diff --git a/.gitignore b/.gitignore
index 0a19790..f813099 100644
--- a/.gitignore
+++ b/.gitignore
@@ -172,3 +172,5 @@ cython_debug/
 
 # PyPI configuration file
 .pypirc
+zoo/*
+*.pickle
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 975030d..5ebfb66 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -5,12 +5,17 @@
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Python: Current File",
+            "name": "SSYA debug",
             "type": "debugpy",
             "request": "launch",
-            "program": "${file}",
+            "program": "${workspaceFolder}/ssya/main.py",
             "cwd": "${workspaceFolder}",
             "console": "integratedTerminal",
+            "args": [
+                "-i",
+                "${workspaceFolder}/images",
+            ],
+            ,
             "justMyCode": false,
         },
     ],
diff --git a/README.md b/README.md
index ab8c655..8d80861 100644
--- a/README.md
+++ b/README.md
@@ -1,51 +1,43 @@
-# Template : How to start and customize?
+# ssya
 
-- [ ] Create new repository from this template
-- [ ] Inside pyproject.toml rename `package_name`
-- [ ] Rename aisp_template directory to `package_name`
-- [ ] Update `README.md`
+SSYA to graficzne narzędzie do segmentacji i wyszukiwania podobnych regionów w zbiorach obrazów, wykorzystujące Segment Anything v2 (SAM2).
 
-# Template directory structure
+## Funkcje
+- Offline’owe indeksowanie i cache’owanie embeddingów z SAM2
+- Interaktywne GUI z paskami postępu i filtrowaniem według progu podobieństwa
+- Szybkie wyszukiwanie podobnych wykryć
 
-- package_name/ - Insert package code here
-- tests/ - Insert unit tests here
-- scripts/ - Insert scripts here
-- images/ - If this is CV/AI repository then insert images here
-
-# Package name
-
-Write package short description here.
-
-# Installation : Developer
-
-Use poetry to install the package in development mode.
+## Wymagania
+- Python 3.11+
 
+## Instalacja
 ```bash
-git clone {URL}
-uv sync
-uv venv
+git clone <repo-url>
+cd ssya
+pdm install
 ```
 
-# Testing   
-
-Run the tests using pytest.
-
+## Użycie
 ```bash
-uv run pytest
+ssya -i /ścieżka/do/dataset
 ```
+Jeśli nie podasz `-i`, pojawi się okno dialogowe do wyboru folderu ze zbiorami.
 
-# Release
+## Format danych
+Umieść w katalogu obrazy i pliki anotacji TXT (jedna linia na obiekt: `klasa xc yc szerokość wysokość`, wartości znormalizowane).
 
-Github workflow is created to automatically release the package to PyPI when a new tag "vX.X.X" (example v1.0.0) is pushed to the main branch. 
+## Testy
+```bash
+pdm run pytest
+```
 
+## Wydania
+Nowe tagi `vX.X.X` wrzucane na `main` są automatycznie publikowane na PyPI.  
+Możesz też ręcznie:
 ```bash
 git tag vX.X.X
 git push --tags
-```
-
-Or manually build and upload the package to PyPI using the following command.
-
-```
-uv build
+pdm build
+pdm publish
 ```
 
