.
+    """
+    if isinstance(cfg, STR_OR_PATH):
+        cfg = YAML.load(cfg)  # load dict
+    elif isinstance(cfg, SimpleNamespace):
+        cfg = vars(cfg)  # convert to dict
+    return cfg
+
+
+def get_cfg(
+    cfg: str | Path | dict | SimpleNamespace = DEFAULT_CFG_DICT, overrides: dict | None = None
+) -> SimpleNamespace:
+    """Load and merge configuration data from a file or dictionary, with optional overrides.
+
+    Args:
+        cfg (str | Path | dict | SimpleNamespace): Configuration data source. Can be a file path, dictionary, or
+            SimpleNamespace object.
+        overrides (dict | None): Dictionary containing key-value pairs to override the base configuration.
+
+    Returns:
+        (SimpleNamespace): Namespace containing the merged configuration arguments.
+
+    Examples:
+        >>> from ultralytics.cfg import get_cfg
+        >>> config = get_cfg()  # Load default configuration
+        >>> config_with_overrides = get_cfg("path/to/config.yaml", overrides={"epochs": 50, "batch_size": 16})
+
+    Notes:
+        - If both `cfg` and `overrides` are provided, the values in `overrides` will take precedence.
+        - Special handling ensures alignment and correctness of the configuration, such as converting numeric
+          `project` and `name` to strings and validating configuration keys and values.
+        - The function performs type and value checks on the configuration data.
+    """
+    cfg = cfg2dict(cfg)
+
+    # Merge overrides
+    if overrides:
+        overrides = cfg2dict(overrides)
+        check_dict_alignment(cfg, overrides)
+        cfg = {**cfg, **overrides}  # merge cfg and overrides dicts (prefer overrides)
+
+    # Special handling for numeric project/name
+    for k in "project", "name":
+        if k in cfg and isinstance(cfg[k], FLOAT_OR_INT):
+            cfg[k] = str(cfg[k])
+    if cfg.get("name") == "model":  # assign model to 'name' arg
+        cfg["name"] = str(cfg.get("model", "")).partition(".")[0]
+        LOGGER.warning(f"'name=model' automatically updated to 'name={cfg['name']}'.")
+
+    # Type and Value checks
+    check_cfg(cfg)
+
+    # Return instance
+    return IterableSimpleNamespace(**cfg)
+
+
+def check_cfg(cfg: dict, hard: bool = True) -> None:
+    """Check configuration argument types and values for the Ultralytics library.
+
+    This function validates the types and values of configuration arguments, ensuring correctness and converting them if
+    necessary. It checks for specific key types defined in global variables such as `CFG_FLOAT_KEYS`,
+    `CFG_FRACTION_KEYS`, `CFG_INT_KEYS`, and `CFG_BOOL_KEYS`.
+
+    Args:
+        cfg (dict): Configuration dictionary to validate.
+        hard (bool): If True, raises exceptions for invalid types and values; if False, attempts to convert them.
+
+    Examples:
+        >>> config = {
+        ...     "epochs": 50,  # valid integer
+        ...     "lr0": 0.01,  # valid float
+        ...     "momentum": 1.2,  # invalid float (out of 0.0-1.0 range)
+        ...     "save": "true",  # invalid bool
+        ... }
+        >>> check_cfg(config, hard=False)
+        >>> print(config)
+        {'epochs': 50, 'lr0': 0.01, 'momentum': 1.2, 'save': False}  # corrected 'save' key
+
+    Notes:
+        - The function modifies the input dictionary in-place.
+        - None values are ignored as they may be from optional arguments.
+        - Fraction keys are checked to be within the range [0.0, 1.0].
+    """
+    for k, v in cfg.items():
+        if v is not None:  # None values may be from optional args
+            if k in CFG_FLOAT_KEYS and not isinstance(v, FLOAT_OR_INT):
+                if hard:
+                    raise TypeError(
+                        f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                        f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
+                    )
+                cfg[k] = float(v)
+            elif k in CFG_FRACTION_KEYS:
+                if not isinstance(v, FLOAT_OR_INT):
+                    if hard:
+                        raise TypeError(
+                            f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                            f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
+                        )
+                    cfg[k] = v = float(v)
+                if not (0.0 <= v <= 1.0):
+                    raise ValueError(f"'{k}={v}' is an invalid value. Valid '{k}' values are between 0.0 and 1.0.")
+            elif k in CFG_INT_KEYS and not isinstance(v, int):
+                if hard:
+                    raise TypeError(
+                        f"'{k}={v}' is of invalid type {type(v).__name__}. '{k}' must be an int (i.e. '{k}=8')"
+                    )
+                cfg[k] = int(v)
+            elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
+                if hard:
+                    raise TypeError(
+                        f"'{k}={v}' is of invalid type {type(v).__name__}. "
+                        f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')"
+                    )
+                cfg[k] = bool(v)
+
+
+def get_save_dir(args: SimpleNamespace, name: str | None = None) -> Path:
+    """Return the directory path for saving outputs, derived from arguments or default settings.
+
+    Args:
+        args (SimpleNamespace): Namespace object containing configurations such as 'project', 'name', 'task', 'mode',
+            and 'save_dir'.
+        name (str | None): Optional name for the output directory. If not provided, it defaults to 'args.name' or the
+            'args.mode'.
+
+    Returns:
+        (Path): Directory path where outputs should be saved.
+
+    Examples:
+        >>> from types import SimpleNamespace
+        >>> args = SimpleNamespace(project="my_project", task="detect", mode="train", exist_ok=True)
+        >>> save_dir = get_save_dir(args)
+        >>> print(save_dir)
+        runs/detect/my_project/train
+    """
+    if getattr(args, "save_dir", None):
+        save_dir = args.save_dir
+    else:
+        from ultralytics.utils.files import increment_path
+
+        project = args.project or ""
+        if not Path(project).is_absolute():
+            project = (ROOT.parent / "tests/tmp/runs" if TESTS_RUNNING else RUNS_DIR) / args.task / project
+        name = name or args.name or f"{args.mode}"
+        save_dir = increment_path(Path(project) / name, exist_ok=args.exist_ok if RANK in {-1, 0} else True)
+
+    return Path(save_dir).resolve()  # resolve to display full path in console
+
+
+def _handle_deprecation(custom: dict) -> dict:
+    """Handle deprecated configuration keys by mapping them to current equivalents with deprecation warnings.
+
+    Args:
+        custom (dict): Configuration dictionary potentially containing deprecated keys.
+
+    Returns:
+        (dict): Updated configuration dictionary with deprecated keys replaced.
+
+    Examples:
+        >>> custom_config = {"boxes": True, "hide_labels": "False", "line_thickness": 2}
+        >>> _handle_deprecation(custom_config)
+        >>> print(custom_config)
+        {'show_boxes': True, 'show_labels': True, 'line_width': 2}
+
+    Notes:
+        This function modifies the input dictionary in-place, replacing deprecated keys with their current
+        equivalents. It also handles value conversions where necessary, such as inverting boolean values for
+        'hide_labels' and 'hide_conf'.
+    """
+    deprecated_mappings = {
+        "boxes": ("show_boxes", lambda v: v),
+        "hide_labels": ("show_labels", lambda v: not bool(v)),
+        "hide_conf": ("show_conf", lambda v: not bool(v)),
+        "line_thickness": ("line_width", lambda v: v),
+    }
+    removed_keys = {"label_smoothing", "save_hybrid", "crop_fraction"}
+
+    for old_key, (new_key, transform) in deprecated_mappings.items():
+        if old_key not in custom:
+            continue
+        deprecation_warn(old_key, new_key)
+        custom[new_key] = transform(custom.pop(old_key))
+
+    for key in removed_keys:
+        if key not in custom:
+            continue
+        deprecation_warn(key)
+        custom.pop(key)
+
+    return custom
+
+
+def check_dict_alignment(
+    base: dict, custom: dict, e: Exception | None = None, allowed_custom_keys: set | None = None
+) -> None:
+    """Check alignment between custom and base configuration dictionaries, handling deprecated keys and providing error
+    messages for mismatched keys.
+
+    Args:
+        base (dict): The base configuration dictionary containing valid keys.
+        custom (dict): The custom configuration dictionary to be checked for alignment.
+        e (Exception | None): Optional error instance passed by the calling function.
+        allowed_custom_keys (set | None): Optional set of additional keys that are allowed in the custom dictionary.
+
+    Raises:
+        SystemExit: If mismatched keys are found between the custom and base dictionaries.
+
+    Examples:
+        >>> base_cfg = {"epochs": 50, "lr0": 0.01, "batch_size": 16}
+        >>> custom_cfg = {"epoch": 100, "lr": 0.02, "batch_size": 32}
+        >>> try:
+        ...     check_dict_alignment(base_cfg, custom_cfg)
+        ... except SystemExit:
+        ...     print("Mismatched keys found")
+
+    Notes:
+        - Suggests corrections for mismatched keys based on similarity to valid keys.
+        - Automatically replaces deprecated keys in the custom configuration with updated equivalents.
+        - Prints detailed error messages for each mismatched key to help users correct their configurations.
+    """
+    custom = _handle_deprecation(custom)
+    base_keys, custom_keys = (frozenset(x.keys()) for x in (base, custom))
+    # Allow 'augmentations' as a valid custom parameter for custom Albumentations transforms
+    if allowed_custom_keys is None:
+        allowed_custom_keys = {"augmentations", "save_dir"}
+    if mismatched := [k for k in custom_keys if k not in base_keys and k not in allowed_custom_keys]:
+        from difflib import get_close_matches
+
+        string = ""
+        for x in mismatched:
+            matches = get_close_matches(x, base_keys)  # key list
+            matches = [f"{k}={base[k]}" if base.get(k) is not None else k for k in matches]
+            match_str = f"Similar arguments are i.e. {matches}." if matches else ""
+            string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
+        raise SyntaxError(string + CLI_HELP_MSG) from e
+
+
+def merge_equals_args(args: list[str]) -> list[str]:
+    """Merge arguments around isolated '=' in a list of strings and join fragments with brackets.
+
+    This function handles the following cases:
+        1. ['arg', '=', 'val'] becomes ['arg=val']
+        2. ['arg=', 'val'] becomes ['arg=val']
+        3. ['arg', '=val'] becomes ['arg=val']
+        4. Joins fragments with brackets, e.g., ['imgsz=[3,', '640,', '640]'] becomes ['imgsz=[3,640,640]']
+
+    Args:
+        args (list[str]): A list of strings where each element represents an argument or fragment.
+
+    Returns:
+        (list[str]): A list of strings where the arguments around isolated '=' are merged and fragments with brackets
+            are joined.
+
+    Examples:
+        >>> args = ["arg1", "=", "value", "arg2=", "value2", "arg3", "=value3", "imgsz=[3,", "640,", "640]"]
+        >>> merge_equals_args(args)
+        ['arg1=value', 'arg2=value2', 'arg3=value3', 'imgsz=[3,640,640]']
+    """
+    new_args = []
+    current = ""
+    depth = 0
+
+    i = 0
+    while i < len(args):
+        arg = args[i]
+
+        # Handle equals sign merging
+        if arg == "=" and 0 < i < len(args) - 1:  # merge ['arg', '=', 'val']
+            new_args[-1] += f"={args[i + 1]}"
+            i += 2
+            continue
+        elif arg.endswith("=") and i < len(args) - 1 and "=" not in args[i + 1]:  # merge ['arg=', 'val']
+            new_args.append(f"{arg}{args[i + 1]}")
+            i += 2
+            continue
+        elif arg.startswith("=") and i > 0:  # merge ['arg', '=val']
+            new_args[-1] += arg
+            i += 1
+            continue
+
+        # Handle bracket joining
+        depth += arg.count("[") - arg.count("]")
+        current += arg
+        if depth == 0:
+            new_args.append(current)
+            current = ""
+
+        i += 1
+
+    # Append any remaining current string
+    if current:
+        new_args.append(current)
+
+    return new_args
+
+
+def handle_yolo_hub(args: list[str]) -> None:
+    """Handle Ultralytics HUB command-line interface (CLI) commands for authentication.
+
+    This function processes Ultralytics HUB CLI commands such as login and logout. It should be called when executing a
+    script with arguments related to HUB authentication.
+
+    Args:
+        args (list[str]): A list of command line arguments. The first argument should be either 'login' or 'logout'. For
+            'login', an optional second argument can be the API key.
+
+    Examples:
+        $ yolo login YOUR_API_KEY
+
+    Notes:
+        - The function imports the 'hub' module from ultralytics to perform login and logout operations.
+        - For the 'login' command, if no API key is provided, an empty string is passed to the login function.
+        - The 'logout' command does not require any additional arguments.
+    """
+    from ultralytics import hub
+
+    if args[0] == "login":
+        key = args[1] if len(args) > 1 else ""
+        # Log in to Ultralytics HUB using the provided API key
+        hub.login(key)
+    elif args[0] == "logout":
+        # Log out from Ultralytics HUB
+        hub.logout()
+
+
+def handle_yolo_settings(args: list[str]) -> None:
+    """Handle YOLO settings command-line interface (CLI) commands.
+
+    This function processes YOLO settings CLI commands such as reset and updating individual settings. It should be
+    called when executing a script with arguments related to YOLO settings management.
+
+    Args:
+        args (list[str]): A list of command line arguments for YOLO settings management.
+
+    Examples:
+        >>> handle_yolo_settings(["reset"])  # Reset YOLO settings
+        >>> handle_yolo_settings(["default_cfg_path=yolo26n.yaml"])  # Update a specific setting
+
+    Notes:
+        - If no arguments are provided, the function will display the current settings.
+        - The 'reset' command will delete the existing settings file and create new default settings.
+        - Other arguments are treated as key-value pairs to update specific settings.
+        - The function will check for alignment between the provided settings and the existing ones.
+        - After processing, the updated settings will be displayed.
+        - For more information on handling YOLO settings, visit:
+          https://docs.ultralytics.com/quickstart/#ultralytics-settings
+    """
+    url = "https://docs.ultralytics.com/quickstart/#ultralytics-settings"  # help URL
+    try:
+        if any(args):
+            if args[0] == "reset":
+                SETTINGS_FILE.unlink()  # delete the settings file
+                SETTINGS.reset()  # create new settings
+                LOGGER.info("Settings reset successfully")  # inform the user that settings have been reset
+            else:  # save a new setting
+                new = dict(parse_key_value_pair(a) for a in args)
+                check_dict_alignment(SETTINGS, new)
+                SETTINGS.update(new)
+                for k, v in new.items():
+                    LOGGER.info(f"✅ Updated '{k}={v}'")
+
+        LOGGER.info(SETTINGS)  # print the current settings
+        LOGGER.info(f"💡 Learn more about Ultralytics Settings at {url}")
+    except Exception as e:
+        LOGGER.warning(f"settings error: '{e}'. Please see {url} for help.")
+
+
+def handle_yolo_solutions(args: list[str]) -> None:
+    """Process YOLO solutions arguments and run the specified computer vision solutions pipeline.
+
+    Args:
+        args (list[str]): Command-line arguments for configuring and running the Ultralytics YOLO solutions.
+
+    Examples:
+        Run people counting solution with default settings:
+        >>> handle_yolo_solutions(["count"])
+
+        Run analytics with custom configuration:
+        >>> handle_yolo_solutions(["analytics", "conf=0.25", "source=path/to/video.mp4"])
+
+        Run inference with custom configuration, requires Streamlit version 1.29.0 or higher.
+        >>> handle_yolo_solutions(["inference", "model=yolo26n.pt"])
+
+    Notes:
+        - Arguments can be provided in the format 'key=value' or as boolean flags
+        - Available solutions are defined in SOLUTION_MAP with their respective classes and methods
+        - If an invalid solution is provided, defaults to 'count' solution
+        - Output videos are saved in 'runs/solution/{solution_name}' directory
+        - For 'analytics' solution, frame numbers are tracked for generating analytical graphs
+        - Video processing can be interrupted by pressing 'q'
+        - Processes video frames sequentially and saves output in .avi format
+        - If no source is specified, downloads and uses a default sample video
+        - The inference solution will be launched using the 'streamlit run' command.
+        - The Streamlit app file is located in the Ultralytics package directory.
+    """
+    from ultralytics.solutions.config import SolutionConfig
+
+    full_args_dict = vars(SolutionConfig())  # arguments dictionary
+    overrides = {}
+
+    # check dictionary alignment
+    for arg in merge_equals_args(args):
+        arg = arg.lstrip("-").rstrip(",")
+        if "=" in arg:
+            try:
+                k, v = parse_key_value_pair(arg)
+                overrides[k] = v
+            except (NameError, SyntaxError, ValueError, AssertionError) as e:
+                check_dict_alignment(full_args_dict, {arg: ""}, e)
+        elif arg in full_args_dict and isinstance(full_args_dict.get(arg), bool):
+            overrides[arg] = True
+    check_dict_alignment(full_args_dict, overrides)  # dict alignment
+
+    # Get solution name
+    if not args:
+        LOGGER.warning("No solution name provided. i.e `yolo solutions count`. Defaulting to 'count'.")
+        args = ["count"]
+    if args[0] == "help":
+        LOGGER.info(SOLUTIONS_HELP_MSG)
+        return  # Early return for 'help' case
+    elif args[0] in SOLUTION_MAP:
+        solution_name = args.pop(0)  # Extract the solution name directly
+    else:
+        LOGGER.warning(
+            f"❌ '{args[0]}' is not a valid solution. 💡 Defaulting to 'count'.\n"
+            f"🚀 Available solutions: {', '.join(list(SOLUTION_MAP.keys())[:-1])}\n"
+        )
+        solution_name = "count"  # Default for invalid solution
+
+    if solution_name == "inference":
+        checks.check_requirements("streamlit>=1.29.0")
+        LOGGER.info("💡 Loading Ultralytics live inference app...")
+        subprocess.run(
+            [  # Run subprocess with Streamlit custom argument
+                "streamlit",
+                "run",
+                str(ROOT / "solutions/streamlit_inference.py"),
+                "--server.headless",
+                "true",
+                overrides.pop("model", "yolo26n.pt"),
+            ]
+        )
+    else:
+        import cv2  # Only needed for cap and vw functionality
+
+        from ultralytics import solutions
+
+        solution = getattr(solutions, SOLUTION_MAP[solution_name])(is_cli=True, **overrides)  # class i.e. ObjectCounter
+
+        cap = cv2.VideoCapture(solution.CFG["source"])  # read the video file
+        if solution_name != "crop":
+            # extract width, height and fps of the video file, create save directory and initialize video writer
+            w, h, fps = (
+                int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)
+            )
+            if solution_name == "analytics":  # analytical graphs follow fixed shape for output i.e w=1920, h=1080
+                w, h = 1280, 720
+            save_dir = get_save_dir(SimpleNamespace(task="solutions", name="exp", exist_ok=False, project=None))
+            save_dir.mkdir(parents=True, exist_ok=True)  # create the output directory i.e. runs/solutions/exp
+            vw = cv2.VideoWriter(str(save_dir / f"{solution_name}.avi"), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+
+        try:  # Process video frames
+            f_n = 0  # frame number, required for analytical graphs
+            while cap.isOpened():
+                success, frame = cap.read()
+                if not success:
+                    break
+                results = solution(frame, f_n := f_n + 1) if solution_name == "analytics" else solution(frame)
+                if solution_name != "crop":
+                    vw.write(results.plot_im)
+                if solution.CFG["show"] and cv2.waitKey(1) & 0xFF == ord("q"):
+                    break
+        finally:
+            cap.release()
+
+
+def parse_key_value_pair(pair: str = "key=value") -> tuple:
+    """Parse a key-value pair string into separate key and value components.
+
+    Args:
+        pair (str): A string containing a key-value pair in the format "key=value".
+
+    Returns:
+        key (str): The parsed key.
+        value (str): The parsed value.
+
+    Raises:
+        AssertionError: If the value is missing or empty.
+
+    Examples:
+        >>> key, value = parse_key_value_pair("model=yolo26n.pt")
+        >>> print(f"Key: {key}, Value: {value}")
+        Key: model, Value: yolo26n.pt
+
+        >>> key, value = parse_key_value_pair("epochs=100")
+        >>> print(f"Key: {key}, Value: {value}")
+        Key: epochs, Value: 100
+
+    Notes:
+        - The function splits the input string on the first '=' character.
+        - Leading and trailing whitespace is removed from both key and value.
+        - An assertion error is raised if the value is empty after stripping.
+    """
+    k, v = pair.split("=", 1)  # split on first '=' sign
+    k, v = k.strip(), v.strip()  # remove spaces
+    assert v, f"missing '{k}' value"
+    return k, smart_value(v)
+
+
+def smart_value(v: str) -> Any:
+    """Convert a string representation of a value to its appropriate Python type.
+
+    This function attempts to convert a given string into a Python object of the most appropriate type. It handles
+    conversions to None, bool, int, float, and other types that can be evaluated safely.
+
+    Args:
+        v (str): The string representation of the value to be converted.
+
+    Returns:
+        (Any): The converted value. The type can be None, bool, int, float, or the original string if no conversion is
+            applicable.
+
+    Examples:
+        >>> smart_value("42")
+        42
+        >>> smart_value("3.14")
+        3.14
+        >>> smart_value("True")
+        True
+        >>> smart_value("None")
+        None
+        >>> smart_value("some_string")
+        'some_string'
+
+    Notes:
+        - The function uses a case-insensitive comparison for boolean and None values.
+        - For other types, it attempts to use Python's ast.literal_eval() function for safe evaluation.
+        - If no conversion is possible, the original string is returned.
+    """
+    v_lower = v.lower()
+    if v_lower == "none":
+        return None
+    elif v_lower == "true":
+        return True
+    elif v_lower == "false":
+        return False
+    else:
+        try:
+            return ast.literal_eval(v)
+        except Exception:
+            return v
+
+
+def entrypoint(debug: str = "") -> None:
+    """Ultralytics entrypoint function for parsing and executing command-line arguments.
+
+    This function serves as the main entry point for the Ultralytics CLI, parsing command-line arguments and executing
+    the corresponding tasks such as training, validation, prediction, exporting models, and more.
+
+    Args:
+        debug (str): Space-separated string of command-line arguments for debugging purposes.
+
+    Examples:
+        Train a detection model for 10 epochs with an initial learning_rate of 0.01:
+        >>> entrypoint("train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01")
+
+        Predict a YouTube video using a pretrained segmentation model at image size 320:
+        >>> entrypoint("predict model=yolo26n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320")
+
+        Validate a pretrained detection model at batch-size 1 and image size 640:
+        >>> entrypoint("val model=yolo26n.pt data=coco8.yaml batch=1 imgsz=640")
+
+    Notes:
+        - If no arguments are passed, the function will display the usage help message.
+        - For a list of all available commands and their arguments, see the provided help messages and the
+          Ultralytics documentation at https://docs.ultralytics.com.
+    """
+    args = (debug.split(" ") if debug else ARGV)[1:]
+    if not args:  # no arguments passed
+        LOGGER.info(CLI_HELP_MSG)
+        return
+
+    special = {
+        "checks": checks.collect_system_info,
+        "version": lambda: LOGGER.info(__version__),
+        "settings": lambda: handle_yolo_settings(args[1:]),
+        "cfg": lambda: YAML.print(DEFAULT_CFG_PATH),
+        "hub": lambda: handle_yolo_hub(args[1:]),
+        "login": lambda: handle_yolo_hub(args),
+        "logout": lambda: handle_yolo_hub(args),
+        "copy-cfg": copy_default_cfg,
+        "solutions": lambda: handle_yolo_solutions(args[1:]),
+        "help": lambda: LOGGER.info(CLI_HELP_MSG),  # help below hub for -h flag precedence
+    }
+    full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
+
+    # Define common misuses of special commands, i.e. -h, -help, --help
+    special.update({k[0]: v for k, v in special.items()})  # singular
+    special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith("s")})  # singular
+    special = {**special, **{f"-{k}": v for k, v in special.items()}, **{f"--{k}": v for k, v in special.items()}}
+
+    overrides = {}  # basic overrides, i.e. imgsz=320
+    for a in merge_equals_args(args):  # merge spaces around '=' sign
+        if a.startswith("--"):
+            LOGGER.warning(f"argument '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
+            a = a[2:]
+        if a.endswith(","):
+            LOGGER.warning(f"argument '{a}' does not require trailing comma ',', updating to '{a[:-1]}'.")
+            a = a[:-1]
+        if "=" in a:
+            try:
+                k, v = parse_key_value_pair(a)
+                if k == "cfg" and v is not None:  # custom.yaml passed
+                    LOGGER.info(f"Overriding {DEFAULT_CFG_PATH} with {v}")
+                    overrides = {k: val for k, val in YAML.load(checks.check_yaml(v)).items() if k != "cfg"}
+                else:
+                    overrides[k] = v
+            except (NameError, SyntaxError, ValueError, AssertionError) as e:
+                check_dict_alignment(full_args_dict, {a: ""}, e)
+
+        elif a in TASKS:
+            overrides["task"] = a
+        elif a in MODES:
+            overrides["mode"] = a
+        elif a.lower() in special:
+            special[a.lower()]()
+            return
+        elif a in DEFAULT_CFG_DICT and isinstance(DEFAULT_CFG_DICT[a], bool):
+            overrides[a] = True  # auto-True for default bool args, i.e. 'yolo show' sets show=True
+        elif a in DEFAULT_CFG_DICT:
+            raise SyntaxError(
+                f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
+                f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}"
+            )
+        else:
+            check_dict_alignment(full_args_dict, {a: ""})
+
+    # Check keys
+    check_dict_alignment(full_args_dict, overrides)
+
+    # Mode
+    mode = overrides.get("mode")
+    if mode is None:
+        mode = DEFAULT_CFG.mode or "predict"
+        LOGGER.warning(f"'mode' argument is missing. Valid modes are {list(MODES)}. Using default 'mode={mode}'.")
+    elif mode not in MODES:
+        raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {list(MODES)}.\n{CLI_HELP_MSG}")
+
+    # Task
+    task = overrides.pop("task", None)
+    if task:
+        if task not in TASKS:
+            if task == "track":
+                LOGGER.warning(
+                    f"invalid 'task=track', setting 'task=detect' and 'mode=track'. Valid tasks are {list(TASKS)}.\n{CLI_HELP_MSG}."
+                )
+                task, mode = "detect", "track"
+            else:
+                raise ValueError(f"Invalid 'task={task}'. Valid tasks are {list(TASKS)}.\n{CLI_HELP_MSG}")
+        if "model" not in overrides:
+            overrides["model"] = TASK2MODEL[task]
+
+    # Model
+    model = overrides.pop("model", DEFAULT_CFG.model)
+    if model is None:
+        model = "yolo26n.pt"
+        LOGGER.warning(f"'model' argument is missing. Using default 'model={model}'.")
+    overrides["model"] = model
+    stem = Path(model).stem.lower()
+    if "rtdetr" in stem:  # guess architecture
+        from ultralytics import RTDETR
+
+        model = RTDETR(model)  # no task argument
+    elif "fastsam" in stem:
+        from ultralytics import FastSAM
+
+        model = FastSAM(model)
+    elif "sam_" in stem or "sam2_" in stem or "sam2.1_" in stem:
+        from ultralytics import SAM
+
+        model = SAM(model)
+    else:
+        from ultralytics import YOLO
+
+        model = YOLO(model, task=task)
+        if "yoloe" in stem or "world" in stem:
+            cls_list = overrides.pop("classes", DEFAULT_CFG.classes)
+            if cls_list is not None and isinstance(cls_list, str):
+                model.set_classes(cls_list.split(","))  # convert "person, bus" -> ['person', ' bus'].
+    # Task Update
+    if task != model.task:
+        if task:
+            LOGGER.warning(
+                f"conflicting 'task={task}' passed with 'task={model.task}' model. "
+                f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model."
+            )
+        task = model.task
+
+    # Mode
+    if mode in {"predict", "track"} and "source" not in overrides:
+        overrides["source"] = (
+            "https://ultralytics.com/images/boats.jpg" if task == "obb" else DEFAULT_CFG.source or ASSETS
+        )
+        LOGGER.warning(f"'source' argument is missing. Using default 'source={overrides['source']}'.")
+    elif mode in {"train", "val"}:
+        if "data" not in overrides and "resume" not in overrides:
+            overrides["data"] = DEFAULT_CFG.data or TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
+            LOGGER.warning(f"'data' argument is missing. Using default 'data={overrides['data']}'.")
+    elif mode == "export":
+        if "format" not in overrides:
+            overrides["format"] = DEFAULT_CFG.format or "torchscript"
+            LOGGER.warning(f"'format' argument is missing. Using default 'format={overrides['format']}'.")
+
+    # Run command in python
+    getattr(model, mode)(**overrides)  # default args from model
+
+    # Show help
+    LOGGER.info(f"💡 Learn more at https://docs.ultralytics.com/modes/{mode}")
+
+    # Recommend VS Code extension
+    if IS_VSCODE and SETTINGS.get("vscode_msg", True):
+        LOGGER.info(vscode_msg())
+
+
+# Special modes --------------------------------------------------------------------------------------------------------
+def copy_default_cfg() -> None:
+    """Copy the default configuration file and create a new one with '_copy' appended to its name.
+
+    This function duplicates the existing default configuration file (DEFAULT_CFG_PATH) and saves it with '_copy'
+    appended to its name in the current working directory. It provides a convenient way to create a custom configuration
+    file based on the default settings.
+
+    Examples:
+        >>> copy_default_cfg()
+        # Output: default.yaml copied to /path/to/current/directory/default_copy.yaml
+        # Example YOLO command with this new custom cfg:
+        #   yolo cfg='/path/to/current/directory/default_copy.yaml' imgsz=320 batch=8
+
+    Notes:
+        - The new configuration file is created in the current working directory.
+        - After copying, the function prints a message with the new file's location and an example
+          YOLO command demonstrating how to use the new configuration file.
+        - This function is useful for users who want to modify the default configuration without
+          altering the original file.
+    """
+    new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")
+    shutil.copy2(DEFAULT_CFG_PATH, new_file)
+    LOGGER.info(
+        f"{DEFAULT_CFG_PATH} copied to {new_file}\n"
+        f"Example YOLO command with this new custom cfg:\n    yolo cfg='{new_file}' imgsz=320 batch=8"
+    )
+
+
+if __name__ == "__main__":
+    # Example: entrypoint(debug='yolo predict model=yolo26n.pt')
+    entrypoint(debug="")

