add standalone train

README.md

@@ -15,21 +15,25 @@ The dashboard simulates a live feed of camera-trap detections across five locati
 ### 1. Clone the repository
 
 ```bash
-git clone <repo-url>
-cd assignment-HAI
+git clone https://git.barrys.cloud/barry/Wildlife-Detection.git
+cd Wildlife-Detection
 ```
 
 ### 2. Obtain the model weights
 
-The trained model weights are not included in the repository due to their size. Place the file in the project root:
+The trained model weights are not included in the repository due to their size. You can regenerate them by running the training script:
 
-| File | Size | Description |
-|---|---|---|
-| `efficientnet_v2_wild_forest_animals.pt` | ~78 MB | Fine-tuned EfficientNet V2-S weights |
+```bash
+uv run python train.py
+```
 
-The model was fine-tuned in `final.ipynb`.
+This downloads the dataset from Roboflow (if not present), fine-tunes EfficientNet V2-S for 3 epochs, and saves `efficientnet_v2_wild_forest_animals.pt`. Optional flags:
 
-The **dataset** (`wild-forest-animals-and-person-1/`) is downloaded automatically from [Roboflow](https://roboflow.com/) on first launch if not already present on disk.
+```bash
+uv run python train.py --epochs 5 --lr 0.0005 --batch-size 16
+```
+
+The **dataset** (`wild-forest-animals-and-person-1/`) is downloaded automatically from [Roboflow](https://roboflow.com/) on first run if not already present on disk.
 