diff --git a/images/01101362e06fd5720647eeba5922bf3cb432f4ca.png b/images/01101362e06fd5720647eeba5922bf3cb432f4ca.png
new file mode 100644
index 0000000..13e3900
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diff --git a/images/01101362e06fd5720647eeba5922bf3cb432f4ca.txt b/images/01101362e06fd5720647eeba5922bf3cb432f4ca.txt
new file mode 100644
index 0000000..e69de29
diff --git a/images/192e2ab2dd07dfe002586af2df6fd5ad775d0073.jpg b/images/192e2ab2dd07dfe002586af2df6fd5ad775d0073.jpg
new file mode 100644
index 0000000..3de5f53
Binary files /dev/null and b/images/192e2ab2dd07dfe002586af2df6fd5ad775d0073.jpg differ
diff --git a/images/414b2c60d4399fd180320d42e7c35b2514b2e0fb.JPG b/images/414b2c60d4399fd180320d42e7c35b2514b2e0fb.JPG
new file mode 100644
index 0000000..cfaa4fb
Binary files /dev/null and b/images/414b2c60d4399fd180320d42e7c35b2514b2e0fb.JPG differ
diff --git a/images/414b2c60d4399fd180320d42e7c35b2514b2e0fb.txt b/images/414b2c60d4399fd180320d42e7c35b2514b2e0fb.txt
new file mode 100644
index 0000000..a2c5e60
--- /dev/null
+++ b/images/414b2c60d4399fd180320d42e7c35b2514b2e0fb.txt
@@ -0,0 +1 @@
+3 0.493349 0.459251 0.911729 0.442731
diff --git a/images/7e954dc0a66a0659163da55410280574388f137d.jpg b/images/7e954dc0a66a0659163da55410280574388f137d.jpg
new file mode 100644
index 0000000..9dbc16e
Binary files /dev/null and b/images/7e954dc0a66a0659163da55410280574388f137d.jpg differ
diff --git a/images/7e954dc0a66a0659163da55410280574388f137d.txt b/images/7e954dc0a66a0659163da55410280574388f137d.txt
new file mode 100644
index 0000000..312cff4
--- /dev/null
+++ b/images/7e954dc0a66a0659163da55410280574388f137d.txt
@@ -0,0 +1,2 @@
+2 0.410937 0.560937 0.268750 0.288542
+2 0.721484 0.518750 0.228906 0.179167
diff --git a/images/8f4de79e2f402d169ca9d902d8bbd45be34a6361.png b/images/8f4de79e2f402d169ca9d902d8bbd45be34a6361.png
new file mode 100644
index 0000000..0c61cb7
Binary files /dev/null and b/images/8f4de79e2f402d169ca9d902d8bbd45be34a6361.png differ
diff --git a/images/8f4de79e2f402d169ca9d902d8bbd45be34a6361.txt b/images/8f4de79e2f402d169ca9d902d8bbd45be34a6361.txt
new file mode 100644
index 0000000..c340b17
--- /dev/null
+++ b/images/8f4de79e2f402d169ca9d902d8bbd45be34a6361.txt
@@ -0,0 +1,7 @@
+6 0.415104 0.306019 0.545833 0.350926
+2 0.670052 0.399537 0.165104 0.191667
+2 0.926042 0.181019 0.020833 0.036111
+2 0.685937 0.126389 0.022917 0.025000
+2 0.890365 0.175000 0.021354 0.024074
+2 0.865104 0.165278 0.019792 0.019444
+14 0.608333 0.418981 0.016667 0.021296
diff --git a/images/image.png b/images/image.png
new file mode 100644
index 0000000..60bfd49
Binary files /dev/null and b/images/image.png differ
diff --git a/images/image.txt b/images/image.txt
new file mode 100644
index 0000000..e69de29
diff --git a/pyproject.toml b/pyproject.toml
index 79c2bf8..ea22cb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ dynamic = ["version"]
 
 dependencies = [
     "dotenv>=0.9.9",
+    "sam2>=1.1.0",
     "yaya-tools",
 ]
 