vendor/ultralytics/cfg/datasets/Argoverse.yaml

@@ -0,0 +1,78 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Argoverse-HD dataset (ring-front-center camera) by Argo AI: https://www.cs.cmu.edu/~mengtial/proj/streaming/
+# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
+# Example usage: yolo train data=Argoverse.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── Argoverse ← downloads here (31.5 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: bus
+  5: truck
+  6: traffic_light
+  7: stop_sign
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  from pathlib import Path
+
+  from ultralytics.utils import TQDM
+  from ultralytics.utils.downloads import download
+
+  def argoverse2yolo(annotation_file):
+      """Convert Argoverse dataset annotations to YOLO format for object detection tasks."""
+      labels = {}
+      with open(annotation_file, encoding="utf-8") as f:
+          a = json.load(f)
+      for annot in TQDM(a["annotations"], desc=f"Converting {annotation_file} to YOLO format..."):
+          img_id = annot["image_id"]
+          img_name = a["images"][img_id]["name"]
+          img_label_name = f"{Path(img_name).stem}.txt"
+
+          cls = annot["category_id"]  # instance class id
+          x_center, y_center, width, height = annot["bbox"]
+          x_center = (x_center + width / 2) / 1920.0  # offset and scale
+          y_center = (y_center + height / 2) / 1200.0  # offset and scale
+          width /= 1920.0  # scale
+          height /= 1200.0  # scale
+
+          img_dir = annotation_file.parents[2] / "Argoverse-1.1" / "labels" / a["seq_dirs"][a["images"][annot["image_id"]]["sid"]]
+          if not img_dir.exists():
+              img_dir.mkdir(parents=True, exist_ok=True)
+
+          k = str(img_dir / img_label_name)
+          if k not in labels:
+              labels[k] = []
+          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+      for k in labels:
+          with open(k, "w", encoding="utf-8") as f:
+              f.writelines(labels[k])
+
+
+  # Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = ["https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link"]
+  print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
+  print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
+  # download(urls, dir=dir)
+
+  # Convert
+  annotations_dir = "Argoverse-HD/annotations/"
+  (dir / "Argoverse-1.1" / "tracking").rename(dir / "Argoverse-1.1" / "images")  # rename 'tracking' to 'images'
+  for d in "train.json", "val.json":
+      argoverse2yolo(dir / annotations_dir / d)  # convert Argoverse annotations to YOLO labels

