Upload folder using huggingface_hub

.flake8

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+[flake8]
+max-line-length = 100

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

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+3.12

Img2GPS/Release_post_process.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5638b0d1",
+      "metadata": {
+        "id": "5638b0d1"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install exifread"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Extracting GPS Information from Images\n",
+        "\n",
+        "(You will need to modify this script based on how your dataset is stored in order to execute the code.).\n"
+      ],
+      "metadata": {
+        "id": "yT4UQMosNN02"
+      },
+      "id": "yT4UQMosNN02"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "# train_or_test_or_validation could be either train, test, or validation.\n",
+        "train_or_test_or_validation = \"train\" # it could also be test or validation\n",
+        "PATH_TO_YOUR_DATA_FOLDER = \"{PATH TO YOUR DATA FOLDER}\"\n",
+        "directory_path = f\"{PATH_TO_YOUR_DATA_FOLDER}/{train_or_test_or_validation}\"\n",
+        "output_csv = \"metadata.csv\"\n",
+        "output_csv = os.path.join(directory_path, output_csv)"
+      ],
+      "metadata": {
+        "id": "7QcjgXtAQB5Q"
+      },
+      "id": "7QcjgXtAQB5Q",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e1713e4e-a848-42e9-b8d8-d1273f1b9689",
+      "metadata": {
+        "id": "e1713e4e-a848-42e9-b8d8-d1273f1b9689"
+      },
+      "outputs": [],
+      "source": [
+        "import exifread, csv\n",
+        "\n",
+        "def get_exif_data(image_path):\n",
+        "    with open(image_path, 'rb') as image_file:\n",
+        "        tags = exifread.process_file(image_file)\n",
+        "    return tags\n",
+        "\n",
+        "def export_exif_to_json(exif_data, output_file):\n",
+        "    # Convert tags to a serializable format\n",
+        "    exif_data_serializable = {str(tag): str(value) for tag, value in exif_data.items()}\n",
+        "    with open(output_file, 'w') as json_file:\n",
+        "        json.dump(exif_data_serializable, json_file, indent=4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "80fb378d",
+      "metadata": {
+        "id": "80fb378d"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to convert GPS coordinates in degrees, minutes, and seconds to decimal degrees\n",
+        "def convert_to_decimal_degrees(value):\n",
+        "    d, m, s = value.values\n",
+        "    return d.num / d.den + (m.num / m.den) / 60 + (s.num / s.den) / 3600"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### You will need to create subfolders in {PATH_TO_YOUR_DATA_FOLDER} for each split (train/test/validation) or just (train/test). Next, place the corresponding images into each split after randomly shuffling them. Then, create a metadata.csv file for each split and place it in the corresponding directory. Note that the current code only works for jpeg images. If the exported images are in some other format, convert them to .jpg before running this code."
+      ],
+      "metadata": {
+        "id": "yaoJPVKrNq9N"
+      },
+      "id": "yaoJPVKrNq9N"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "be3c347d",
+      "metadata": {
+        "id": "be3c347d"
+      },
+      "outputs": [],
+      "source": [
+        "with open(output_csv, mode='w', newline='') as csv_file:\n",
+        "    fieldnames = ['file_name', 'Latitude', 'Longitude']\n",
+        "    writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n",
+        "\n",
+        "    # Write the header row\n",
+        "    writer.writeheader()\n",
+        "    for filename in os.listdir(directory_path):\n",
+        "        if os.path.isfile(os.path.join(directory_path, filename)):\n",
+        "            exif_data = get_exif_data(os.path.join(directory_path, filename))\n",
+        "            if exif_data:\n",
+        "                gps_latitude = exif_data.get('GPS GPSLatitude', None)\n",
+        "                gps_latitude_ref = exif_data.get('GPS GPSLatitudeRef', None)\n",
+        "                gps_longitude = exif_data.get('GPS GPSLongitude', None)\n",
+        "                gps_longitude_ref = exif_data.get('GPS GPSLongitudeRef', None)\n",
+        "                if gps_latitude and gps_longitude:\n",
+        "                    # Convert latitude and longitude to decimal degrees\n",
+        "                    latitude = convert_to_decimal_degrees(gps_latitude)\n",
+        "                    longitude = convert_to_decimal_degrees(gps_longitude)\n",
+        "\n",
+        "                    # Adjust for N/S and E/W reference\n",
+        "                    if gps_latitude_ref.values[0] == 'S':\n",
+        "                        latitude = -latitude\n",
+        "                    if gps_longitude_ref.values[0] == 'W':\n",
+        "                        longitude = -longitude\n",
+        "\n",
+        "                    # Write the data to the CSV file\n",
+        "                    writer.writerow({'file_name': filename, 'Latitude': latitude, 'Longitude': longitude})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Uploading and Reading a Dataset on Hugging Face"
+      ],
+      "metadata": {
+        "id": "WEkVLwUcQ7PV"
+      },
+      "id": "WEkVLwUcQ7PV"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install datasets"
+      ],
+      "metadata": {
+        "id": "xtmhkAUSRBxp",
+        "outputId": "cab82646-49d6-431e-bb91-a5fee48296f6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "xtmhkAUSRBxp",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting datasets\n",
+            "  Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
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+            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
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+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+            "Collecting multiprocess<0.70.17 (from datasets)\n",
+            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
+            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
+            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.10)\n",
+            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.26.2)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\n",
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+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.17.1)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.4.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
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+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
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+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
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+            "Downloading datasets-3.1.0-py3-none-any.whl (480 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
+            "  Attempting uninstall: fsspec\n",
+            "    Found existing installation: fsspec 2024.10.0\n",
+            "    Uninstalling fsspec-2024.10.0:\n",
+            "      Successfully uninstalled fsspec-2024.10.0\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