diff --git a/ssya/controllers/dataset_manager.py b/ssya/controllers/dataset_manager.py
new file mode 100644
index 0000000..d0e400f
--- /dev/null
+++ b/ssya/controllers/dataset_manager.py
@@ -0,0 +1,77 @@
+import logging
+from pathlib import Path
+
+import cv2
+import numpy as np
+from tqdm import tqdm
+from yaya_tools.helpers.dataset import load_directory_images_annotatations
+
+from ssya.controllers.features_index import FeatureIndex
+from ssya.controllers.sam2_wrapper import Sam2Runner
+from ssya.models.detection import Detection
+
+logger = logging.getLogger(__name__)
+
+
+class DatasetManager:
+    """Loads dataset, detections, builds/loads feature index."""
+
+    def __init__(self, root: Path):
+        self.root = root
+        ann_map = load_directory_images_annotatations(str(root))
+        self.images: list[str] = list(ann_map.keys())
+        self.ann_map = ann_map
+        self.detections: dict[str, list[Detection]] = {}
+        for img_idx, img_path in enumerate(self.images):
+            if not ann_map[img_path]:
+                self.detections[img_path] = []
+                continue
+            with open(root / ann_map[img_path]) as f:
+                lines = [l.split() for l in f]
+            self.detections[img_path] = [
+                Detection(int(cls), (float(xc), float(yc), float(w), float(h)), img_idx) for cls, xc, yc, w, h in lines
+            ]
+        logger.info("Dataset: %d images (%d with annotations)", len(self.images), len(self.detections))
+
+        # Build or load feature index ---------------------------------
+        self.index_path = root / "features.pickle"
+        if self.index_path.exists():
+            logger.info("Loading cached features …")
+            self.fidx = FeatureIndex.load(self.index_path)
+        else:
+            self.fidx = FeatureIndex()
+            self._build_index()
+            self.fidx.save(self.index_path)
+
+        # Detections : Update with embeddings from the index
+        for img_path, dets in self.detections.items():
+            self.detections[img_path] = self.fidx.get_features(dets)
+
+    # ------------------------------------------------------------------
+
+    def _build_index(self) -> None:
+        sam = Sam2Runner()
+        logger.info("Building feature index (SAM2)…")
+        for img_idx, img_path in enumerate(tqdm(self.images, desc="Images")):
+            img = cv2.imread(str(self.root / img_path))
+            if img is None:
+                continue
+            for det_idx, det in enumerate(self.detections[img_path]):
+                mask, emb = sam.mask_and_embed(img, det.bbox_pixels(img.shape[1], img.shape[0]))
+                det.embedding = emb
+                self.fidx.add(img_idx, det_idx, emb)
+
+    # ------------------------------------------------------------------
+
+    # Convenience helpers used by GUI ----------------------------------
+    def image(self, idx: int) -> np.ndarray:
+        """Get image at index `idx`."""
+        return cv2.imread(str(self.root / self.images[idx]))
+
+    def image_detections(self, idx: int) -> list[Detection]:
+        """Get detections for the image at index `idx`."""
+        return self.detections[self.images[idx]]
+
+    def image_count(self) -> int:
+        """Get the number of images in the dataset."""
+        return len(self.images)
diff --git a/ssya/controllers/features_index.py b/ssya/controllers/features_index.py
new file mode 100644
index 0000000..0c021e5
--- /dev/null
+++ b/ssya/controllers/features_index.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+import logging
+import pickle
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from ssya.helpers.metrics import cosine_similarity
+from ssya.models.detection import Detection  # type: ignore
+
+logger = logging.getLogger(__name__)
+
+
+class FeatureIndex:
+    """Persistent RAM index: list of (image_idx, det_idx, embedding)."""
+
+    def __init__(self, entries: list[dict[str, Any]] | None = None):
+        """Initialize with existing entries or empty."""
+        if entries is not None:
+            self.entries = entries
+        else:
+            self.entries: list[dict[str, Any]] = []
+
+    def add(self, image_idx: int, det_idx: int, emb: np.ndarray):
+        self.entries.append({"image_idx": image_idx, "det_idx": det_idx, "emb": emb})
+
+    def save(self, path: Path):
+        with open(path, "wb") as f:
+            pickle.dump(self.entries, f, protocol=pickle.HIGHEST_PROTOCOL)
+
+    def get_features(self, detections: list[Detection]) -> list[Detection]:
+        """Update detections list with embeddings from the index."""
+        for det in detections:
+            if det.embedding is None:
+                for e in self.entries:
+                    if e["image_idx"] == det.image_idx and e["det_idx"] == det.class_id:
+                        det.embedding = e["emb"]
+                        break
+
+        return detections
+
+    @classmethod
+    def load(cls, path: Path) -> FeatureIndex:
+        with open(path, "rb") as f:
+            entries = pickle.load(f)
+
+        return cls(entries)
+
+    def get_similar_images(self, ref_emb: np.ndarray, thresh: float) -> set[int]:
+        """Find images with at least one detection above the threshold."""
+        imgs: set[int] = set()
+        for e in self.entries:
+            if cosine_similarity(ref_emb, e["emb"]) >= thresh:
+                imgs.add(e["image_idx"])
+
+        return imgs
diff --git a/ssya/controllers/sam2_wrapper.py b/ssya/controllers/sam2_wrapper.py
new file mode 100644
index 0000000..609b41a
--- /dev/null
+++ b/ssya/controllers/sam2_wrapper.py
@@ -0,0 +1,95 @@
+from __future__ import annotations
+
+import logging
+import os
+from pathlib import Path
+
+import cv2  # type: ignore
+import numpy as np  # type: ignore
+import requests
+import torch
+import torch.nn.functional as F
+from sam2.build_sam import build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+
+logger = logging.getLogger(__name__)
+
+
+def gem_pooling(features: torch.Tensor, mask: torch.Tensor, p: float = 3.0):
+    """
+    GeM pooling z maską: features (B, C, H, W), mask (B, 1, H, W) – bool/int.
+    Zwraca (B, C)
+    """
+    eps = 1e-6
+    masked = features * mask  # (B, C, H, W)
+    pooled = F.avg_pool2d(masked.clamp(min=eps).pow(p), kernel_size=masked.shape[-2:])  # (B, C, 1, 1)
+    pooled = pooled.pow(1.0 / p).squeeze(-1).squeeze(-1)
+    # uwzględnij liczbę aktywnych pikseli
+    denom = mask.flatten(2).sum(-1).clamp(min=1e-6)  # (B,1)
+    pooled = pooled / denom
+    return F.normalize(pooled, dim=-1)
+
+
+class Sam2Runner:
+    """Light wrapper that exposes mask + embedding for a bbox."""
+
+    _instance = None  # singleton for reuse
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._init_model()
+        return cls._instance
+
+    # ------------------------------------------------------------------
+
+    def _init_model(self) -> None:
+        """Initialize the SAM2 model."""
+        model_path = "zoo/sam2_tiny.pth"
+        if not os.path.exists(model_path):
+            url = "https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt"
+            Path(model_path).parent.mkdir(parents=True, exist_ok=True)
+            logger.info("Downloading SAM2 weights …")
+            with requests.get(url, stream=True) as r, open(model_path, "wb") as f:
+                for chunk in r.iter_content(1 << 14):
+                    f.write(chunk)
+        cfg = "configs/sam2.1/sam2.1_hiera_t.yaml"
+        device = "cuda" if os.environ.get("CUDA_VISIBLE_DEVICES", "") else "cpu"
+        model = build_sam2(cfg, model_path).to(device).eval()
+        self._predictor = SAM2ImagePredictor(model)
+        self.device = device
+
+    # ------------------------------------------------------------------
+
+    def mask_and_embed(self, img_bgr: np.ndarray, box_px: tuple[int, int, int, int]) -> tuple[np.ndarray, np.ndarray]:
+        img_rgb = img_bgr[:, :, ::-1].copy()
+        self._predictor.set_image(img_rgb)  # ← tutaj SAM2 wylicza embedding
+
+        # ---------- segmentacja ----------
+        masks, _, _ = self._predictor.predict(
+            box=np.array([box_px[0], box_px[1], box_px[0] + box_px[2], box_px[1] + box_px[3]]),
+            multimask_output=False,
+            return_logits=False,
+        )
+        mask_hr = masks[0]  # (H, W) bool
+
+        # ---------- mapa cech ----------
+        feat_container = getattr(self._predictor, "_features", None)
+        if feat_container is None:
+            raise RuntimeError("Brak _features w predictorze — sprawdź wersję biblioteki")
+
+        # słownik → weź 'image_embed'
+        feat_map = feat_container.get("image_embed", None) if isinstance(feat_container, dict) else feat_container
+
+        if feat_map is None or not torch.is_tensor(feat_map):
+            raise RuntimeError("Nie znalazłem tensora z mapą cech w _features")
+
+        B, C, h, w = feat_map.shape
+
+        mask_lr = cv2.resize(mask_hr.astype(np.uint8), (w, h), interpolation=cv2.INTER_NEAREST).astype(bool)
+        mask_t = torch.from_numpy(mask_lr).to(feat_map.device).view(1, 1, h, w)
+
+        emb_t = gem_pooling(feat_map, mask_t, p=3.0)  # z poprzedniej odpowiedzi
+        emb = emb_t.cpu().numpy()[0]  # (C,)
+
+        return mask_hr, emb
diff --git a/ssya/helpers/metrics.py b/ssya/helpers/metrics.py
new file mode 100644
index 0000000..f7405c9
--- /dev/null
+++ b/ssya/helpers/metrics.py
@@ -0,0 +1,9 @@
+import numpy as np
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    if a is None or b is None:
+        return 0.0
+    if np.linalg.norm(a) == 0 or np.linalg.norm(b) == 0:
+        return 0.0
+    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
diff --git a/ssya/main.py b/ssya/main.py
index 584e443..20daf7a 100644
--- a/ssya/main.py
+++ b/ssya/main.py
@@ -1,68 +1,186 @@
+from __future__ import annotations
+
 import argparse
 import logging
+import sys
+from pathlib import Path
 