vendor/ultralytics/cfg/datasets/DOTAv1.5.yaml

@@ -0,0 +1,37 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA 1.5 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.5.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota1.5 ← downloads here (2 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: DOTAv1.5 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.5
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+  15: container crane
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.5.zip

vendor/ultralytics/cfg/datasets/DOTAv1.yaml

@@ -0,0 +1,36 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota1 ← downloads here (2 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: DOTAv1 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.zip

vendor/ultralytics/cfg/datasets/GlobalWheat2020.yaml

@@ -0,0 +1,68 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
+# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
+# Example usage: yolo train data=GlobalWheat2020.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── GlobalWheat2020 ← downloads here (7.0 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
+
+# Classes
+names:
+  0: wheat_head
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from pathlib import Path
+
+  from ultralytics.utils.downloads import download
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [
+      "https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip",
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip",
+  ]
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in "annotations", "images", "labels":
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in (
+      "arvalis_1",
+      "arvalis_2",
+      "arvalis_3",
+      "ethz_1",
+      "rres_1",
+      "inrae_1",
+      "usask_1",
+      "utokyo_1",
+      "utokyo_2",
+      "nau_1",
+      "uq_1",
+  ):
+      (dir / "global-wheat-codalab-official" / p).rename(dir / "images" / p)  # move to /images
+      f = (dir / "global-wheat-codalab-official" / p).with_suffix(".json")  # json file
+      if f.exists():
+          f.rename((dir / "annotations" / p).with_suffix(".json"))  # move to /annotations

vendor/ultralytics/cfg/datasets/HomeObjects-3K.yaml

@@ -0,0 +1,32 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# HomeObjects-3K dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/homeobjects-3k/
+# Example usage: yolo train data=HomeObjects-3K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── homeobjects-3K ← downloads here (390 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: homeobjects-3K # dataset root dir
+train: images/train # train images (relative to 'path') 2285 images
+val: images/val # val images (relative to 'path') 404 images
+
+# Classes
+names:
+  0: bed
+  1: sofa
+  2: chair
+  3: table
+  4: lamp
+  5: tv
+  6: laptop
+  7: wardrobe
+  8: window
+  9: door
+  10: potted plant
+  11: photo frame
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/homeobjects-3K.zip