Img2GPS/eval_project_a.py

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+from __future__ import annotations
+
+import argparse
+import importlib.util
+import math
+import os
+import sys
+import time
+from typing import Any, Iterable, List, Tuple
+
+import numpy as np
+import pandas as pd
+import torch
+
+
+def _dynamic_import(module_path: str, module_name: str):
+    spec = importlib.util.spec_from_file_location(module_name, module_path)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def _instantiate_model(model_module, weights_path_override: str | None = None) -> Any:
+    """
+    Instantiate the student's model in a way that avoids automatic weight loading
+    inside their constructor (so we can control loading ourselves).
+    """
+    # Prefer explicit class if available so we can pass a sentinel weights path
+    if hasattr(model_module, "Model"):
+        ModelCls = getattr(model_module, "Model")
+        try:
+            # Pass a non-existent path so student's loader skips default weights
+            sentinel = weights_path_override or "__no_weights__.pth"
+            return ModelCls(weights_path=sentinel)
+        except TypeError:
+            # Constructor may not accept weights_path
+            return ModelCls()
+        except Exception:
+            # Fall back to get_model
+            if hasattr(model_module, "get_model") and callable(model_module.get_model):
+                return model_module.get_model()
+            raise
+    # Otherwise, try factory
+    if hasattr(model_module, "get_model") and callable(model_module.get_model):
+        try:
+            return model_module.get_model()
+        except Exception:
+            # As a last resort, try common class names without args
+            for cls_name in ["IMG2GPS", "Model"]:
+                if hasattr(model_module, cls_name):
+                    try:
+                        return getattr(model_module, cls_name)()
+                    except Exception:
+                        continue
+            raise
+    # Direct class fallback
+    for cls_name in ["Model", "IMG2GPS"]:
+        if hasattr(model_module, cls_name):
+            cls = getattr(model_module, cls_name)
+            return cls()
+    raise AttributeError("Model module must expose 'get_model()' or a class named 'Model'/'IMG2GPS'.")
+
+
+def _normalize_state_dict_keys(state_dict: dict) -> dict:
+    normalized = {}
+    for k, v in state_dict.items():
+        key = k
+        if key.startswith("module."):
+            key = key[len("module.") :]
+        if key.startswith("model."):
+            key = key[len("model.") :]
+        while key.startswith("backbone.backbone."):
+            key = key.replace("backbone.backbone.", "backbone.", 1)
+        normalized[key] = v
+    return normalized
+
+
+def _load_state_into_target(target: Any, sd: dict) -> int:
+    """
+    Load only intersecting keys (and matching shapes) into the target module.
+    Returns number of parameters loaded.
+    """
+    if target is None or not hasattr(target, "state_dict") or not hasattr(target, "load_state_dict"):
+        return 0
+    target_sd = target.state_dict()
+    filtered = {}
+    for k, v in sd.items():
+        if k in target_sd and isinstance(v, torch.Tensor) and target_sd[k].shape == v.shape:
+            filtered[k] = v
+    if not filtered:
+        return 0
+    missing, unexpected = target.load_state_dict(filtered, strict=False)
+    # load_state_dict returns a NamedTuple in newer torch; handle tuple/list fallback
+    # We don't use missing/unexpected here beyond validation; count by filtered size.
+    return len(filtered)
+
+
+def _load_checkpoint(model: Any, ckpt_path: str | None) -> Any:
+    if not ckpt_path:
+        if hasattr(model, "eval"):
+            model.eval()
+        return model
+    checkpoint = torch.load(ckpt_path, map_location="cpu")
+    # Accept either {"state_dict": ...} or plain state dict
+    if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
+        sd = _normalize_state_dict_keys(checkpoint["state_dict"])
+    elif isinstance(checkpoint, dict):
+        sd = _normalize_state_dict_keys(checkpoint)
+    else:
+        raise RuntimeError("Checkpoint must be a state_dict or {'state_dict': ...} dictionary.")
+    # Try loading into inner model first (common wrapper), then wrapper
+    total_loaded = 0
+    inner = getattr(model, "model", None)
+    total_loaded += _load_state_into_target(inner, sd)
+    total_loaded += _load_state_into_target(model, sd)
+    if total_loaded == 0:
+        # Provide actionable debug info
+        sample_keys = list(sd.keys())[:10]
+        raise RuntimeError(
+            "Failed to load any parameters from checkpoint into model. "
+            f"Example checkpoint keys after normalization: {sample_keys}"
+        )
+    if hasattr(model, "eval"):
+        model.eval()
+    return model
+
+
+def _predict_in_batches(model: Any, X: List[Any], batch_size: int = 32) -> Tuple[List[Any], float, float]:
+    preds: List[Any] = []
+    total_s = 0.0
+    total_examples = 0
+    has_predict = hasattr(model, "predict") and callable(getattr(model, "predict"))
+    for i in range(0, len(X), batch_size):
+        batch = X[i : i + batch_size]
+        start = time.perf_counter()
+        if has_predict:
+            batch_preds = model.predict(batch)
+        else:
+            with torch.no_grad():
+                outputs = model(batch)  # type: ignore
+                if isinstance(outputs, torch.Tensor):
+                    batch_preds = outputs.cpu().tolist()
+                else:
+                    batch_preds = outputs
+        end = time.perf_counter()
+        infer_time = end - start
+        total_s += infer_time
+        total_examples += len(batch)
+        if isinstance(batch_preds, torch.Tensor):
+            batch_preds = batch_preds.cpu().tolist()
+        preds.extend(list(batch_preds))
+    avg_ms = (total_s / max(total_examples, 1)) * 1000.0
+    return preds, total_s, avg_ms
+
+
+def _resolve_column(columns: List[str], aliases: List[str]) -> str:
+    for name in aliases:
+        if name in columns:
+            return name
+    raise KeyError(f"Could not find any of the columns {aliases} in {columns}")
+
+
+def _load_raw_lat_lon(csv_path: str) -> List[List[float]]:
+    df = pd.read_csv(csv_path)
+    cols = df.columns.tolist()
+    lat_col = _resolve_column(cols, ["Latitude", "latitude", "lat"])
+    lon_col = _resolve_column(cols, ["Longitude", "longitude", "lon"])
+    labels: List[List[float]] = []
+    for _, row in df.iterrows():
+        labels.append([float(row[lat_col]), float(row[lon_col])])
+    return labels
+
+
+def _ensure_pairs(arr: List[Any]) -> np.ndarray:
+    pairs: List[List[float]] = []
+    for item in arr:
+        if isinstance(item, torch.Tensor):
+            item = item.detach().cpu().numpy()
+        item_np = np.asarray(item, dtype=np.float64)
+        if item_np.shape == (2,):
+            pairs.append([float(item_np[0]), float(item_np[1])])
+        elif item_np.ndim == 1 and item_np.size == 2:
+            pairs.append([float(item_np[0]), float(item_np[1])])
+        else:
+            raise ValueError(f"Expected 2-length pair, got shape {item_np.shape}")
+    return np.asarray(pairs, dtype=np.float64)
+
+
+def _haversine_m(a: Iterable[float], b: Iterable[float]) -> float:
+    lat1, lon1 = a
+    lat2, lon2 = b
+    radius = 6_371_000.0
+    phi1 = math.radians(lat1)
+    phi2 = math.radians(lat2)
+    dphi = math.radians(lat2 - lat1)
+    dlambda = math.radians(lon2 - lon1)
+    h = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlambda / 2) ** 2
+    return 2 * radius * math.asin(math.sqrt(h))
+
+
+def compute_metrics(preds: List[Any], targets_raw: List[Any]) -> dict:
+    preds_np = _ensure_pairs(preds)
+    t_np = _ensure_pairs(targets_raw)
+    n = min(len(preds_np), len(t_np))
+    preds_np = preds_np[:n]
+    t_np = t_np[:n]
+    diffs = preds_np - t_np
+    mae = float(np.abs(diffs).mean())
+    rmse = float(np.sqrt((diffs ** 2).mean()))
+    distances = [_haversine_m(p, t) for p, t in zip(preds_np, t_np)]
+    avg_distance_m = float(np.mean(distances)) if distances else float("nan")
+    return {"mae": mae, "rmse": rmse, "avg_distance_m": avg_distance_m, "num_examples": n}
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Local evaluator for Project A (img2gps).")
+    p.add_argument("--model", required=True, help="Path to student's model.py")
+    p.add_argument("--preprocess", required=True, help="Path to student's preprocess.py")
+    p.add_argument("--weights", default=None, help="Optional path to model checkpoint (e.g., model.pt)")
+    p.add_argument("--csv", required=True, help="Path to validation CSV (e.g., ./val/metadata.csv)")
+    p.add_argument("--batch-size", type=int, default=32)
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    model_mod = _dynamic_import(args.model, "student_model_a")
+    preproc_mod = _dynamic_import(args.preprocess, "student_preproc_a")
+    # Instantiate while preventing any default weight load from student's constructor
+    model = _instantiate_model(model_mod, weights_path_override="__no_weights__.pth")
+    model = _load_checkpoint(model, args.weights)
+
+    X, _ = preproc_mod.prepare_data(args.csv)
+    if isinstance(X, torch.Tensor):
+        inputs = list(X)
+    elif isinstance(X, np.ndarray):
+        inputs = list(X)
+    else:
+        inputs = list(X)
+
+    preds, total_s, avg_ms = _predict_in_batches(model, inputs, batch_size=args.batch_size)
+    targets_raw = _load_raw_lat_lon(args.csv)
+    metrics = compute_metrics(preds, targets_raw)
+
+    print(f"num_examples: {metrics['num_examples']}")
+    print(f"avg_infer_ms: {avg_ms:.3f}")
+    print(f"total_infer_s: {total_s:.3f}")
+    print(f"mae (deg): {metrics['mae']:.6f}")
+    print(f"rmse (deg): {metrics['rmse']:.6f}")
+    print(f"avg_distance_m: {metrics['avg_distance_m']:.3f}")
+
+
+if __name__ == "__main__":
+    main()
+
+