-from yaya_tools import __version__
-from yaya_tools.helpers.annotations import (
-    annotations_load_as_sv,
-)
-from yaya_tools.helpers.dataset import (
-    get_images_annotated,
-    load_directory_images_annotatations,
+import numpy as np  # type: ignore
+from PyQt5.QtCore import Qt
+from PyQt5.QtWidgets import (
+    QApplication,
+    QFileDialog,
+    QLabel,
+    QListWidget,
+    QMessageBox,
+    QPushButton,
+    QSlider,
+    QSplitter,
+    QVBoxLayout,
+    QWidget,
 )
 
+from ssya.controllers.dataset_manager import DatasetManager
+from ssya.controllers.sam2_wrapper import Sam2Runner
+from ssya.models.detection import Detection
+from ssya.widgets.image_viewer import ImageViewer  # type: ignore
+
 logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+
+
+class MainWindow(QWidget):
+    def __init__(self, dm: DatasetManager):
+        super().__init__()
+        self.dm = dm
+        self.setWindowTitle("SSYA – embeddings cache & filtering")
+
+        # widgets ------------------------------------------------------
+        self.viewer = ImageViewer()
+
+        self.files_list = QListWidget()
+        self.files_list.addItems(dm.images)
+        self.dets_list = QListWidget()
+
+        self.slider = QSlider(Qt.Horizontal, minimum=0, maximum=100, value=50)
+        btn_similar = QPushButton("🔍 Find similar")
+        btn_clear = QPushButton("❌ Clear filter")
+
+        # layout -------------------------------------------------------
+        side = QVBoxLayout()
+        side.addWidget(QLabel("Images"))
+        side.addWidget(self.files_list)
+        side.addWidget(QLabel("Detections"))
+        side.addWidget(self.dets_list)
+        side.addWidget(QLabel("Threshold"))
+        side.addWidget(self.slider)
+        side.addWidget(btn_similar)
+        side.addWidget(btn_clear)
+        side.addStretch()
+
+        splitter = QSplitter()
+        splitter.addWidget(self.viewer)
+        side_widget = QWidget()
+        side_widget.setLayout(side)
+        splitter.addWidget(side_widget)
+        splitter.setSizes([800, 300])
+
+        QVBoxLayout(self).addWidget(splitter)
+
+        # state --------------------------------------------------------
+        self.cur_img_idx = 0
+        self.selected_mask: list[np.ndarray] = []
+        self.selected_detection: Detection | None = None
+
+        # signals ------------------------------------------------------
+        self.files_list.currentRowChanged.connect(self.on_file_select)
+        self.dets_list.currentRowChanged.connect(self.on_det_select)
+        btn_similar.clicked.connect(self.on_find_similar)
+        btn_clear.clicked.connect(self.on_clear_filter)
+
+        self.display_image(0)
+
+    # ------------------------------------------------------------------
+
+    def display_image(self, idx: int) -> None:
+        """Display image and its detections."""
+        self.cur_img_idx = idx
+        img = self.dm.image(idx)
+        dets = self.dm.image_detections(idx)
+        self.dets_list.clear()
+        for i, d in enumerate(dets):
+            self.dets_list.addItem(f"#{i} cls={d.class_id}")
+        self.selected_mask = []
+        self.viewer.show_image(
+            img,
+            dets,
+            self.selected_mask,
+            selected_detection=self.selected_detection,
+            sim_threshold=self.slider.value() / 100.0,
+        )
+
+    # ------------------------------------------------------------------
+
+    def on_file_select(self, row: int):
+        if row >= 0:
+            self.display_image(row)
+
+    # ------------------------------------------------------------------
+
+    def on_det_select(self, row: int):
+        if row < 0:
+            return
+        det = self.dm.image_detections(self.cur_img_idx)[row]
+        img = self.dm.image(self.cur_img_idx)
+
+        # Always re-run SAM2 to get the embedding
+        sam = Sam2Runner()
+        mask, emb = sam.mask_and_embed(img, det.bbox_pixels(img.shape[1], img.shape[0]))
+        det.embedding = emb
+
+        self.selected_mask = [mask]
+        self.viewer.show_image(
+            img,
+            self.dm.image_detections(self.cur_img_idx),
+            self.selected_mask,
+            selected_detection=det,
+            sim_threshold=self.slider.value() / 100.0,
+        )
+
+    # ------------------------------------------------------------------
+
+    def on_find_similar(self) -> None:
+        """Find images with at least one detection above the threshold."""
+        row = self.dets_list.currentRow()
+        if row < 0:
+            QMessageBox.warning(self, "Select", "Select a detection first")
+            return
+
+        det = self.dm.image_detections(self.cur_img_idx)[row]
+        if det.embedding is None:
+            QMessageBox.warning(self, "No embedding", "Embedding missing – click detection again")
+            return
+
+        self.selected_detection = det
+
+        thresh = self.slider.value() / 100.0
+        keep = self.dm.fidx.get_similar_images(det.embedding, thresh)
+
+        self.files_list.clear()
+        self.files_list.addItems([self.dm.images[i] for i in sorted(keep)])
+
+        if keep:
+            self.files_list.setCurrentRow(0)
+
+    def on_clear_filter(self):
+        self.files_list.clear()
+        self.files_list.addItems(self.dm.images)
+        self.files_list.setCurrentRow(self.cur_img_idx)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-i", "--dataset_path", type=Path)
+    args = ap.parse_args()
+
+    app = QApplication(sys.argv)
 
+    ds_path = args.dataset_path
+    if ds_path is None:
+        dlg = QFileDialog()
+        dlg.setFileMode(QFileDialog.Directory)
+        if dlg.exec_():
+            sel = dlg.selectedFiles()
+            if sel:
+                ds_path = Path(sel[0])
+    if ds_path is None:
+        sys.exit("Dataset path missing")
 