vendor/ultralytics/cfg/datasets/ImageNet.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Documentation: https://docs.ultralytics.com/datasets/classify/imagenet/
+# Example usage: yolo train task=classify data=imagenet
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── imagenet ← downloads here (144 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: imagenet # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: tench
+  1: goldfish
+  2: great white shark
+  3: tiger shark
+  4: hammerhead shark
+  5: electric ray
+  6: stingray
+  7: cock
+  8: hen
+  9: ostrich
+  10: brambling
+  11: goldfinch
+  12: house finch
+  13: junco
+  14: indigo bunting
+  15: American robin
+  16: bulbul
+  17: jay
+  18: magpie
+  19: chickadee
+  20: American dipper
+  21: kite
+  22: bald eagle
+  23: vulture
+  24: great grey owl
+  25: fire salamander
+  26: smooth newt
+  27: newt
+  28: spotted salamander
+  29: axolotl
+  30: American bullfrog
+  31: tree frog
+  32: tailed frog
+  33: loggerhead sea turtle
+  34: leatherback sea turtle
+  35: mud turtle
+  36: terrapin
+  37: box turtle
+  38: banded gecko
+  39: green iguana
+  40: Carolina anole
+  41: desert grassland whiptail lizard
+  42: agama
+  43: frilled-necked lizard
+  44: alligator lizard
+  45: Gila monster
+  46: European green lizard
+  47: chameleon
+  48: Komodo dragon
+  49: Nile crocodile
+  50: American alligator
+  51: triceratops
+  52: worm snake
+  53: ring-necked snake
+  54: eastern hog-nosed snake
+  55: smooth green snake
+  56: kingsnake
+  57: garter snake
+  58: water snake
+  59: vine snake
+  60: night snake
+  61: boa constrictor
+  62: African rock python
+  63: Indian cobra
+  64: green mamba
+  65: sea snake
+  66: Saharan horned viper
+  67: eastern diamondback rattlesnake
+  68: sidewinder
+  69: trilobite
+  70: harvestman
+  71: scorpion
+  72: yellow garden spider
+  73: barn spider
+  74: European garden spider
+  75: southern black widow
+  76: tarantula
+  77: wolf spider
+  78: tick
+  79: centipede
+  80: black grouse
+  81: ptarmigan
+  82: ruffed grouse
+  83: prairie grouse
+  84: peacock
+  85: quail
+  86: partridge
+  87: grey parrot
+  88: macaw
+  89: sulphur-crested cockatoo
+  90: lorikeet
+  91: coucal
+  92: bee eater
+  93: hornbill
+  94: hummingbird
+  95: jacamar
+  96: toucan
+  97: duck
+  98: red-breasted merganser
+  99: goose
+  100: black swan
+  101: tusker
+  102: echidna
+  103: platypus
+  104: wallaby
+  105: koala
+  106: wombat
+  107: jellyfish
+  108: sea anemone
+  109: brain coral
+  110: flatworm
+  111: nematode
+  112: conch
+  113: snail
+  114: slug
+  115: sea slug
+  116: chiton
+  117: chambered nautilus
+  118: Dungeness crab
+  119: rock crab
+  120: fiddler crab
+  121: red king crab
+  122: American lobster
+  123: spiny lobster
+  124: crayfish
+  125: hermit crab
+  126: isopod
+  127: white stork
+  128: black stork
+  129: spoonbill
+  130: flamingo
+  131: little blue heron
+  132: great egret
+  133: bittern
+  134: crane (bird)
+  135: limpkin
+  136: common gallinule
+  137: American coot
+  138: bustard
+  139: ruddy turnstone
+  140: dunlin
+  141: common redshank
+  142: dowitcher
+  143: oystercatcher
+  144: pelican
+  145: king penguin
+  146: albatross
+  147: grey whale
+  148: killer whale
+  149: dugong
+  150: sea lion
+  151: Chihuahua
+  152: Japanese Chin
+  153: Maltese
+  154: Pekingese
+  155: Shih Tzu
+  156: King Charles Spaniel
+  157: Papillon
+  158: toy terrier
+  159: Rhodesian Ridgeback
+  160: Afghan Hound
+  161: Basset Hound
+  162: Beagle
+  163: Bloodhound
+  164: Bluetick Coonhound
+  165: Black and Tan Coonhound
+  166: Treeing Walker Coonhound
+  167: English foxhound
+  168: Redbone Coonhound
+  169: borzoi
+  170: Irish Wolfhound
+  171: Italian Greyhound
+  172: Whippet
+  173: Ibizan Hound
+  174: Norwegian Elkhound
+  175: Otterhound
+  176: Saluki
+  177: Scottish Deerhound
+  178: Weimaraner
+  179: Staffordshire Bull Terrier
+  180: American Staffordshire Terrier
+  181: Bedlington Terrier
+  182: Border Terrier
+  183: Kerry Blue Terrier
+  184: Irish Terrier
+  185: Norfolk Terrier
+  186: Norwich Terrier
+  187: Yorkshire Terrier
+  188: Wire Fox Terrier
+  189: Lakeland Terrier
+  190: Sealyham Terrier
+  191: Airedale Terrier
+  192: Cairn Terrier
+  193: Australian Terrier
+  194: Dandie Dinmont Terrier
+  195: Boston Terrier
+  196: Miniature Schnauzer
+  197: Giant Schnauzer
+  198: Standard Schnauzer
+  199: Scottish Terrier
+  200: Tibetan Terrier
+  201: Australian Silky Terrier
+  202: Soft-coated Wheaten Terrier
+  203: West Highland White Terrier
+  204: Lhasa Apso
+  205: Flat-Coated Retriever
+  206: Curly-coated Retriever
+  207: Golden Retriever
+  208: Labrador Retriever
+  209: Chesapeake Bay Retriever
+  210: German Shorthaired Pointer
+  211: Vizsla
+  212: English Setter
+  213: Irish Setter
+  214: Gordon Setter
+  215: Brittany
+  216: Clumber Spaniel
+  217: English Springer Spaniel
+  218: Welsh Springer Spaniel
+  219: Cocker Spaniels
+  220: Sussex Spaniel
+  221: Irish Water Spaniel
+  222: Kuvasz
+  223: Schipperke
+  224: Groenendael
+  225: Malinois
+  226: Briard
+  227: Australian Kelpie
+  228: Komondor
+  229: Old English Sheepdog
+  230: Shetland Sheepdog
+  231: collie
+  232: Border Collie
+  233: Bouvier des Flandres
+  234: Rottweiler
+  235: German Shepherd Dog
+  236: Dobermann
+  237: Miniature Pinscher
+  238: Greater Swiss Mountain Dog
+  239: Bernese Mountain Dog
+  240: Appenzeller Sennenhund
+  241: Entlebucher Sennenhund
+  242: Boxer
+  243: Bullmastiff
+  244: Tibetan Mastiff
+  245: French Bulldog
+  246: Great Dane
+  247: St. Bernard
+  248: husky
+  249: Alaskan Malamute
+  250: Siberian Husky
+  251: Dalmatian
+  252: Affenpinscher
+  253: Basenji
+  254: pug
+  255: Leonberger
+  256: Newfoundland
+  257: Pyrenean Mountain Dog
+  258: Samoyed
+  259: Pomeranian
+  260: Chow Chow
+  261: Keeshond
+  262: Griffon Bruxellois
+  263: Pembroke Welsh Corgi
+  264: Cardigan Welsh Corgi
+  265: Toy Poodle
+  266: Miniature Poodle
+  267: Standard Poodle
+  268: Mexican hairless dog
+  269: grey wolf
+  270: Alaskan tundra wolf
+  271: red wolf
+  272: coyote
+  273: dingo
+  274: dhole
+  275: African wild dog
+  276: hyena
+  277: red fox
+  278: kit fox
+  279: Arctic fox
+  280: grey fox
+  281: tabby cat
+  282: tiger cat
+  283: Persian cat
+  284: Siamese cat
+  285: Egyptian Mau
+  286: cougar
+  287: lynx
+  288: leopard
+  289: snow leopard
+  290: jaguar
+  291: lion
+  292: tiger
+  293: cheetah
+  294: brown bear
+  295: American black bear
+  296: polar bear
+  297: sloth bear
+  298: mongoose
+  299: meerkat
+  300: tiger beetle
+  301: ladybug
+  302: ground beetle
+  303: longhorn beetle
+  304: leaf beetle
+  305: dung beetle
+  306: rhinoceros beetle
+  307: weevil
+  308: fly
+  309: bee
+  310: ant
+  311: grasshopper
+  312: cricket
+  313: stick insect
+  314: cockroach
+  315: mantis
+  316: cicada
+  317: leafhopper
+  318: lacewing
+  319: dragonfly
+  320: damselfly
+  321: red admiral
+  322: ringlet
+  323: monarch butterfly
+  324: small white
+  325: sulfur butterfly
+  326: gossamer-winged butterfly
+  327: starfish
+  328: sea urchin
+  329: sea cucumber
+  330: cottontail rabbit
+  331: hare
+  332: Angora rabbit
+  333: hamster
+  334: porcupine
+  335: fox squirrel
+  336: marmot
+  337: beaver
+  338: guinea pig
+  339: common sorrel
+  340: zebra
+  341: pig
+  342: wild boar
+  343: warthog
+  344: hippopotamus
+  345: ox
+  346: water buffalo
+  347: bison
+  348: ram
+  349: bighorn sheep
+  350: Alpine ibex
+  351: hartebeest
+  352: impala
+  353: gazelle
+  354: dromedary
+  355: llama
+  356: weasel
+  357: mink
+  358: European polecat
+  359: black-footed ferret
+  360: otter
+  361: skunk
+  362: badger
+  363: armadillo
+  364: three-toed sloth
+  365: orangutan
+  366: gorilla
+  367: chimpanzee
+  368: gibbon
+  369: siamang
+  370: guenon
+  371: patas monkey
+  372: baboon
+  373: macaque
+  374: langur
+  375: black-and-white colobus
+  376: proboscis monkey
+  377: marmoset
+  378: white-headed capuchin
+  379: howler monkey
+  380: titi
+  381: Geoffroy's spider monkey
+  382: common squirrel monkey
+  383: ring-tailed lemur
+  384: indri
+  385: Asian elephant
+  386: African bush elephant
+  387: red panda
+  388: giant panda
+  389: snoek
+  390: eel
+  391: coho salmon
+  392: rock beauty
+  393: clownfish
+  394: sturgeon
+  395: garfish
+  396: lionfish
+  397: pufferfish
+  398: abacus
+  399: abaya
+  400: academic gown
+  401: accordion
+  402: acoustic guitar
+  403: aircraft carrier
+  404: airliner
+  405: airship
+  406: altar
+  407: ambulance
+  408: amphibious vehicle
+  409: analog clock
+  410: apiary
+  411: apron
+  412: waste container
+  413: assault rifle
+  414: backpack
+  415: bakery
+  416: balance beam
+  417: balloon
+  418: ballpoint pen
+  419: Band-Aid
+  420: banjo
+  421: baluster
+  422: barbell
+  423: barber chair
+  424: barbershop
+  425: barn
+  426: barometer
+  427: barrel
+  428: wheelbarrow
+  429: baseball
+  430: basketball
+  431: bassinet
+  432: bassoon
+  433: swimming cap
+  434: bath towel
+  435: bathtub
+  436: station wagon
+  437: lighthouse
+  438: beaker
+  439: military cap
+  440: beer bottle
+  441: beer glass
+  442: bell-cot
+  443: bib
+  444: tandem bicycle
+  445: bikini
+  446: ring binder
+  447: binoculars
+  448: birdhouse
+  449: boathouse
+  450: bobsleigh
+  451: bolo tie
+  452: poke bonnet
+  453: bookcase
+  454: bookstore
+  455: bottle cap
+  456: bow
+  457: bow tie
+  458: brass
+  459: bra
+  460: breakwater
+  461: breastplate
+  462: broom
+  463: bucket
+  464: buckle
+  465: bulletproof vest
+  466: high-speed train
+  467: butcher shop
+  468: taxicab
+  469: cauldron
+  470: candle
+  471: cannon
+  472: canoe
+  473: can opener
+  474: cardigan
+  475: car mirror
+  476: carousel
+  477: tool kit
+  478: carton
+  479: car wheel
+  480: automated teller machine
+  481: cassette
+  482: cassette player
+  483: castle
+  484: catamaran
+  485: CD player
+  486: cello
+  487: mobile phone
+  488: chain
+  489: chain-link fence
+  490: chain mail
+  491: chainsaw
+  492: chest
+  493: chiffonier
+  494: chime
+  495: china cabinet
+  496: Christmas stocking
+  497: church
+  498: movie theater
+  499: cleaver
+  500: cliff dwelling
+  501: cloak
+  502: clogs
+  503: cocktail shaker
+  504: coffee mug
+  505: coffeemaker
+  506: coil
+  507: combination lock
+  508: computer keyboard
+  509: confectionery store
+  510: container ship
+  511: convertible
+  512: corkscrew
+  513: cornet
+  514: cowboy boot
+  515: cowboy hat
+  516: cradle
+  517: crane (machine)
+  518: crash helmet
+  519: crate
+  520: infant bed
+  521: Crock Pot
+  522: croquet ball
+  523: crutch
+  524: cuirass
+  525: dam
+  526: desk
+  527: desktop computer
+  528: rotary dial telephone
+  529: diaper
+  530: digital clock
+  531: digital watch
+  532: dining table
+  533: dishcloth
+  534: dishwasher
+  535: disc brake
+  536: dock
+  537: dog sled
+  538: dome
+  539: doormat
+  540: drilling rig
+  541: drum
+  542: drumstick
+  543: dumbbell
+  544: Dutch oven
+  545: electric fan
+  546: electric guitar
+  547: electric locomotive
+  548: entertainment center
+  549: envelope
+  550: espresso machine
+  551: face powder
+  552: feather boa
+  553: filing cabinet
+  554: fireboat
+  555: fire engine
+  556: fire screen sheet
+  557: flagpole
+  558: flute
+  559: folding chair
+  560: football helmet
+  561: forklift
+  562: fountain
+  563: fountain pen
+  564: four-poster bed
+  565: freight car
+  566: French horn
+  567: frying pan
+  568: fur coat
+  569: garbage truck
+  570: gas mask
+  571: gas pump
+  572: goblet
+  573: go-kart
+  574: golf ball
+  575: golf cart
+  576: gondola
+  577: gong
+  578: gown
+  579: grand piano
+  580: greenhouse
+  581: grille
+  582: grocery store
+  583: guillotine
+  584: barrette
+  585: hair spray
+  586: half-track
+  587: hammer
+  588: hamper
+  589: hair dryer
+  590: hand-held computer
+  591: handkerchief
+  592: hard disk drive
+  593: harmonica
+  594: harp
+  595: harvester
+  596: hatchet
+  597: holster
+  598: home theater
+  599: honeycomb
+  600: hook
+  601: hoop skirt
+  602: horizontal bar
+  603: horse-drawn vehicle
+  604: hourglass
+  605: iPod
+  606: clothes iron
+  607: jack-o'-lantern
+  608: jeans
+  609: jeep
+  610: T-shirt
+  611: jigsaw puzzle
+  612: pulled rickshaw
+  613: joystick
+  614: kimono
+  615: knee pad
+  616: knot
+  617: lab coat
+  618: ladle
+  619: lampshade
+  620: laptop computer
+  621: lawn mower
+  622: lens cap
+  623: paper knife
+  624: library
+  625: lifeboat
+  626: lighter
+  627: limousine
+  628: ocean liner
+  629: lipstick
+  630: slip-on shoe
+  631: lotion
+  632: speaker
+  633: loupe
+  634: sawmill
+  635: magnetic compass
+  636: mail bag
+  637: mailbox
+  638: tights
+  639: tank suit
+  640: manhole cover
+  641: maraca
+  642: marimba
+  643: mask
+  644: match
+  645: maypole
+  646: maze
+  647: measuring cup
+  648: medicine chest
+  649: megalith
+  650: microphone
+  651: microwave oven
+  652: military uniform
+  653: milk can
+  654: minibus
+  655: miniskirt
+  656: minivan
+  657: missile
+  658: mitten
+  659: mixing bowl
+  660: mobile home
+  661: Model T
+  662: modem
+  663: monastery
+  664: monitor
+  665: moped
+  666: mortar
+  667: square academic cap
+  668: mosque
+  669: mosquito net
+  670: scooter
+  671: mountain bike
+  672: tent
+  673: computer mouse
+  674: mousetrap
+  675: moving van
+  676: muzzle
+  677: nail
+  678: neck brace
+  679: necklace
+  680: nipple
+  681: notebook computer
+  682: obelisk
+  683: oboe
+  684: ocarina
+  685: odometer
+  686: oil filter
+  687: organ
+  688: oscilloscope
+  689: overskirt
+  690: bullock cart
+  691: oxygen mask
+  692: packet
+  693: paddle
+  694: paddle wheel
+  695: padlock
+  696: paintbrush
+  697: pajamas
+  698: palace
+  699: pan flute
+  700: paper towel
+  701: parachute
+  702: parallel bars
+  703: park bench
+  704: parking meter
+  705: passenger car
+  706: patio
+  707: payphone
+  708: pedestal
+  709: pencil case
+  710: pencil sharpener
+  711: perfume
+  712: Petri dish
+  713: photocopier
+  714: plectrum
+  715: Pickelhaube
+  716: picket fence
+  717: pickup truck
+  718: pier
+  719: piggy bank
+  720: pill bottle
+  721: pillow
+  722: ping-pong ball
+  723: pinwheel
+  724: pirate ship
+  725: pitcher
+  726: hand plane
+  727: planetarium
+  728: plastic bag
+  729: plate rack
+  730: plow
+  731: plunger
+  732: Polaroid camera
+  733: pole
+  734: police van