 ### 3. Install dependencies
 
@@ -50,6 +54,7 @@ The server starts at **http://localhost:5000**.
 ```
 assignment-HAI/
 ├── dashboard.py          # Flask application (main entry point)
+├── train.py              # Standalone training script
 ├── final.ipynb           # Training, evaluation, and XAI notebook
 ├── map.webp              # Park map background image
 ├── pyproject.toml        # Project metadata and dependencies

train.py

@@ -0,0 +1,169 @@
+"""
+Fine-tune EfficientNet V2-S on the Wild Forest Animals dataset.
+
+Produces: efficientnet_v2_wild_forest_animals.pt
+
+Usage:  uv run python train.py
+        uv run python train.py --epochs 5 --lr 0.0005 --batch-size 16
+"""
+
+import argparse
+import csv
+import random
+from pathlib import Path
+
+import numpy as np
+import torch
+from torch import nn
+from torch.amp import GradScaler, autocast
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
+from PIL import Image
+
+SEED = 42
+CLASS_NAMES = ["bear", "deer", "fox", "hare", "moose", "person", "wolf"]
+DATASET_DIR = Path("wild-forest-animals-and-person-1")
+WEIGHTS_PATH = Path("efficientnet_v2_wild_forest_animals.pt")
+
+
+def seed_everything(seed: int = SEED):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def ensure_dataset():
+    if DATASET_DIR.exists():
+        return
+    print("Dataset not found -- downloading from Roboflow ...")
+    from roboflow import Roboflow
+    rf = Roboflow(api_key="VCZWezdoCHQz7juipBdt")
+    project = rf.workspace("forestanimals").project("wild-forest-animals-and-person")
+    project.version(1).download("multiclass")
+    if not DATASET_DIR.exists():
+        raise RuntimeError(f"Download finished but {DATASET_DIR} not found.")
+
+
+class WildForestAnimalsDataset(Dataset):
+    def __init__(self, root: Path, split: str, transform=None):
+        self.root, self.transform = Path(root), transform
+        split_dir = self.root / split
+        csv_path = split_dir / "_classes.csv"
+        if not csv_path.exists():
+            raise FileNotFoundError(f"CSV not found for split '{split}' at {csv_path}")
+        self.samples: list[tuple[Path, int]] = []
+        with csv_path.open(newline="") as f:
+            for row in csv.DictReader(f):
+                oh = [int(row[n]) for n in CLASS_NAMES]
+                try:
+                    label = oh.index(1)
+                except ValueError:
+                    continue
+                self.samples.append((split_dir / row["filename"], label))
+        if not self.samples:
+            raise RuntimeError(f"No samples loaded for split '{split}'")
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx):
+        path, label = self.samples[idx]
+        img = Image.open(path).convert("RGB")
+        return self.transform(img) if self.transform else img, label
+
+
+def build_transforms(train: bool):
+    common = [
+        transforms.ToTensor(),
+        transforms.Resize((224, 224), antialias=True),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ]
+    if train:
+        return transforms.Compose([transforms.RandomHorizontalFlip(p=0.5)] + common)
+    return transforms.Compose(common)
+
+
+def create_model(num_classes: int) -> nn.Module:
+    model = efficientnet_v2_s(weights=EfficientNet_V2_S_Weights.IMAGENET1K_V1)
+    model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)
+    return model
+
+
+def train_one_epoch(model, loader, criterion, optimizer, scaler, device):
+    model.train()
+    total_loss = 0.0
+    for images, labels in loader:
+        images, labels = images.to(device), labels.to(device)
+        optimizer.zero_grad()
+        with autocast(device.type, enabled=(device.type == "cuda")):
+            loss = criterion(model(images), labels)
+        scaler.scale(loss).backward()
+        scaler.step(optimizer)
+        scaler.update()
+        total_loss += loss.item() * images.size(0)
+    return total_loss / len(loader.dataset)
+
+
+def evaluate(model, loader, criterion, device):
+    model.eval()
+    total_loss, correct, total = 0.0, 0, 0
+    with torch.no_grad():
+        for images, labels in loader:
+            images, labels = images.to(device), labels.to(device)
+            with autocast(device.type, enabled=(device.type == "cuda")):
+                outputs = model(images)
+                loss = criterion(outputs, labels)
+            total_loss += loss.item() * images.size(0)
+            correct += (outputs.argmax(1) == labels).sum().item()
+            total += labels.size(0)
+    return total_loss / len(loader.dataset), 100.0 * correct / total
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Fine-tune EfficientNet V2-S")
+    parser.add_argument("--epochs", type=int, default=3)
+    parser.add_argument("--batch-size", type=int, default=32)
+    parser.add_argument("--lr", type=float, default=1e-3)
+    parser.add_argument("--output", type=Path, default=WEIGHTS_PATH)
+    args = parser.parse_args()
+
+    seed_everything()
+    ensure_dataset()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Device: {device}")
+
+    train_ds = WildForestAnimalsDataset(DATASET_DIR, "train", build_transforms(train=True))
+    val_ds = WildForestAnimalsDataset(DATASET_DIR, "valid", build_transforms(train=False))
+    test_ds = WildForestAnimalsDataset(DATASET_DIR, "test", build_transforms(train=False))
+    print(f"Splits: {len(train_ds)} train, {len(val_ds)} val, {len(test_ds)} test")
+
+    gen = torch.Generator().manual_seed(SEED)
+    train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, generator=gen)
+    val_loader = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)
+    test_loader = DataLoader(test_ds, batch_size=args.batch_size, shuffle=False)
+
+    model = create_model(len(CLASS_NAMES)).to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
+    scaler = GradScaler(device.type, enabled=(device.type == "cuda"))
+
+    for epoch in range(1, args.epochs + 1):
+        train_loss = train_one_epoch(model, train_loader, criterion, optimizer, scaler, device)
+        val_loss, val_acc = evaluate(model, val_loader, criterion, device)
+        print(f"Epoch {epoch}/{args.epochs} -- "
+              f"train loss: {train_loss:.4f}, val loss: {val_loss:.4f}, val acc: {val_acc:.2f}%")
+
+    test_loss, test_acc = evaluate(model, test_loader, criterion, device)
+    print(f"Test loss: {test_loss:.4f}, test accuracy: {test_acc:.2f}%")
+
+    torch.save(model.state_dict(), args.output)
+    print(f"Saved weights to {args.output.resolve()}")
+
+
+if __name__ == "__main__":
+    main()