-def logging_terminal_setup() -> None:
-    """
-    Setup logging for the application.
-
-    Parameters
-    ----------
-    path_field : str
-        Field in the config file that contains the path to the log file.
-        Default is "path".
-    is_terminal : bool
-        If True, logs will be printed to the terminal.
-        Default is True.
-    """
-    logging.getLogger().setLevel(logging.DEBUG)  # Ensure log level is set to DEBUG
-    formatter = logging.Formatter("%(asctime)s %(levelname)s: %(message)s")
-    console = logging.StreamHandler()
-    console.setLevel(logging.DEBUG)
-    console.setFormatter(formatter)
-    logging.getLogger().addHandler(console)
-    logger.info("\n\n###### Logging start of terminal session ######\n")
-
-
-def main() -> None:
-    """
-    Main function for dataset management
-
-    Returns
-    -------
-    None
-    """
-    logging_terminal_setup()
-
-    # Argument parser
-    parser = argparse.ArgumentParser(add_help=False, description="YAYa dataset management tool")
-    parser.add_argument("-i", "--dataset_path", type=str, required=True, help="Path to the dataset folder")
-    parser.add_argument("-h", "--help", action="help", help="Show this help message and exit.")
-    parser.add_argument("-v", action="version", version=__version__, help="Show version and exit.")
-    args = parser.parse_args()
-
-    dataset_path = args.dataset_path
-
-    # All images : with optional annotation filename
-    all_images_annotations: dict[str, str | None] = load_directory_images_annotatations(dataset_path)
-    # Images annotated : Filter only
-    images_annotated: list[str] = get_images_annotated(all_images_annotations)
-
-    # All annotations as SV : Get
-    all_annotations_sv, all_negatives = annotations_load_as_sv(all_images_annotations, dataset_path)
-
-    # TODO
+    dm = DatasetManager(ds_path)
+    win = MainWindow(dm)
+    win.resize(1400, 800)
+    win.show()
+    sys.exit(app.exec_())
 
 
 if __name__ == "__main__":
diff --git a/ssya/models/detection.py b/ssya/models/detection.py
new file mode 100644
index 0000000..0da8e37
--- /dev/null
+++ b/ssya/models/detection.py
@@ -0,0 +1,18 @@
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass
+class Detection:
+    class_id: int
+    bbox_norm: tuple[float, float, float, float]
+    image_idx: int  # index in DatasetManager.images
+    embedding: np.ndarray | None = None
+
+    def bbox_pixels(self, img_w: int, img_h: int) -> tuple[int, int, int, int]:
+        xc, yc, w, h = self.bbox_norm
+        w_px, h_px = int(w * img_w), int(h * img_h)
+        x1 = int((xc * img_w) - w_px / 2)
+        y1 = int((yc * img_h) - h_px / 2)
+        return x1, y1, w_px, h_px
diff --git a/ssya/widgets/image_viewer.py b/ssya/widgets/image_viewer.py
new file mode 100644
index 0000000..4de4721
--- /dev/null
+++ b/ssya/widgets/image_viewer.py
@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+import logging
+
+import cv2  # type: ignore
+import numpy as np  # type: ignore
+from PyQt5.QtCore import Qt
+from PyQt5.QtGui import QImage, QPixmap
+from PyQt5.QtWidgets import (
+    QLabel,
+    QSizePolicy,
+    QVBoxLayout,
+    QWidget,
+)
+
+from ssya.helpers.metrics import cosine_similarity
+from ssya.models.detection import Detection  # type: ignore
+
+logger = logging.getLogger(__name__)
+
+
+class ImageViewer(QWidget):
+    """Widget to display images with detections and masks."""
+
+    def __init__(self):
+        super().__init__()
+        self.lbl = QLabel(alignment=Qt.AlignCenter)
+        self.lbl.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
+        self.lbl.setScaledContents(True)
+        # Fixed with and height set, 1920 - 200 , 1080 - 200
+        self.lbl.setFixedWidth(1620)
+        self.lbl.setFixedHeight(880)
+
+        QVBoxLayout(self).addWidget(self.lbl)
+
+    def show_image(
+        self,
+        img_bgr: np.ndarray,
+        dets: list[Detection],
+        masks: list[np.ndarray],
+        selected_detection: Detection | None = None,
+        sim_threshold: float = 0.5,
+    ):
+        """Display image with detections and masks."""
+        if img_bgr is None:
+            return
+
+        disp = img_bgr.copy()
+        h, w, _ = disp.shape
+
+        for m in masks:
+            disp[m > 0] = (disp[m > 0] * 0.4 + np.array([255, 0, 0]) * 0.6).astype(np.uint8)
+
+        for d in dets:
+            x, y, bw, bh = d.bbox_pixels(w, h)
+            cv2.rectangle(disp, (x, y), (x + bw, y + bh), (255, 0, 0), 1)
+            cv2.putText(disp, str(d.class_id), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 1)
+
+        if selected_detection is not None:
+            for det in dets:
+                sim = cosine_similarity(selected_detection.embedding, det.embedding)
+                x, y, bw, bh = det.bbox_pixels(w, h)
+                cv2.putText(disp, f"{sim:.2f}", (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+                if sim < sim_threshold:
+                    continue
+
+                cv2.rectangle(disp, (x, y), (x + bw, y + bh), (0, 255, 0), thickness=2)
+
+        rgb = cv2.cvtColor(disp, cv2.COLOR_BGR2RGB)
+        qimg = QImage(rgb.data, w, h, QImage.Format_RGB888)
+        self.lbl.setPixmap(QPixmap.fromImage(qimg))
diff --git a/uv.lock b/uv.lock
index 354a1ea..002dfa4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -43,6 +43,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
+
 [[package]]
 name = "certifi"
 version = "2025.7.9"
@@ -205,6 +211,29 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d7/d4/1d85a1996b6188cd2713230e002d79a6f3a289bb17cef600cba385848b72/fonttools-4.58.5-py3-none-any.whl", hash = "sha256:e48a487ed24d9b611c5c4b25db1e50e69e9854ca2670e39a3486ffcd98863ec4", size = 1115318 },
 ]
 