+  735: poncho
+  736: billiard table
+  737: soda bottle
+  738: pot
+  739: potter's wheel
+  740: power drill
+  741: prayer rug
+  742: printer
+  743: prison
+  744: projectile
+  745: projector
+  746: hockey puck
+  747: punching bag
+  748: purse
+  749: quill
+  750: quilt
+  751: race car
+  752: racket
+  753: radiator
+  754: radio
+  755: radio telescope
+  756: rain barrel
+  757: recreational vehicle
+  758: reel
+  759: reflex camera
+  760: refrigerator
+  761: remote control
+  762: restaurant
+  763: revolver
+  764: rifle
+  765: rocking chair
+  766: rotisserie
+  767: eraser
+  768: rugby ball
+  769: ruler
+  770: running shoe
+  771: safe
+  772: safety pin
+  773: salt shaker
+  774: sandal
+  775: sarong
+  776: saxophone
+  777: scabbard
+  778: weighing scale
+  779: school bus
+  780: schooner
+  781: scoreboard
+  782: CRT screen
+  783: screw
+  784: screwdriver
+  785: seat belt
+  786: sewing machine
+  787: shield
+  788: shoe store
+  789: shoji
+  790: shopping basket
+  791: shopping cart
+  792: shovel
+  793: shower cap
+  794: shower curtain
+  795: ski
+  796: ski mask
+  797: sleeping bag
+  798: slide rule
+  799: sliding door
+  800: slot machine
+  801: snorkel
+  802: snowmobile
+  803: snowplow
+  804: soap dispenser
+  805: soccer ball
+  806: sock
+  807: solar thermal collector
+  808: sombrero
+  809: soup bowl
+  810: space bar
+  811: space heater
+  812: space shuttle
+  813: spatula
+  814: motorboat
+  815: spider web
+  816: spindle
+  817: sports car
+  818: spotlight
+  819: stage
+  820: steam locomotive
+  821: through arch bridge
+  822: steel drum
+  823: stethoscope
+  824: scarf
+  825: stone wall
+  826: stopwatch
+  827: stove
+  828: strainer
+  829: tram
+  830: stretcher
+  831: couch
+  832: stupa
+  833: submarine
+  834: suit
+  835: sundial
+  836: sunglass
+  837: sunglasses
+  838: sunscreen
+  839: suspension bridge
+  840: mop
+  841: sweatshirt
+  842: swimsuit
+  843: swing
+  844: switch
+  845: syringe
+  846: table lamp
+  847: tank
+  848: tape player
+  849: teapot
+  850: teddy bear
+  851: television
+  852: tennis ball
+  853: thatched roof
+  854: front curtain
+  855: thimble
+  856: threshing machine
+  857: throne
+  858: tile roof
+  859: toaster
+  860: tobacco shop
+  861: toilet seat
+  862: torch
+  863: totem pole
+  864: tow truck
+  865: toy store
+  866: tractor
+  867: semi-trailer truck
+  868: tray
+  869: trench coat
+  870: tricycle
+  871: trimaran
+  872: tripod
+  873: triumphal arch
+  874: trolleybus
+  875: trombone
+  876: tub
+  877: turnstile
+  878: typewriter keyboard
+  879: umbrella
+  880: unicycle
+  881: upright piano
+  882: vacuum cleaner
+  883: vase
+  884: vault
+  885: velvet
+  886: vending machine
+  887: vestment
+  888: viaduct
+  889: violin
+  890: volleyball
+  891: waffle iron
+  892: wall clock
+  893: wallet
+  894: wardrobe
+  895: military aircraft
+  896: sink
+  897: washing machine
+  898: water bottle
+  899: water jug
+  900: water tower
+  901: whiskey jug
+  902: whistle
+  903: wig
+  904: window screen
+  905: window shade
+  906: Windsor tie
+  907: wine bottle
+  908: wing
+  909: wok
+  910: wooden spoon
+  911: wool
+  912: split-rail fence
+  913: shipwreck
+  914: yawl
+  915: yurt
+  916: website
+  917: comic book
+  918: crossword
+  919: traffic sign
+  920: traffic light
+  921: dust jacket
+  922: menu
+  923: plate
+  924: guacamole
+  925: consomme
+  926: hot pot
+  927: trifle
+  928: ice cream
+  929: ice pop
+  930: baguette
+  931: bagel
+  932: pretzel
+  933: cheeseburger
+  934: hot dog
+  935: mashed potato
+  936: cabbage
+  937: broccoli
+  938: cauliflower
+  939: zucchini
+  940: spaghetti squash
+  941: acorn squash
+  942: butternut squash
+  943: cucumber
+  944: artichoke
+  945: bell pepper
+  946: cardoon
+  947: mushroom
+  948: Granny Smith
+  949: strawberry
+  950: orange
+  951: lemon
+  952: fig
+  953: pineapple
+  954: banana
+  955: jackfruit
+  956: custard apple
+  957: pomegranate
+  958: hay
+  959: carbonara
+  960: chocolate syrup
+  961: dough
+  962: meatloaf
+  963: pizza
+  964: pot pie
+  965: burrito
+  966: red wine
+  967: espresso
+  968: cup
+  969: eggnog
+  970: alp
+  971: bubble
+  972: cliff
+  973: coral reef
+  974: geyser
+  975: lakeshore
+  976: promontory
+  977: shoal
+  978: seashore
+  979: valley
+  980: volcano
+  981: baseball player
+  982: bridegroom
+  983: scuba diver
+  984: rapeseed
+  985: daisy
+  986: yellow lady's slipper
+  987: corn
+  988: acorn
+  989: rose hip
+  990: horse chestnut seed
+  991: coral fungus
+  992: agaric
+  993: gyromitra
+  994: stinkhorn mushroom
+  995: earth star
+  996: hen-of-the-woods
+  997: bolete
+  998: ear
+  999: toilet paper
+
+# Imagenet class codes to human-readable names
+map:
+  n01440764: tench
+  n01443537: goldfish
+  n01484850: great_white_shark
+  n01491361: tiger_shark
+  n01494475: hammerhead
+  n01496331: electric_ray
+  n01498041: stingray
+  n01514668: cock
+  n01514859: hen
+  n01518878: ostrich
+  n01530575: brambling
+  n01531178: goldfinch
+  n01532829: house_finch
+  n01534433: junco
+  n01537544: indigo_bunting
+  n01558993: robin
+  n01560419: bulbul
+  n01580077: jay
+  n01582220: magpie
+  n01592084: chickadee
+  n01601694: water_ouzel
+  n01608432: kite
+  n01614925: bald_eagle
+  n01616318: vulture
+  n01622779: great_grey_owl
+  n01629819: European_fire_salamander
+  n01630670: common_newt
+  n01631663: eft
+  n01632458: spotted_salamander
+  n01632777: axolotl
+  n01641577: bullfrog
+  n01644373: tree_frog
+  n01644900: tailed_frog
+  n01664065: loggerhead
+  n01665541: leatherback_turtle
+  n01667114: mud_turtle
+  n01667778: terrapin
+  n01669191: box_turtle
+  n01675722: banded_gecko
+  n01677366: common_iguana
+  n01682714: American_chameleon
+  n01685808: whiptail
+  n01687978: agama
+  n01688243: frilled_lizard
+  n01689811: alligator_lizard
+  n01692333: Gila_monster
+  n01693334: green_lizard
+  n01694178: African_chameleon
+  n01695060: Komodo_dragon
+  n01697457: African_crocodile
+  n01698640: American_alligator
+  n01704323: triceratops
+  n01728572: thunder_snake
+  n01728920: ringneck_snake
+  n01729322: hognose_snake
+  n01729977: green_snake
+  n01734418: king_snake
+  n01735189: garter_snake
+  n01737021: water_snake
+  n01739381: vine_snake
+  n01740131: night_snake
+  n01742172: boa_constrictor
+  n01744401: rock_python
+  n01748264: Indian_cobra
+  n01749939: green_mamba
+  n01751748: sea_snake
+  n01753488: horned_viper
+  n01755581: diamondback
+  n01756291: sidewinder
+  n01768244: trilobite
+  n01770081: harvestman
+  n01770393: scorpion
+  n01773157: black_and_gold_garden_spider
+  n01773549: barn_spider
+  n01773797: garden_spider
+  n01774384: black_widow
+  n01774750: tarantula
+  n01775062: wolf_spider
+  n01776313: tick
+  n01784675: centipede
+  n01795545: black_grouse
+  n01796340: ptarmigan
+  n01797886: ruffed_grouse
+  n01798484: prairie_chicken
+  n01806143: peacock
+  n01806567: quail
+  n01807496: partridge
+  n01817953: African_grey
+  n01818515: macaw
+  n01819313: sulphur-crested_cockatoo
+  n01820546: lorikeet
+  n01824575: coucal
+  n01828970: bee_eater
+  n01829413: hornbill
+  n01833805: hummingbird
+  n01843065: jacamar
+  n01843383: toucan
+  n01847000: drake
+  n01855032: red-breasted_merganser
+  n01855672: goose
+  n01860187: black_swan
+  n01871265: tusker
+  n01872401: echidna
+  n01873310: platypus
+  n01877812: wallaby
+  n01882714: koala
+  n01883070: wombat
+  n01910747: jellyfish
+  n01914609: sea_anemone
+  n01917289: brain_coral
+  n01924916: flatworm
+  n01930112: nematode
+  n01943899: conch
+  n01944390: snail
+  n01945685: slug
+  n01950731: sea_slug
+  n01955084: chiton
+  n01968897: chambered_nautilus
+  n01978287: Dungeness_crab
+  n01978455: rock_crab
+  n01980166: fiddler_crab
+  n01981276: king_crab
+  n01983481: American_lobster
+  n01984695: spiny_lobster
+  n01985128: crayfish
+  n01986214: hermit_crab
+  n01990800: isopod
+  n02002556: white_stork
+  n02002724: black_stork
+  n02006656: spoonbill
+  n02007558: flamingo
+  n02009229: little_blue_heron
+  n02009912: American_egret
+  n02011460: bittern
+  n02012849: crane_(bird)
+  n02013706: limpkin
+  n02017213: European_gallinule
+  n02018207: American_coot
+  n02018795: bustard
+  n02025239: ruddy_turnstone
+  n02027492: red-backed_sandpiper
+  n02028035: redshank
+  n02033041: dowitcher
+  n02037110: oystercatcher
+  n02051845: pelican
+  n02056570: king_penguin
+  n02058221: albatross
+  n02066245: grey_whale
+  n02071294: killer_whale
+  n02074367: dugong
+  n02077923: sea_lion
+  n02085620: Chihuahua
+  n02085782: Japanese_spaniel
+  n02085936: Maltese_dog
+  n02086079: Pekinese
+  n02086240: Shih-Tzu
+  n02086646: Blenheim_spaniel
+  n02086910: papillon
+  n02087046: toy_terrier
+  n02087394: Rhodesian_ridgeback
+  n02088094: Afghan_hound
+  n02088238: basset
+  n02088364: beagle
+  n02088466: bloodhound
+  n02088632: bluetick
+  n02089078: black-and-tan_coonhound
+  n02089867: Walker_hound
+  n02089973: English_foxhound
+  n02090379: redbone
+  n02090622: borzoi
+  n02090721: Irish_wolfhound
+  n02091032: Italian_greyhound
+  n02091134: whippet
+  n02091244: Ibizan_hound
+  n02091467: Norwegian_elkhound
+  n02091635: otterhound
+  n02091831: Saluki
+  n02092002: Scottish_deerhound
+  n02092339: Weimaraner
+  n02093256: Staffordshire_bullterrier
+  n02093428: American_Staffordshire_terrier
+  n02093647: Bedlington_terrier
+  n02093754: Border_terrier
+  n02093859: Kerry_blue_terrier
+  n02093991: Irish_terrier
+  n02094114: Norfolk_terrier
+  n02094258: Norwich_terrier
+  n02094433: Yorkshire_terrier
+  n02095314: wire-haired_fox_terrier
+  n02095570: Lakeland_terrier
+  n02095889: Sealyham_terrier
+  n02096051: Airedale
+  n02096177: cairn
+  n02096294: Australian_terrier
+  n02096437: Dandie_Dinmont
+  n02096585: Boston_bull
+  n02097047: miniature_schnauzer
+  n02097130: giant_schnauzer
+  n02097209: standard_schnauzer
+  n02097298: Scotch_terrier
+  n02097474: Tibetan_terrier
+  n02097658: silky_terrier
+  n02098105: soft-coated_wheaten_terrier
+  n02098286: West_Highland_white_terrier
+  n02098413: Lhasa
+  n02099267: flat-coated_retriever
+  n02099429: curly-coated_retriever
+  n02099601: golden_retriever
+  n02099712: Labrador_retriever
+  n02099849: Chesapeake_Bay_retriever
+  n02100236: German_short-haired_pointer
+  n02100583: vizsla
+  n02100735: English_setter
+  n02100877: Irish_setter
+  n02101006: Gordon_setter
+  n02101388: Brittany_spaniel
+  n02101556: clumber
+  n02102040: English_springer
+  n02102177: Welsh_springer_spaniel
+  n02102318: cocker_spaniel
+  n02102480: Sussex_spaniel
+  n02102973: Irish_water_spaniel
+  n02104029: kuvasz
+  n02104365: schipperke
+  n02105056: groenendael
+  n02105162: malinois
+  n02105251: briard
+  n02105412: kelpie
+  n02105505: komondor
+  n02105641: Old_English_sheepdog
+  n02105855: Shetland_sheepdog
+  n02106030: collie
+  n02106166: Border_collie
+  n02106382: Bouvier_des_Flandres
+  n02106550: Rottweiler
+  n02106662: German_shepherd
+  n02107142: Doberman
+  n02107312: miniature_pinscher
+  n02107574: Greater_Swiss_Mountain_dog
+  n02107683: Bernese_mountain_dog
+  n02107908: Appenzeller
+  n02108000: EntleBucher
+  n02108089: boxer
+  n02108422: bull_mastiff
+  n02108551: Tibetan_mastiff
+  n02108915: French_bulldog
+  n02109047: Great_Dane
+  n02109525: Saint_Bernard
+  n02109961: Eskimo_dog
+  n02110063: malamute
+  n02110185: Siberian_husky
+  n02110341: dalmatian
+  n02110627: affenpinscher
+  n02110806: basenji
+  n02110958: pug
+  n02111129: Leonberg
+  n02111277: Newfoundland
+  n02111500: Great_Pyrenees
+  n02111889: Samoyed
+  n02112018: Pomeranian
+  n02112137: chow
+  n02112350: keeshond
+  n02112706: Brabancon_griffon
+  n02113023: Pembroke
+  n02113186: Cardigan
+  n02113624: toy_poodle
+  n02113712: miniature_poodle
+  n02113799: standard_poodle
+  n02113978: Mexican_hairless
+  n02114367: timber_wolf
+  n02114548: white_wolf
+  n02114712: red_wolf
+  n02114855: coyote
+  n02115641: dingo
+  n02115913: dhole
+  n02116738: African_hunting_dog
+  n02117135: hyena
+  n02119022: red_fox
+  n02119789: kit_fox
+  n02120079: Arctic_fox
+  n02120505: grey_fox
+  n02123045: tabby
+  n02123159: tiger_cat
+  n02123394: Persian_cat
+  n02123597: Siamese_cat
+  n02124075: Egyptian_cat
+  n02125311: cougar
+  n02127052: lynx
+  n02128385: leopard
+  n02128757: snow_leopard
+  n02128925: jaguar
+  n02129165: lion
+  n02129604: tiger
+  n02130308: cheetah
+  n02132136: brown_bear
+  n02133161: American_black_bear
+  n02134084: ice_bear
+  n02134418: sloth_bear
+  n02137549: mongoose
+  n02138441: meerkat
+  n02165105: tiger_beetle
+  n02165456: ladybug
+  n02167151: ground_beetle
+  n02168699: long-horned_beetle
+  n02169497: leaf_beetle
+  n02172182: dung_beetle
+  n02174001: rhinoceros_beetle
+  n02177972: weevil
+  n02190166: fly
+  n02206856: bee
+  n02219486: ant
+  n02226429: grasshopper
+  n02229544: cricket
+  n02231487: walking_stick
+  n02233338: cockroach
+  n02236044: mantis
+  n02256656: cicada
+  n02259212: leafhopper
+  n02264363: lacewing
+  n02268443: dragonfly
+  n02268853: damselfly
+  n02276258: admiral
+  n02277742: ringlet
+  n02279972: monarch
+  n02280649: cabbage_butterfly
+  n02281406: sulphur_butterfly
+  n02281787: lycaenid
+  n02317335: starfish
+  n02319095: sea_urchin
+  n02321529: sea_cucumber
+  n02325366: wood_rabbit
+  n02326432: hare
+  n02328150: Angora
+  n02342885: hamster
+  n02346627: porcupine
+  n02356798: fox_squirrel
+  n02361337: marmot
+  n02363005: beaver
+  n02364673: guinea_pig
+  n02389026: sorrel
+  n02391049: zebra
+  n02395406: hog
+  n02396427: wild_boar
+  n02397096: warthog
+  n02398521: hippopotamus
+  n02403003: ox
+  n02408429: water_buffalo
+  n02410509: bison
+  n02412080: ram
+  n02415577: bighorn
+  n02417914: ibex
+  n02422106: hartebeest
+  n02422699: impala
+  n02423022: gazelle
+  n02437312: Arabian_camel
+  n02437616: llama
+  n02441942: weasel
+  n02442845: mink
+  n02443114: polecat
+  n02443484: black-footed_ferret
+  n02444819: otter
+  n02445715: skunk
+  n02447366: badger
+  n02454379: armadillo
+  n02457408: three-toed_sloth
+  n02480495: orangutan
+  n02480855: gorilla
+  n02481823: chimpanzee
+  n02483362: gibbon
+  n02483708: siamang
+  n02484975: guenon
+  n02486261: patas
+  n02486410: baboon
+  n02487347: macaque
+  n02488291: langur
+  n02488702: colobus
+  n02489166: proboscis_monkey
+  n02490219: marmoset
+  n02492035: capuchin
+  n02492660: howler_monkey
+  n02493509: titi
+  n02493793: spider_monkey
+  n02494079: squirrel_monkey
+  n02497673: Madagascar_cat