Img2GPS/reference/metadata.csv

@@ -0,0 +1,11 @@
+file_name,Latitude,Longitude
+IMG_7182.jpg,39.95240833333334,-75.19158055555556
+IMG_7181.jpg,39.95240833333334,-75.19158055555556
+IMG_7171.jpg,39.952308333333335,-75.191575
+IMG_7179.jpg,39.952400000000004,-75.191575
+IMG_7175.jpg,39.952325,-75.19158055555556
+IMG_7178.jpg,39.952400000000004,-75.19158055555556
+IMG_7163.jpg,39.9523,-75.19155
+IMG_7159.jpg,39.9523,-75.19155
+IMG_7170.jpg,39.952308333333335,-75.191575
+IMG_7165.jpg,39.9523,-75.19155

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from final!")
+
+
+if __name__ == "__main__":
+    main()

model_template.py

@@ -0,0 +1,45 @@
+import torch
+from torch import nn
+from typing import Any, Iterable, List
+
+
+class Model(nn.Module):
+    """
+    Template model for the leaderboard.
+
+    Requirements:
+    - Must be instantiable with no arguments (called by the evaluator).
+    - Must implement `predict(batch)` which receives an iterable of inputs and
+      returns a list of predictions (labels).
+    - Must implement `eval()` to place the model in evaluation mode.
+    - If you use PyTorch, submit a state_dict to be loaded via `load_state_dict`
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        # Initialize your model here
+
+    def eval(self) -> nn.Module:
+        # Optional: set your model to evaluation mode
+        return self
+
+    def predict(self, batch: Iterable[Any]) -> List[Any]:
+        """
+        Implement your inference here.
+        Inputs:
+            batch: Iterable of preprocessed inputs (as produced by your preprocess.py)
+        Returns:
+            A list of predictions with the same length as `batch`.
+        """
+        raise NotImplementedError("Implement predict(...) to return a list of labels.")
+
+
+def get_model() -> Model:
+    """
+    Factory function required by the evaluator.
+    Returns an uninitialized model instance. The evaluator may optionally load
+    weights (if provided) before calling predict(...).
+    """
+    return Model()
+
+

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "final"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "numpy",
+    "pandas",
+    "torch==2.9.1",
+    "torchvision",
+    "scikit-learn",
+    "opencv-python"
+]