+[[package]]
+name = "fsspec"
+version = "2025.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/00/f7/27f15d41f0ed38e8fcc488584b57e902b331da7f7c6dcda53721b15838fc/fsspec-2025.5.1.tar.gz", hash = "sha256:2e55e47a540b91843b755e83ded97c6e897fa0942b11490113f09e9c443c2475", size = 303033 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bb/61/78c7b3851add1481b048b5fdc29067397a1784e2910592bc81bb3f608635/fsspec-2025.5.1-py3-none-any.whl", hash = "sha256:24d3a2e663d5fc735ab256263c4075f374a174c3410c0b25e5bd1970bceaa462", size = 199052 },
+]
+
+[[package]]
+name = "hydra-core"
+version = "1.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "antlr4-python3-runtime" },
+    { name = "omegaconf" },
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6d/8e/07e42bc434a847154083b315779b0a81d567154504624e181caf2c71cd98/hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824", size = 3263494 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c6/50/e0edd38dcd63fb26a8547f13d28f7a008bc4a3fd4eb4ff030673f22ad41a/hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b", size = 154547 },
+]
+
 [[package]]
 name = "identify"
 version = "2.6.10"
@@ -232,6 +261,29 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050 },
 ]
 
+[[package]]
+name = "iopath"
+version = "0.1.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "portalocker" },
+    { name = "tqdm" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/73/b3d451dfc523756cf177d3ebb0af76dc7751b341c60e2a21871be400ae29/iopath-0.1.10.tar.gz", hash = "sha256:3311c16a4d9137223e20f141655759933e1eda24f8bff166af834af3c645ef01", size = 42226 }
+
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899 },
+]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.8"
@@ -255,6 +307,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4a/c9/9642ea855604aeb2968a8e145fc662edf61db7632ad2e4fb92424be6b6c0/kiwisolver-1.4.8-cp311-cp311-win_arm64.whl", hash = "sha256:16523b40aab60426ffdebe33ac374457cf62863e330a90a0383639ce14bf44b2", size = 65311 },
 ]
 