+  n02500267: indri
+  n02504013: Indian_elephant
+  n02504458: African_elephant
+  n02509815: lesser_panda
+  n02510455: giant_panda
+  n02514041: barracouta
+  n02526121: eel
+  n02536864: coho
+  n02606052: rock_beauty
+  n02607072: anemone_fish
+  n02640242: sturgeon
+  n02641379: gar
+  n02643566: lionfish
+  n02655020: puffer
+  n02666196: abacus
+  n02667093: abaya
+  n02669723: academic_gown
+  n02672831: accordion
+  n02676566: acoustic_guitar
+  n02687172: aircraft_carrier
+  n02690373: airliner
+  n02692877: airship
+  n02699494: altar
+  n02701002: ambulance
+  n02704792: amphibian
+  n02708093: analog_clock
+  n02727426: apiary
+  n02730930: apron
+  n02747177: ashcan
+  n02749479: assault_rifle
+  n02769748: backpack
+  n02776631: bakery
+  n02777292: balance_beam
+  n02782093: balloon
+  n02783161: ballpoint
+  n02786058: Band_Aid
+  n02787622: banjo
+  n02788148: bannister
+  n02790996: barbell
+  n02791124: barber_chair
+  n02791270: barbershop
+  n02793495: barn
+  n02794156: barometer
+  n02795169: barrel
+  n02797295: barrow
+  n02799071: baseball
+  n02802426: basketball
+  n02804414: bassinet
+  n02804610: bassoon
+  n02807133: bathing_cap
+  n02808304: bath_towel
+  n02808440: bathtub
+  n02814533: beach_wagon
+  n02814860: beacon
+  n02815834: beaker
+  n02817516: bearskin
+  n02823428: beer_bottle
+  n02823750: beer_glass
+  n02825657: bell_cote
+  n02834397: bib
+  n02835271: bicycle-built-for-two
+  n02837789: bikini
+  n02840245: binder
+  n02841315: binoculars
+  n02843684: birdhouse
+  n02859443: boathouse
+  n02860847: bobsled
+  n02865351: bolo_tie
+  n02869837: bonnet
+  n02870880: bookcase
+  n02871525: bookshop
+  n02877765: bottlecap
+  n02879718: bow
+  n02883205: bow_tie
+  n02892201: brass
+  n02892767: brassiere
+  n02894605: breakwater
+  n02895154: breastplate
+  n02906734: broom
+  n02909870: bucket
+  n02910353: buckle
+  n02916936: bulletproof_vest
+  n02917067: bullet_train
+  n02927161: butcher_shop
+  n02930766: cab
+  n02939185: caldron
+  n02948072: candle
+  n02950826: cannon
+  n02951358: canoe
+  n02951585: can_opener
+  n02963159: cardigan
+  n02965783: car_mirror
+  n02966193: carousel
+  n02966687: carpenter's_kit
+  n02971356: carton
+  n02974003: car_wheel
+  n02977058: cash_machine
+  n02978881: cassette
+  n02979186: cassette_player
+  n02980441: castle
+  n02981792: catamaran
+  n02988304: CD_player
+  n02992211: cello
+  n02992529: cellular_telephone
+  n02999410: chain
+  n03000134: chainlink_fence
+  n03000247: chain_mail
+  n03000684: chain_saw
+  n03014705: chest
+  n03016953: chiffonier
+  n03017168: chime
+  n03018349: china_cabinet
+  n03026506: Christmas_stocking
+  n03028079: church
+  n03032252: cinema
+  n03041632: cleaver
+  n03042490: cliff_dwelling
+  n03045698: cloak
+  n03047690: clog
+  n03062245: cocktail_shaker
+  n03063599: coffee_mug
+  n03063689: coffeepot
+  n03065424: coil
+  n03075370: combination_lock
+  n03085013: computer_keyboard
+  n03089624: confectionery
+  n03095699: container_ship
+  n03100240: convertible
+  n03109150: corkscrew
+  n03110669: cornet
+  n03124043: cowboy_boot
+  n03124170: cowboy_hat
+  n03125729: cradle
+  n03126707: crane_(machine)
+  n03127747: crash_helmet
+  n03127925: crate
+  n03131574: crib
+  n03133878: Crock_Pot
+  n03134739: croquet_ball
+  n03141823: crutch
+  n03146219: cuirass
+  n03160309: dam
+  n03179701: desk
+  n03180011: desktop_computer
+  n03187595: dial_telephone
+  n03188531: diaper
+  n03196217: digital_clock
+  n03197337: digital_watch
+  n03201208: dining_table
+  n03207743: dishrag
+  n03207941: dishwasher
+  n03208938: disk_brake
+  n03216828: dock
+  n03218198: dogsled
+  n03220513: dome
+  n03223299: doormat
+  n03240683: drilling_platform
+  n03249569: drum
+  n03250847: drumstick
+  n03255030: dumbbell
+  n03259280: Dutch_oven
+  n03271574: electric_fan
+  n03272010: electric_guitar
+  n03272562: electric_locomotive
+  n03290653: entertainment_center
+  n03291819: envelope
+  n03297495: espresso_maker
+  n03314780: face_powder
+  n03325584: feather_boa
+  n03337140: file
+  n03344393: fireboat
+  n03345487: fire_engine
+  n03347037: fire_screen
+  n03355925: flagpole
+  n03372029: flute
+  n03376595: folding_chair
+  n03379051: football_helmet
+  n03384352: forklift
+  n03388043: fountain
+  n03388183: fountain_pen
+  n03388549: four-poster
+  n03393912: freight_car
+  n03394916: French_horn
+  n03400231: frying_pan
+  n03404251: fur_coat
+  n03417042: garbage_truck
+  n03424325: gasmask
+  n03425413: gas_pump
+  n03443371: goblet
+  n03444034: go-kart
+  n03445777: golf_ball
+  n03445924: golfcart
+  n03447447: gondola
+  n03447721: gong
+  n03450230: gown
+  n03452741: grand_piano
+  n03457902: greenhouse
+  n03459775: grille
+  n03461385: grocery_store
+  n03467068: guillotine
+  n03476684: hair_slide
+  n03476991: hair_spray
+  n03478589: half_track
+  n03481172: hammer
+  n03482405: hamper
+  n03483316: hand_blower
+  n03485407: hand-held_computer
+  n03485794: handkerchief
+  n03492542: hard_disc
+  n03494278: harmonica
+  n03495258: harp
+  n03496892: harvester
+  n03498962: hatchet
+  n03527444: holster
+  n03529860: home_theater
+  n03530642: honeycomb
+  n03532672: hook
+  n03534580: hoopskirt
+  n03535780: horizontal_bar
+  n03538406: horse_cart
+  n03544143: hourglass
+  n03584254: iPod
+  n03584829: iron
+  n03590841: jack-o'-lantern
+  n03594734: jean
+  n03594945: jeep
+  n03595614: jersey
+  n03598930: jigsaw_puzzle
+  n03599486: jinrikisha
+  n03602883: joystick
+  n03617480: kimono
+  n03623198: knee_pad
+  n03627232: knot
+  n03630383: lab_coat
+  n03633091: ladle
+  n03637318: lampshade
+  n03642806: laptop
+  n03649909: lawn_mower
+  n03657121: lens_cap
+  n03658185: letter_opener
+  n03661043: library
+  n03662601: lifeboat
+  n03666591: lighter
+  n03670208: limousine
+  n03673027: liner
+  n03676483: lipstick
+  n03680355: Loafer
+  n03690938: lotion
+  n03691459: loudspeaker
+  n03692522: loupe
+  n03697007: lumbermill
+  n03706229: magnetic_compass
+  n03709823: mailbag
+  n03710193: mailbox
+  n03710637: maillot_(tights)
+  n03710721: maillot_(tank_suit)
+  n03717622: manhole_cover
+  n03720891: maraca
+  n03721384: marimba
+  n03724870: mask
+  n03729826: matchstick
+  n03733131: maypole
+  n03733281: maze
+  n03733805: measuring_cup
+  n03742115: medicine_chest
+  n03743016: megalith
+  n03759954: microphone
+  n03761084: microwave
+  n03763968: military_uniform
+  n03764736: milk_can
+  n03769881: minibus
+  n03770439: miniskirt
+  n03770679: minivan
+  n03773504: missile
+  n03775071: mitten
+  n03775546: mixing_bowl
+  n03776460: mobile_home
+  n03777568: Model_T
+  n03777754: modem
+  n03781244: monastery
+  n03782006: monitor
+  n03785016: moped
+  n03786901: mortar
+  n03787032: mortarboard
+  n03788195: mosque
+  n03788365: mosquito_net
+  n03791053: motor_scooter
+  n03792782: mountain_bike
+  n03792972: mountain_tent
+  n03793489: mouse
+  n03794056: mousetrap
+  n03796401: moving_van
+  n03803284: muzzle
+  n03804744: nail
+  n03814639: neck_brace
+  n03814906: necklace
+  n03825788: nipple
+  n03832673: notebook
+  n03837869: obelisk
+  n03838899: oboe
+  n03840681: ocarina
+  n03841143: odometer
+  n03843555: oil_filter
+  n03854065: organ
+  n03857828: oscilloscope
+  n03866082: overskirt
+  n03868242: oxcart
+  n03868863: oxygen_mask
+  n03871628: packet
+  n03873416: paddle
+  n03874293: paddlewheel
+  n03874599: padlock
+  n03876231: paintbrush
+  n03877472: pajama
+  n03877845: palace
+  n03884397: panpipe
+  n03887697: paper_towel
+  n03888257: parachute
+  n03888605: parallel_bars
+  n03891251: park_bench
+  n03891332: parking_meter
+  n03895866: passenger_car
+  n03899768: patio
+  n03902125: pay-phone
+  n03903868: pedestal
+  n03908618: pencil_box
+  n03908714: pencil_sharpener
+  n03916031: perfume
+  n03920288: Petri_dish
+  n03924679: photocopier
+  n03929660: pick
+  n03929855: pickelhaube
+  n03930313: picket_fence
+  n03930630: pickup
+  n03933933: pier
+  n03935335: piggy_bank
+  n03937543: pill_bottle
+  n03938244: pillow
+  n03942813: ping-pong_ball
+  n03944341: pinwheel
+  n03947888: pirate
+  n03950228: pitcher
+  n03954731: plane
+  n03956157: planetarium
+  n03958227: plastic_bag
+  n03961711: plate_rack
+  n03967562: plow
+  n03970156: plunger
+  n03976467: Polaroid_camera
+  n03976657: pole
+  n03977966: police_van
+  n03980874: poncho
+  n03982430: pool_table
+  n03983396: pop_bottle
+  n03991062: pot
+  n03992509: potter's_wheel
+  n03995372: power_drill
+  n03998194: prayer_rug
+  n04004767: printer
+  n04005630: prison
+  n04008634: projectile
+  n04009552: projector
+  n04019541: puck
+  n04023962: punching_bag
+  n04026417: purse
+  n04033901: quill
+  n04033995: quilt
+  n04037443: racer
+  n04039381: racket
+  n04040759: radiator
+  n04041544: radio
+  n04044716: radio_telescope
+  n04049303: rain_barrel
+  n04065272: recreational_vehicle
+  n04067472: reel
+  n04069434: reflex_camera
+  n04070727: refrigerator
+  n04074963: remote_control
+  n04081281: restaurant
+  n04086273: revolver
+  n04090263: rifle
+  n04099969: rocking_chair
+  n04111531: rotisserie
+  n04116512: rubber_eraser
+  n04118538: rugby_ball
+  n04118776: rule
+  n04120489: running_shoe
+  n04125021: safe
+  n04127249: safety_pin
+  n04131690: saltshaker
+  n04133789: sandal
+  n04136333: sarong
+  n04141076: sax
+  n04141327: scabbard
+  n04141975: scale
+  n04146614: school_bus
+  n04147183: schooner
+  n04149813: scoreboard
+  n04152593: screen
+  n04153751: screw
+  n04154565: screwdriver
+  n04162706: seat_belt
+  n04179913: sewing_machine
+  n04192698: shield
+  n04200800: shoe_shop
+  n04201297: shoji
+  n04204238: shopping_basket
+  n04204347: shopping_cart
+  n04208210: shovel
+  n04209133: shower_cap
+  n04209239: shower_curtain
+  n04228054: ski
+  n04229816: ski_mask
+  n04235860: sleeping_bag
+  n04238763: slide_rule
+  n04239074: sliding_door
+  n04243546: slot
+  n04251144: snorkel
+  n04252077: snowmobile
+  n04252225: snowplow
+  n04254120: soap_dispenser
+  n04254680: soccer_ball
+  n04254777: sock
+  n04258138: solar_dish
+  n04259630: sombrero
+  n04263257: soup_bowl
+  n04264628: space_bar
+  n04265275: space_heater
+  n04266014: space_shuttle
+  n04270147: spatula
+  n04273569: speedboat
+  n04275548: spider_web
+  n04277352: spindle
+  n04285008: sports_car
+  n04286575: spotlight
+  n04296562: stage
+  n04310018: steam_locomotive
+  n04311004: steel_arch_bridge
+  n04311174: steel_drum
+  n04317175: stethoscope
+  n04325704: stole
+  n04326547: stone_wall
+  n04328186: stopwatch
+  n04330267: stove
+  n04332243: strainer
+  n04335435: streetcar
+  n04336792: stretcher
+  n04344873: studio_couch
+  n04346328: stupa
+  n04347754: submarine
+  n04350905: suit
+  n04355338: sundial
+  n04355933: sunglass
+  n04356056: sunglasses
+  n04357314: sunscreen
+  n04366367: suspension_bridge
+  n04367480: swab
+  n04370456: sweatshirt
+  n04371430: swimming_trunks
+  n04371774: swing
+  n04372370: switch
+  n04376876: syringe
+  n04380533: table_lamp
+  n04389033: tank
+  n04392985: tape_player
+  n04398044: teapot
+  n04399382: teddy
+  n04404412: television
+  n04409515: tennis_ball
+  n04417672: thatch
+  n04418357: theater_curtain
+  n04423845: thimble
+  n04428191: thresher
+  n04429376: throne
+  n04435653: tile_roof
+  n04442312: toaster
+  n04443257: tobacco_shop
+  n04447861: toilet_seat
+  n04456115: torch
+  n04458633: totem_pole
+  n04461696: tow_truck
+  n04462240: toyshop
+  n04465501: tractor
+  n04467665: trailer_truck
+  n04476259: tray
+  n04479046: trench_coat
+  n04482393: tricycle
+  n04483307: trimaran
+  n04485082: tripod
+  n04486054: triumphal_arch
+  n04487081: trolleybus
+  n04487394: trombone
+  n04493381: tub
+  n04501370: turnstile
+  n04505470: typewriter_keyboard
+  n04507155: umbrella
+  n04509417: unicycle
+  n04515003: upright
+  n04517823: vacuum
+  n04522168: vase
+  n04523525: vault
+  n04525038: velvet
+  n04525305: vending_machine
+  n04532106: vestment
+  n04532670: viaduct
+  n04536866: violin
+  n04540053: volleyball
+  n04542943: waffle_iron
+  n04548280: wall_clock
+  n04548362: wallet
+  n04550184: wardrobe
+  n04552348: warplane
+  n04553703: washbasin
+  n04554684: washer
+  n04557648: water_bottle
+  n04560804: water_jug
+  n04562935: water_tower
+  n04579145: whiskey_jug
+  n04579432: whistle
+  n04584207: wig
+  n04589890: window_screen
+  n04590129: window_shade
+  n04591157: Windsor_tie
+  n04591713: wine_bottle
+  n04592741: wing
+  n04596742: wok
+  n04597913: wooden_spoon
+  n04599235: wool
+  n04604644: worm_fence
+  n04606251: wreck
+  n04612504: yawl
+  n04613696: yurt
+  n06359193: web_site
+  n06596364: comic_book
+  n06785654: crossword_puzzle
+  n06794110: street_sign
+  n06874185: traffic_light
+  n07248320: book_jacket
+  n07565083: menu
+  n07579787: plate
+  n07583066: guacamole
+  n07584110: consomme
+  n07590611: hot_pot
+  n07613480: trifle
+  n07614500: ice_cream
+  n07615774: ice_lolly
+  n07684084: French_loaf
+  n07693725: bagel
+  n07695742: pretzel
+  n07697313: cheeseburger
+  n07697537: hotdog
+  n07711569: mashed_potato
+  n07714571: head_cabbage
+  n07714990: broccoli
+  n07715103: cauliflower
+  n07716358: zucchini
+  n07716906: spaghetti_squash
+  n07717410: acorn_squash
+  n07717556: butternut_squash
+  n07718472: cucumber
+  n07718747: artichoke
+  n07720875: bell_pepper
+  n07730033: cardoon
+  n07734744: mushroom
+  n07742313: Granny_Smith
+  n07745940: strawberry
+  n07747607: orange
+  n07749582: lemon
+  n07753113: fig
+  n07753275: pineapple
+  n07753592: banana
+  n07754684: jackfruit
+  n07760859: custard_apple
+  n07768694: pomegranate
+  n07802026: hay
+  n07831146: carbonara
+  n07836838: chocolate_sauce
+  n07860988: dough
+  n07871810: meat_loaf
+  n07873807: pizza
+  n07875152: potpie
+  n07880968: burrito
+  n07892512: red_wine
+  n07920052: espresso
+  n07930864: cup
+  n07932039: eggnog
+  n09193705: alp
+  n09229709: bubble
+  n09246464: cliff
+  n09256479: coral_reef
+  n09288635: geyser
+  n09332890: lakeside
+  n09399592: promontory
+  n09421951: sandbar
+  n09428293: seashore
+  n09468604: valley
+  n09472597: volcano
+  n09835506: ballplayer
+  n10148035: groom
+  n10565667: scuba_diver
+  n11879895: rapeseed
+  n11939491: daisy
+  n12057211: yellow_lady's_slipper
+  n12144580: corn
+  n12267677: acorn
+  n12620546: hip
+  n12768682: buckeye
+  n12985857: coral_fungus
+  n12998815: agaric
+  n13037406: gyromitra
+  n13040303: stinkhorn
+  n13044778: earthstar
+  n13052670: hen-of-the-woods
+  n13054560: bolete
+  n13133613: ear
+  n15075141: toilet_tissue
+
+# Download script/URL (optional)
+download: ultralytics/data/scripts/get_imagenet.sh

vendor/ultralytics/cfg/datasets/Objects365.yaml

@@ -0,0 +1,447 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
+# Example usage: yolo train data=Objects365.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: Person
+  1: Sneakers
+  2: Chair
+  3: Other Shoes
+  4: Hat
+  5: Car
+  6: Lamp
+  7: Glasses
+  8: Bottle
+  9: Desk
+  10: Cup
+  11: Street Lights
+  12: Cabinet/shelf
+  13: Handbag/Satchel
+  14: Bracelet
+  15: Plate
+  16: Picture/Frame
+  17: Helmet
+  18: Book
+  19: Gloves
+  20: Storage box
+  21: Boat
+  22: Leather Shoes
+  23: Flower
+  24: Bench
+  25: Potted Plant
+  26: Bowl/Basin
+  27: Flag
+  28: Pillow
+  29: Boots
+  30: Vase
+  31: Microphone
+  32: Necklace
+  33: Ring
+  34: SUV
+  35: Wine Glass
+  36: Belt
+  37: Monitor/TV
+  38: Backpack
+  39: Umbrella
+  40: Traffic Light
+  41: Speaker
+  42: Watch
+  43: Tie
+  44: Trash bin Can
+  45: Slippers
+  46: Bicycle
+  47: Stool
+  48: Barrel/bucket
+  49: Van
+  50: Couch
+  51: Sandals
+  52: Basket
+  53: Drum
+  54: Pen/Pencil
+  55: Bus
+  56: Wild Bird
+  57: High Heels
+  58: Motorcycle
+  59: Guitar
+  60: Carpet
+  61: Cell Phone
+  62: Bread
+  63: Camera
+  64: Canned
+  65: Truck
+  66: Traffic cone
+  67: Cymbal
+  68: Lifesaver
+  69: Towel
+  70: Stuffed Toy
+  71: Candle
+  72: Sailboat
+  73: Laptop
+  74: Awning
+  75: Bed
+  76: Faucet
+  77: Tent
+  78: Horse
+  79: Mirror
+  80: Power outlet
+  81: Sink
+  82: Apple
+  83: Air Conditioner
+  84: Knife
+  85: Hockey Stick
+  86: Paddle
+  87: Pickup Truck
+  88: Fork
+  89: Traffic Sign
+  90: Balloon
+  91: Tripod
+  92: Dog
+  93: Spoon
+  94: Clock
+  95: Pot
+  96: Cow
+  97: Cake
+  98: Dining Table
+  99: Sheep
+  100: Hanger
+  101: Blackboard/Whiteboard
+  102: Napkin
+  103: Other Fish
+  104: Orange/Tangerine
+  105: Toiletry
+  106: Keyboard
+  107: Tomato
+  108: Lantern
+  109: Machinery Vehicle
+  110: Fan
+  111: Green Vegetables
+  112: Banana
+  113: Baseball Glove
+  114: Airplane
+  115: Mouse
+  116: Train
+  117: Pumpkin
+  118: Soccer
+  119: Skiboard
+  120: Luggage
+  121: Nightstand
+  122: Tea pot
+  123: Telephone
+  124: Trolley
+  125: Head Phone
+  126: Sports Car
+  127: Stop Sign
+  128: Dessert
+  129: Scooter
+  130: Stroller
+  131: Crane
+  132: Remote
+  133: Refrigerator
+  134: Oven
+  135: Lemon
+  136: Duck
+  137: Baseball Bat
+  138: Surveillance Camera
+  139: Cat
+  140: Jug
+  141: Broccoli
+  142: Piano
+  143: Pizza
+  144: Elephant
+  145: Skateboard
+  146: Surfboard
+  147: Gun
+  148: Skating and Skiing shoes
+  149: Gas stove
+  150: Donut
+  151: Bow Tie
+  152: Carrot
+  153: Toilet
+  154: Kite
+  155: Strawberry
+  156: Other Balls
+  157: Shovel
+  158: Pepper
+  159: Computer Box
+  160: Toilet Paper
+  161: Cleaning Products
+  162: Chopsticks
+  163: Microwave
+  164: Pigeon
+  165: Baseball
+  166: Cutting/chopping Board
+  167: Coffee Table
+  168: Side Table
+  169: Scissors
+  170: Marker
+  171: Pie
+  172: Ladder
+  173: Snowboard
+  174: Cookies
+  175: Radiator
+  176: Fire Hydrant
+  177: Basketball
+  178: Zebra
+  179: Grape
+  180: Giraffe
+  181: Potato
+  182: Sausage
+  183: Tricycle
+  184: Violin
+  185: Egg
+  186: Fire Extinguisher
+  187: Candy
+  188: Fire Truck
+  189: Billiards
+  190: Converter
+  191: Bathtub
+  192: Wheelchair
+  193: Golf Club
+  194: Briefcase
+  195: Cucumber
+  196: Cigar/Cigarette
+  197: Paint Brush
+  198: Pear
+  199: Heavy Truck
+  200: Hamburger
+  201: Extractor
+  202: Extension Cord
+  203: Tong
+  204: Tennis Racket
+  205: Folder
+  206: American Football
+  207: earphone
+  208: Mask
+  209: Kettle
+  210: Tennis
+  211: Ship
+  212: Swing
+  213: Coffee Machine
+  214: Slide
+  215: Carriage
+  216: Onion
+  217: Green beans
+  218: Projector
+  219: Frisbee
+  220: Washing Machine/Drying Machine
+  221: Chicken
+  222: Printer
+  223: Watermelon
+  224: Saxophone
+  225: Tissue
+  226: Toothbrush
+  227: Ice cream
+  228: Hot-air balloon
+  229: Cello
+  230: French Fries
+  231: Scale
+  232: Trophy
+  233: Cabbage
+  234: Hot dog
+  235: Blender
+  236: Peach
+  237: Rice
+  238: Wallet/Purse
+  239: Volleyball
+  240: Deer
+  241: Goose
+  242: Tape
+  243: Tablet
+  244: Cosmetics
+  245: Trumpet
+  246: Pineapple
+  247: Golf Ball
+  248: Ambulance
+  249: Parking meter
+  250: Mango
+  251: Key
+  252: Hurdle
+  253: Fishing Rod
+  254: Medal
+  255: Flute
+  256: Brush
+  257: Penguin
+  258: Megaphone
+  259: Corn
+  260: Lettuce
+  261: Garlic
+  262: Swan
+  263: Helicopter
+  264: Green Onion
+  265: Sandwich
+  266: Nuts
+  267: Speed Limit Sign
+  268: Induction Cooker
+  269: Broom
+  270: Trombone
+  271: Plum
+  272: Rickshaw
+  273: Goldfish
+  274: Kiwi fruit
+  275: Router/modem
+  276: Poker Card
+  277: Toaster
+  278: Shrimp
+  279: Sushi
+  280: Cheese
+  281: Notepaper
+  282: Cherry
+  283: Pliers
+  284: CD
+  285: Pasta
+  286: Hammer
+  287: Cue
+  288: Avocado
+  289: Hami melon
+  290: Flask
+  291: Mushroom
+  292: Screwdriver
+  293: Soap
+  294: Recorder
+  295: Bear
+  296: Eggplant
+  297: Board Eraser
+  298: Coconut
+  299: Tape Measure/Ruler
+  300: Pig
+  301: Showerhead
+  302: Globe
+  303: Chips
+  304: Steak
+  305: Crosswalk Sign
+  306: Stapler
+  307: Camel
+  308: Formula 1
+  309: Pomegranate
+  310: Dishwasher
+  311: Crab
+  312: Hoverboard
+  313: Meatball
+  314: Rice Cooker
+  315: Tuba
+  316: Calculator
+  317: Papaya
+  318: Antelope
+  319: Parrot
+  320: Seal
+  321: Butterfly
+  322: Dumbbell
+  323: Donkey
+  324: Lion
+  325: Urinal
+  326: Dolphin
+  327: Electric Drill
+  328: Hair Dryer
+  329: Egg tart
+  330: Jellyfish
+  331: Treadmill
+  332: Lighter
+  333: Grapefruit
+  334: Game board
+  335: Mop
+  336: Radish
+  337: Baozi
+  338: Target
+  339: French
+  340: Spring Rolls
+  341: Monkey
+  342: Rabbit
+  343: Pencil Case
+  344: Yak
+  345: Red Cabbage
+  346: Binoculars
+  347: Asparagus
+  348: Barbell
+  349: Scallop
+  350: Noddles
+  351: Comb
+  352: Dumpling
+  353: Oyster
+  354: Table Tennis paddle
+  355: Cosmetics Brush/Eyeliner Pencil
+  356: Chainsaw
+  357: Eraser
+  358: Lobster
+  359: Durian
+  360: Okra
+  361: Lipstick
+  362: Cosmetics Mirror
+  363: Curling
+  364: Table Tennis
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from concurrent.futures import ThreadPoolExecutor
+  from pathlib import Path
+
+  import numpy as np
+
+  from ultralytics.utils import TQDM
+  from ultralytics.utils.checks import check_requirements
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywhn
+
+  check_requirements("faster-coco-eval")
+  from faster_coco_eval import COCO
+
+  # Train, Val Splits
+  dir = Path(yaml["path"])
+  for split, patches in [("train", 50 + 1), ("val", 43 + 1)]:
+      print(f"Processing {split} in {patches} patches ...")
+      images, labels = dir / "images" / split, dir / "labels" / split
+      images.mkdir(parents=True, exist_ok=True)
+      labels.mkdir(parents=True, exist_ok=True)
+
+      # Download
+      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+      if split == "train":
+          download([f"{url}zhiyuan_objv2_{split}.tar.gz"], dir=dir)  # annotations json
+          download([f"{url}patch{i}.tar.gz" for i in range(patches)], dir=images, threads=17)  # 51 patches / 17 threads = 3
+      elif split == "val":
+          download([f"{url}zhiyuan_objv2_{split}.json"], dir=dir)  # annotations json
+          download([f"{url}images/v1/patch{i}.tar.gz" for i in range(15 + 1)], dir=images, threads=16)
+          download([f"{url}images/v2/patch{i}.tar.gz" for i in range(16, patches)], dir=images, threads=16)
+
+      # Move
+      files = list(images.rglob("*.jpg"))
+      with ThreadPoolExecutor(max_workers=16) as executor:
+          list(TQDM(executor.map(lambda f: f.rename(images / f.name), files), total=len(files), desc=f"Moving {split} images"))
+
+      # Labels
+      coco = COCO(dir / f"zhiyuan_objv2_{split}.json")
+      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+
+          def process_annotation(im):
+              """Process and write annotations for a single image."""
+              try:
+                  width, height = im["width"], im["height"]
+                  path = Path(im["file_name"])
+                  with open(labels / path.with_suffix(".txt").name, "a", encoding="utf-8") as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a["bbox"]  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)
+
+          images_list = coco.loadImgs(imgIds)
+          with ThreadPoolExecutor(max_workers=16) as executor:
+              list(TQDM(executor.map(process_annotation, images_list), total=len(images_list), desc=f"Class {cid + 1}/{len(names)} {cat}"))

vendor/ultralytics/cfg/datasets/SKU-110K.yaml

@@ -0,0 +1,58 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
+# Example usage: yolo train data=SKU-110K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── SKU-110K ← downloads here (13.6 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+names:
+  0: object
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from pathlib import Path
+
+  import numpy as np
+  import polars as pl
+
+  from ultralytics.utils import TQDM
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywh
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  parent = Path(dir.parent)  # download dir
+  urls = ["http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz"]
+  download(urls, dir=parent)
+
+  # Rename directories
+  if dir.exists():
+      shutil.rmtree(dir)
+  (parent / "SKU110K_fixed").rename(dir)  # rename dir
+  (dir / "labels").mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = "image", "x1", "y1", "x2", "y2", "class", "image_width", "image_height"  # column names
+  for d in "annotations_train.csv", "annotations_val.csv", "annotations_test.csv":
+      x = pl.read_csv(dir / "annotations" / d, has_header=False, new_columns=names, infer_schema_length=None).to_numpy()  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix(".txt").__str__().replace("annotations_", ""), "w", encoding="utf-8") as f:
+          f.writelines(f"./images/{s}\n" for s in unique_images)
+      for im in TQDM(unique_images, desc=f"Converting {dir / d}"):
+          cls = 0  # single-class dataset
+          with open((dir / "labels" / im).with_suffix(".txt"), "a", encoding="utf-8") as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

vendor/ultralytics/cfg/datasets/TT100K.yaml

@@ -0,0 +1,346 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Tsinghua-Tencent 100K (TT100K) dataset https://cg.cs.tsinghua.edu.cn/traffic-sign/ by Tsinghua University
+# Documentation: https://cg.cs.tsinghua.edu.cn/traffic-sign/tutorial.html
+# Paper: Traffic-Sign Detection and Classification in the Wild (CVPR 2016)
+# License: CC BY-NC 2.0 license for non-commercial use only
+# Example usage: yolo train data=TT100K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── TT100K ← downloads here (~18 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: TT100K # dataset root dir
+train: images/train # train images (relative to 'path') 6105 images
+val: images/val # val images (relative to 'path') 7641 images (original 'other' split)
+test: images/test # test images (relative to 'path') 3071 images
+
+# Classes (221 traffic sign categories, 45 with sufficient training instances)
+names:
+  0: pl5
+  1: pl10
+  2: pl15
+  3: pl20
+  4: pl25
+  5: pl30
+  6: pl40
+  7: pl50
+  8: pl60
+  9: pl70
+  10: pl80
+  11: pl90
+  12: pl100
+  13: pl110
+  14: pl120
+  15: pm5
+  16: pm10
+  17: pm13
+  18: pm15
+  19: pm20
+  20: pm25
+  21: pm30
+  22: pm35
+  23: pm40
+  24: pm46
+  25: pm50
+  26: pm55
+  27: pm8
+  28: pn
+  29: pne
+  30: ph4
+  31: ph4.5
+  32: ph5
+  33: ps
+  34: pg
+  35: ph1.5
+  36: ph2
+  37: ph2.1
+  38: ph2.2
+  39: ph2.4
+  40: ph2.5
+  41: ph2.8
+  42: ph2.9
+  43: ph3
+  44: ph3.2
+  45: ph3.5
+  46: ph3.8
+  47: ph4.2
+  48: ph4.3
+  49: ph4.8
+  50: ph5.3
+  51: ph5.5
+  52: pb
+  53: pr10
+  54: pr100
+  55: pr20
+  56: pr30
+  57: pr40
+  58: pr45
+  59: pr50
+  60: pr60
+  61: pr70
+  62: pr80
+  63: pr90
+  64: p1
+  65: p2
+  66: p3
+  67: p4
+  68: p5
+  69: p6
+  70: p7
+  71: p8
+  72: p9
+  73: p10
+  74: p11
+  75: p12
+  76: p13
+  77: p14
+  78: p15
+  79: p16
+  80: p17
+  81: p18
+  82: p19
+  83: p20
+  84: p21
+  85: p22
+  86: p23
+  87: p24
+  88: p25
+  89: p26
+  90: p27
+  91: p28
+  92: pa8
+  93: pa10
+  94: pa12
+  95: pa13
+  96: pa14
+  97: pb5
+  98: pc
+  99: pg
+  100: ph1
+  101: ph1.3
+  102: ph1.5
+  103: ph2
+  104: ph3
+  105: ph4
+  106: ph5
+  107: pi
+  108: pl0
+  109: pl4
+  110: pl5
+  111: pl8
+  112: pl10
+  113: pl15
+  114: pl20
+  115: pl25
+  116: pl30
+  117: pl35
+  118: pl40
+  119: pl50
+  120: pl60
+  121: pl65
+  122: pl70
+  123: pl80
+  124: pl90
+  125: pl100
+  126: pl110
+  127: pl120
+  128: pm2
+  129: pm8
+  130: pm10
+  131: pm13
+  132: pm15
+  133: pm20
+  134: pm25
+  135: pm30
+  136: pm35
+  137: pm40
+  138: pm46
+  139: pm50
+  140: pm55
+  141: pn
+  142: pne
+  143: po
+  144: pr10
+  145: pr100
+  146: pr20
+  147: pr30
+  148: pr40
+  149: pr45
+  150: pr50
+  151: pr60
+  152: pr70
+  153: pr80
+  154: ps
+  155: w1
+  156: w2
+  157: w3
+  158: w5
+  159: w8
+  160: w10
+  161: w12
+  162: w13
+  163: w16
+  164: w18
+  165: w20
+  166: w21
+  167: w22
+  168: w24
+  169: w28
+  170: w30
+  171: w31
+  172: w32
+  173: w34
+  174: w35
+  175: w37
+  176: w38
+  177: w41
+  178: w42
+  179: w43
+  180: w44
+  181: w45
+  182: w46
+  183: w47
+  184: w48
+  185: w49
+  186: w50
+  187: w51
+  188: w52
+  189: w53
+  190: w54
+  191: w55
+  192: w56
+  193: w57
+  194: w58
+  195: w59
+  196: w60
+  197: w62
+  198: w63
+  199: w66
+  200: i1
+  201: i2
+  202: i3
+  203: i4
+  204: i5
+  205: i6
+  206: i7
+  207: i8
+  208: i9
+  209: i10
+  210: i11
+  211: i12
+  212: i13
+  213: i14
+  214: i15
+  215: il60
+  216: il80
+  217: il100
+  218: il110
+  219: io
+  220: ip
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  import shutil
+  from pathlib import Path
+
+  from PIL import Image
+
+  from ultralytics.utils import TQDM
+  from ultralytics.utils.downloads import download
+
+
+  def tt100k2yolo(dir):
+      """Convert TT100K annotations to YOLO format with images/{split} and labels/{split} structure."""
+      data_dir = dir / "data"
+      anno_file = data_dir / "annotations.json"
+
+      print("Loading annotations...")
+      with open(anno_file, encoding="utf-8") as f:
+          data = json.load(f)
+
+      # Build class name to index mapping from yaml
+      names = yaml["names"]
+      class_to_idx = {v: k for k, v in names.items()}
+
+      # Create directories
+      for split in ["train", "val", "test"]:
+          (dir / "images" / split).mkdir(parents=True, exist_ok=True)
+          (dir / "labels" / split).mkdir(parents=True, exist_ok=True)
+
+      print("Converting annotations to YOLO format...")
+      skipped = 0
+      for img_id, img_data in TQDM(data["imgs"].items(), desc="Processing"):
+          img_path_str = img_data["path"]
+          if "train" in img_path_str:
+              split = "train"
+          elif "test" in img_path_str:
+              split = "test"
+          else:
+              split = "val"
+
+          # Source and destination paths
+          src_img = data_dir / img_path_str
+          if not src_img.exists():
+              continue
+
+          dst_img = dir / "images" / split / src_img.name
+
+          # Get image dimensions
+          try:
+              with Image.open(src_img) as img:
+                  img_width, img_height = img.size
+          except Exception as e:
+              print(f"Error reading {src_img}: {e}")
+              continue
+
+          # Copy image to destination
+          shutil.copy2(src_img, dst_img)
+
+          # Convert annotations
+          label_file = dir / "labels" / split / f"{src_img.stem}.txt"
+          lines = []
+
+          for obj in img_data.get("objects", []):
+              category = obj["category"]
+              if category not in class_to_idx:
+                  skipped += 1
+                  continue
+
+              bbox = obj["bbox"]
+              xmin, ymin = bbox["xmin"], bbox["ymin"]
+              xmax, ymax = bbox["xmax"], bbox["ymax"]
+
+              # Convert to YOLO format (normalized center coordinates and dimensions)
+              x_center = ((xmin + xmax) / 2.0) / img_width
+              y_center = ((ymin + ymax) / 2.0) / img_height
+              width = (xmax - xmin) / img_width
+              height = (ymax - ymin) / img_height
+
+              # Clip to valid range
+              x_center = max(0, min(1, x_center))
+              y_center = max(0, min(1, y_center))
+              width = max(0, min(1, width))
+              height = max(0, min(1, height))
+
+              cls_idx = class_to_idx[category]
+              lines.append(f"{cls_idx} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n")
+
+          # Write label file
+          if lines:
+              label_file.write_text("".join(lines), encoding="utf-8")
+
+      if skipped:
+          print(f"Skipped {skipped} annotations with unknown categories")
+      print("Conversion complete!")
+
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = ["https://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/data.zip"]
+  download(urls, dir=dir, curl=True, threads=1)
+
+  # Convert
+  tt100k2yolo(dir)