+[[package]]
+name = "markupsafe"
+version = "3.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b2/97/5d42485e71dfc078108a86d6de8fa46db44a1a9295e89c5d6d4a06e23a62/markupsafe-3.0.2.tar.gz", hash = "sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0", size = 20537 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6b/28/bbf83e3f76936960b850435576dd5e67034e200469571be53f69174a2dfd/MarkupSafe-3.0.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9025b4018f3a1314059769c7bf15441064b2207cb3f065e6ea1e7359cb46db9d", size = 14353 },
+    { url = "https://files.pythonhosted.org/packages/6c/30/316d194b093cde57d448a4c3209f22e3046c5bb2fb0820b118292b334be7/MarkupSafe-3.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:93335ca3812df2f366e80509ae119189886b0f3c2b81325d39efdb84a1e2ae93", size = 12392 },
+    { url = "https://files.pythonhosted.org/packages/f2/96/9cdafba8445d3a53cae530aaf83c38ec64c4d5427d975c974084af5bc5d2/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2cb8438c3cbb25e220c2ab33bb226559e7afb3baec11c4f218ffa7308603c832", size = 23984 },
+    { url = "https://files.pythonhosted.org/packages/f1/a4/aefb044a2cd8d7334c8a47d3fb2c9f328ac48cb349468cc31c20b539305f/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a123e330ef0853c6e822384873bef7507557d8e4a082961e1defa947aa59ba84", size = 23120 },
+    { url = "https://files.pythonhosted.org/packages/8d/21/5e4851379f88f3fad1de30361db501300d4f07bcad047d3cb0449fc51f8c/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1e084f686b92e5b83186b07e8a17fc09e38fff551f3602b249881fec658d3eca", size = 23032 },
+    { url = "https://files.pythonhosted.org/packages/00/7b/e92c64e079b2d0d7ddf69899c98842f3f9a60a1ae72657c89ce2655c999d/MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d8213e09c917a951de9d09ecee036d5c7d36cb6cb7dbaece4c71a60d79fb9798", size = 24057 },
+    { url = "https://files.pythonhosted.org/packages/f9/ac/46f960ca323037caa0a10662ef97d0a4728e890334fc156b9f9e52bcc4ca/MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:5b02fb34468b6aaa40dfc198d813a641e3a63b98c2b05a16b9f80b7ec314185e", size = 23359 },
+    { url = "https://files.pythonhosted.org/packages/69/84/83439e16197337b8b14b6a5b9c2105fff81d42c2a7c5b58ac7b62ee2c3b1/MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0bff5e0ae4ef2e1ae4fdf2dfd5b76c75e5c2fa4132d05fc1b0dabcd20c7e28c4", size = 23306 },
+    { url = "https://files.pythonhosted.org/packages/9a/34/a15aa69f01e2181ed8d2b685c0d2f6655d5cca2c4db0ddea775e631918cd/MarkupSafe-3.0.2-cp311-cp311-win32.whl", hash = "sha256:6c89876f41da747c8d3677a2b540fb32ef5715f97b66eeb0c6b66f5e3ef6f59d", size = 15094 },
+    { url = "https://files.pythonhosted.org/packages/da/b8/3a3bd761922d416f3dc5d00bfbed11f66b1ab89a0c2b6e887240a30b0f6b/MarkupSafe-3.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:70a87b411535ccad5ef2f1df5136506a10775d267e197e4cf531ced10537bd6b", size = 15521 },
+]
+
 [[package]]
 name = "matplotlib"
 version = "3.10.3"
@@ -280,6 +350,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a6/da/948a017c3ea13fd4a97afad5fdebe2f5bbc4d28c0654510ce6fd6b06b7bd/matplotlib-3.10.3-cp311-cp311-win_amd64.whl", hash = "sha256:eef6ed6c03717083bc6d69c2d7ee8624205c29a8e6ea5a31cd3492ecdbaee1e1", size = 8065492 },
 ]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 },
+]
+
 [[package]]
 name = "mypy"
 version = "1.15.0"
@@ -308,6 +387,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "networkx"
+version = "3.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037", size = 2471065 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406 },
+]
+
 [[package]]
 name = "nodeenv"
 version = "1.9.1"
@@ -335,6 +423,152 @@ wheels = [
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+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.6.4.1"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/af/eb/ff4b8c503fa1f1796679dce648854d58751982426e4e4b37d6fce49d259c/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08ed2686e9875d01b58e3cb379c6896df8e76c75e0d4a7f7dace3d7b6d9ef8eb", size = 393138322 },
+]
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+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.6.80"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/49/60/7b6497946d74bcf1de852a21824d63baad12cd417db4195fc1bfe59db953/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6768bad6cab4f19e8292125e5f1ac8aa7d1718704012a0e3272a6f61c4bce132", size = 8917980 },
+    { url = "https://files.pythonhosted.org/packages/a5/24/120ee57b218d9952c379d1e026c4479c9ece9997a4fb46303611ee48f038/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a3eff6cdfcc6a4c35db968a06fcadb061cbc7d6dde548609a941ff8701b98b73", size = 8917972 },
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.6.77"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/75/2e/46030320b5a80661e88039f59060d1790298b4718944a65a7f2aeda3d9e9/nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:35b0cc6ee3a9636d5409133e79273ce1f3fd087abb0532d2d2e8fff1fe9efc53", size = 23650380 },
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diff --git a/zoo/.gitkeep b/zoo/.gitkeep
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diff --git a/zoo/.gitkepp b/zoo/.gitkepp
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