vendor/ultralytics/cfg/datasets/VOC.yaml

@@ -0,0 +1,102 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Documentation: https://docs.ultralytics.com/datasets/detect/voc/
+# Example usage: yolo train data=VOC.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── VOC ← downloads here (2.8 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: VOC
+train: # train images (relative to 'path') 16551 images
+  - images/train2012
+  - images/train2007
+  - images/val2012
+  - images/val2007
+val: # val images (relative to 'path') 4952 images
+  - images/test2007
+test: # test images (optional)
+  - images/test2007
+
+# Classes
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import xml.etree.ElementTree as ET
+  from pathlib import Path
+
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils import ASSETS_URL, TQDM
+
+  def convert_label(path, lb_path, year, image_id):
+      """Converts XML annotations from VOC format to YOLO format by extracting bounding boxes and class IDs."""
+
+      def convert_box(size, box):
+          dw, dh = 1.0 / size[0], 1.0 / size[1]
+          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+          return x * dw, y * dh, w * dw, h * dh
+
+      with open(path / f"VOC{year}/Annotations/{image_id}.xml") as in_file, open(lb_path, "w", encoding="utf-8") as out_file:
+          tree = ET.parse(in_file)
+          root = tree.getroot()
+          size = root.find("size")
+          w = int(size.find("width").text)
+          h = int(size.find("height").text)
+
+          names = list(yaml["names"].values())  # names list
+          for obj in root.iter("object"):
+              cls = obj.find("name").text
+              if cls in names and int(obj.find("difficult").text) != 1:
+                  xmlbox = obj.find("bndbox")
+                  bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ("xmin", "xmax", "ymin", "ymax")])
+                  cls_id = names.index(cls)  # class id
+                  out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + "\n")
+
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [
+      f"{ASSETS_URL}/VOCtrainval_06-Nov-2007.zip",  # 446MB, 5012 images
+      f"{ASSETS_URL}/VOCtest_06-Nov-2007.zip",  # 438MB, 4953 images
+      f"{ASSETS_URL}/VOCtrainval_11-May-2012.zip",  # 1.95GB, 17126 images
+  ]
+  download(urls, dir=dir / "images", threads=3, exist_ok=True)  # download and unzip over existing (required)
+
+  # Convert
+  path = dir / "images/VOCdevkit"
+  for year, image_set in ("2012", "train"), ("2012", "val"), ("2007", "train"), ("2007", "val"), ("2007", "test"):
+      imgs_path = dir / "images" / f"{image_set}{year}"
+      lbs_path = dir / "labels" / f"{image_set}{year}"
+      imgs_path.mkdir(exist_ok=True, parents=True)
+      lbs_path.mkdir(exist_ok=True, parents=True)
+
+      with open(path / f"VOC{year}/ImageSets/Main/{image_set}.txt") as f:
+          image_ids = f.read().strip().split()
+      for id in TQDM(image_ids, desc=f"{image_set}{year}"):
+          f = path / f"VOC{year}/JPEGImages/{id}.jpg"  # old img path
+          lb_path = (lbs_path / f.name).with_suffix(".txt")  # new label path
+          f.rename(imgs_path / f.name)  # move image
+          convert_label(path, lb_path, year, id)  # convert labels to YOLO format

vendor/ultralytics/cfg/datasets/VisDrone.yaml

@@ -0,0 +1,87 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
+# Example usage: yolo train data=VisDrone.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── VisDrone ← downloads here (2.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: VisDrone # dataset root dir
+train: images/train # train images (relative to 'path') 6471 images
+val: images/val # val images (relative to 'path') 548 images
+test: images/test # test-dev images (optional) 1610 images
+
+# Classes
+names:
+  0: pedestrian
+  1: people
+  2: bicycle
+  3: car
+  4: van
+  5: truck
+  6: tricycle
+  7: awning-tricycle
+  8: bus
+  9: motor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import os
+  from pathlib import Path
+  import shutil
+
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils import ASSETS_URL, TQDM
+
+
+  def visdrone2yolo(dir, split, source_name=None):
+      """Convert VisDrone annotations to YOLO format with images/{split} and labels/{split} structure."""
+      from PIL import Image
+
+      source_dir = dir / (source_name or f"VisDrone2019-DET-{split}")
+      images_dir = dir / "images" / split
+      labels_dir = dir / "labels" / split
+      labels_dir.mkdir(parents=True, exist_ok=True)
+
+      # Move images to new structure
+      if (source_images_dir := source_dir / "images").exists():
+          images_dir.mkdir(parents=True, exist_ok=True)
+          for img in source_images_dir.glob("*.jpg"):
+              img.rename(images_dir / img.name)
+
+      for f in TQDM((source_dir / "annotations").glob("*.txt"), desc=f"Converting {split}"):
+          img_size = Image.open(images_dir / f.with_suffix(".jpg").name).size
+          dw, dh = 1.0 / img_size[0], 1.0 / img_size[1]
+          lines = []
+
+          with open(f, encoding="utf-8") as file:
+              for row in [x.split(",") for x in file.read().strip().splitlines()]:
+                  if row[4] != "0":  # Skip ignored regions
+                      x, y, w, h = map(int, row[:4])
+                      cls = int(row[5]) - 1
+                      # Convert to YOLO format
+                      x_center, y_center = (x + w / 2) * dw, (y + h / 2) * dh
+                      w_norm, h_norm = w * dw, h * dh
+                      lines.append(f"{cls} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}\n")
+
+          (labels_dir / f.name).write_text("".join(lines), encoding="utf-8")
+
+
+  # Download (ignores test-challenge split)
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [
+      f"{ASSETS_URL}/VisDrone2019-DET-train.zip",
+      f"{ASSETS_URL}/VisDrone2019-DET-val.zip",
+      f"{ASSETS_URL}/VisDrone2019-DET-test-dev.zip",
+      # f"{ASSETS_URL}/VisDrone2019-DET-test-challenge.zip",
+  ]
+  download(urls, dir=dir, threads=4)
+
+  # Convert
+  splits = {"VisDrone2019-DET-train": "train", "VisDrone2019-DET-val": "val", "VisDrone2019-DET-test-dev": "test"}
+  for folder, split in splits.items():
+      visdrone2yolo(dir, split, folder)  # convert VisDrone annotations to YOLO labels
+      shutil.rmtree(dir / folder)  # cleanup original directory

vendor/ultralytics/cfg/datasets/african-wildlife.yaml

@@ -0,0 +1,25 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# African Wildlife dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/african-wildlife/
+# Example usage: yolo train data=african-wildlife.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── african-wildlife ← downloads here (100 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: african-wildlife # dataset root dir
+train: images/train # train images (relative to 'path') 1052 images
+val: images/val # val images (relative to 'path') 225 images
+test: images/test # test images (relative to 'path') 227 images
+
+# Classes
+names:
+  0: buffalo
+  1: elephant
+  2: rhino
+  3: zebra
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/african-wildlife.zip

vendor/ultralytics/cfg/datasets/brain-tumor.yaml

@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Brain-tumor dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
+# Example usage: yolo train data=brain-tumor.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── brain-tumor ← downloads here (4.21 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: brain-tumor # dataset root dir
+train: images/train # train images (relative to 'path') 893 images
+val: images/val # val images (relative to 'path') 223 images
+
+# Classes
+names:
+  0: negative
+  1: positive
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip

vendor/ultralytics/cfg/datasets/carparts-seg.yaml

@@ -0,0 +1,44 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Carparts-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
+# Example usage: yolo train data=carparts-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── carparts-seg ← downloads here (133 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: carparts-seg # dataset root dir
+train: images/train # train images (relative to 'path') 3516 images
+val: images/val # val images (relative to 'path') 276 images
+test: images/test # test images (relative to 'path') 401 images
+
+# Classes
+names:
+  0: back_bumper
+  1: back_door
+  2: back_glass
+  3: back_left_door
+  4: back_left_light
+  5: back_light
+  6: back_right_door
+  7: back_right_light
+  8: front_bumper
+  9: front_door
+  10: front_glass
+  11: front_left_door
+  12: front_left_light
+  13: front_light
+  14: front_right_door
+  15: front_right_light
+  16: hood
+  17: left_mirror
+  18: object
+  19: right_mirror
+  20: tailgate
+  21: trunk
+  22: wheel
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/carparts-seg.zip

vendor/ultralytics/cfg/datasets/coco-pose.yaml

@@ -0,0 +1,64 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 Keypoints dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
+# Example usage: yolo train data=coco-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco-pose ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco-pose # dataset root dir
+train: train2017.txt # train images (relative to 'path') 56599 images
+val: val2017.txt # val images (relative to 'path') 2346 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://codalab.lisn.upsaclay.fr/competitions/7403
+
+# Keypoints
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes
+names:
+  0: person
+
+# Keypoint names per class
+kpt_names:
+  0:
+    - nose
+    - left_eye
+    - right_eye
+    - left_ear
+    - right_ear
+    - left_shoulder
+    - right_shoulder
+    - left_elbow
+    - right_elbow
+    - left_wrist
+    - right_wrist
+    - left_hip
+    - right_hip
+    - left_knee
+    - right_knee
+    - left_ankle
+    - right_ankle
+
+# Download script/URL (optional)
+download: |
+  from pathlib import Path
+
+  from ultralytics.utils import ASSETS_URL
+  from ultralytics.utils.downloads import download
+
+  # Download labels
+  dir = Path(yaml["path"])  # dataset root dir
+
+  urls = [f"{ASSETS_URL}/coco2017labels-pose.zip"]
+  download(urls, dir=dir.parent)
+  # Download data
+  urls = [
+      "http://images.cocodataset.org/zips/train2017.zip",  # 19G, 118k images
+      "http://images.cocodataset.org/zips/val2017.zip",  # 1G, 5k images
+      "http://images.cocodataset.org/zips/test2017.zip",  # 7G, 41k images (optional)
+  ]
+  download(urls, dir=dir / "images", threads=3)

vendor/ultralytics/cfg/datasets/coco.yaml

@@ -0,0 +1,118 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
+# Example usage: yolo train data=coco.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: |
+  from pathlib import Path
+
+  from ultralytics.utils import ASSETS_URL
+  from ultralytics.utils.downloads import download
+
+  # Download labels
+  segments = True  # segment or box labels
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [ASSETS_URL + ("/coco2017labels-segments.zip" if segments else "/coco2017labels.zip")]  # labels
+  download(urls, dir=dir.parent)
+  # Download data
+  urls = [
+      "http://images.cocodataset.org/zips/train2017.zip",  # 19G, 118k images
+      "http://images.cocodataset.org/zips/val2017.zip",  # 1G, 5k images
+      "http://images.cocodataset.org/zips/test2017.zip",  # 7G, 41k images (optional)
+  ]
+  download(urls, dir=dir / "images", threads=3)

vendor/ultralytics/cfg/datasets/coco12-formats.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO12-Formats dataset (12 images testing all supported image formats) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco12-formats/
+# Example usage: yolo train data=coco12-formats.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco12-formats ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco12-formats # dataset root dir
+train: images/train # train images (relative to 'path') 6 images
+val: images/val # val images (relative to 'path') 6 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco12-formats.zip

vendor/ultralytics/cfg/datasets/coco128-seg.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
+# Example usage: yolo train data=coco128-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco128-seg ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip

vendor/ultralytics/cfg/datasets/coco128.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
+# Example usage: yolo train data=coco128.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco128 ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip

vendor/ultralytics/cfg/datasets/coco8-grayscale.yaml

@@ -0,0 +1,103 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-Grayscale dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8-grayscale/
+# Example usage: yolo train data=coco8-grayscale.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-grayscale ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-grayscale # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+channels: 1
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-grayscale.zip

vendor/ultralytics/cfg/datasets/coco8-multispectral.yaml

@@ -0,0 +1,104 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-Multispectral dataset (COCO8 images interpolated across 10 channels in the visual spectrum) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8-multispectral/
+# Example usage: yolo train data=coco8-multispectral.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-multispectral ← downloads here (20.2 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-multispectral # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Number of multispectral image channels
+channels: 10
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-multispectral.zip

vendor/ultralytics/cfg/datasets/coco8-pose.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose/
+# Example usage: yolo train data=coco8-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-pose ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-pose # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Keypoints
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes
+names:
+  0: person
+
+# Keypoint names per class
+kpt_names:
+  0:
+    - nose
+    - left_eye
+    - right_eye
+    - left_ear
+    - right_ear
+    - left_shoulder
+    - right_shoulder
+    - left_elbow
+    - right_elbow
+    - left_wrist
+    - right_wrist
+    - left_hip
+    - right_hip
+    - left_knee
+    - right_knee
+    - left_ankle
+    - right_ankle
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-pose.zip

vendor/ultralytics/cfg/datasets/coco8-seg.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
+# Example usage: yolo train data=coco8-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-seg ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-seg # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip

vendor/ultralytics/cfg/datasets/coco8.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
+# Example usage: yolo train data=coco8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8 ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip

vendor/ultralytics/cfg/datasets/construction-ppe.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Construction-PPE dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/construction-ppe/
+# Example usage: yolo train data=construction-ppe.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── construction-ppe ← downloads here (178.4 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: construction-ppe # dataset root dir
+train: images/train # train images (relative to 'path') 1132 images
+val: images/val # val images (relative to 'path') 143 images
+test: images/test # test images (relative to 'path') 141 images
+
+# Classes
+names:
+  0: helmet
+  1: gloves
+  2: vest
+  3: boots
+  4: goggles
+  5: none
+  6: Person
+  7: no_helmet
+  8: no_goggle
+  9: no_gloves
+  10: no_boots
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/construction-ppe.zip

vendor/ultralytics/cfg/datasets/crack-seg.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Crack-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
+# Example usage: yolo train data=crack-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── crack-seg ← downloads here (91.6 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: crack-seg # dataset root dir
+train: images/train # train images (relative to 'path') 3717 images
+val: images/val # val images (relative to 'path') 112 images
+test: images/test # test images (relative to 'path') 200 images
+
+# Classes
+names:
+  0: crack
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip

vendor/ultralytics/cfg/datasets/dog-pose.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Dogs dataset http://vision.stanford.edu/aditya86/ImageNetDogs/ by Stanford
+# Documentation: https://docs.ultralytics.com/datasets/pose/dog-pose/
+# Example usage: yolo train data=dog-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dog-pose ← downloads here (337 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: dog-pose # dataset root dir
+train: images/train # train images (relative to 'path') 6773 images
+val: images/val # val images (relative to 'path') 1703 images
+
+# Keypoints
+kpt_shape: [24, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+
+# Classes
+names:
+  0: dog
+
+# Keypoint names per class
+kpt_names:
+  0:
+    - front_left_paw
+    - front_left_knee
+    - front_left_elbow
+    - rear_left_paw
+    - rear_left_knee
+    - rear_left_elbow
+    - front_right_paw
+    - front_right_knee
+    - front_right_elbow
+    - rear_right_paw
+    - rear_right_knee
+    - rear_right_elbow
+    - tail_start
+    - tail_end
+    - left_ear_base
+    - right_ear_base
+    - nose
+    - chin
+    - left_ear_tip
+    - right_ear_tip
+    - left_eye
+    - right_eye
+    - withers
+    - throat
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dog-pose.zip

vendor/ultralytics/cfg/datasets/dota8-multispectral.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA8-Multispectral dataset (DOTA8 interpolated across 10 channels in the visual spectrum) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8-multispectral.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8-multispectral ← downloads here (37.3 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: dota8-multispectral # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+
+# Number of multispectral image channels
+channels: 10
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dota8-multispectral.zip

vendor/ultralytics/cfg/datasets/dota8.yaml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA8 dataset (8 images from the DOTAv1 split) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8 ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: dota8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dota8.zip