New backbone

.claude/scheduled_tasks.lock

@@ -0,0 +1 @@
+{"sessionId":"ce21bc15-a2ef-47a3-ac8b-8dad11b440dd","pid":9390,"procStart":"45841","acquiredAt":1778247797938}

.claude/settings.local.json

@@ -0,0 +1,24 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(python3 -c ' *)",
+      "Bash(python3 -)",
+      "Bash(python3 -m src.utils.detector test_image.jpg)",
+      "Bash(python3 -c \"import sys; print\\(sys.executable\\)\")",
+      "Bash(pip show *)",
+      "Bash(.venv/bin/python -m src.utils.detector test_image.jpg)",
+      "Bash(.venv/bin/python -)",
+      "Bash(cat)",
+      "Bash(.venv/bin/python /tmp/eval_model.py)",
+      "Bash(.venv/bin/python /tmp/eval_final.py)",
+      "Bash(.venv/bin/python identify.py test_image.jpg)",
+      "Bash(.venv/bin/python -m src.data.generate_aug_test_set)",
+      "Bash(.venv/bin/python -c ' *)",
+      "Bash(.venv/bin/python -m src.models.train --epochs 5)",
+      "Bash(.venv/bin/python -m src.models.evaluate)",
+      "Bash(.venv/bin/python -m src.models.evaluate --backbone resnet34 --model_path models/best_resnet34_model.pth)",
+      "Bash(.venv/bin/python identify.py test_image2.jpg --backbone resnet34 --model_path models/best_resnet34_model.pth)",
+      "Bash(.venv/bin/python src/models/regression_model.py)"
+    ]
+  }
+}

CLAUDE.md

@@ -13,6 +13,9 @@ All commands must be run from the project root using the local venv:
 # Train the model
 .venv/bin/python -m src.models.train --epochs 50 --batch_size 64 --lr 1e-4
 
+# Evaluate a trained model on val and aug_test sets
+.venv/bin/python -m src.models.evaluate [--backbone resnet18|resnet34] [--model_path <path>]
+
 # Run inference only (no registry lookup)
 .venv/bin/python src/models/inference.py <image_path>
 

identify.py

@@ -8,7 +8,11 @@ from src.registry.database import SpindaRegistry
 from src.data.high_fidelity_generator import generate_high_fidelity_spinda
 from src.utils.detector import SpindaDetector # Import the detector
 
-def identify_spinda(image_path: str):
+def identify_spinda(
+    image_path: str,
+    model_path: str = "models/best_spinda_model.pth",
+    backbone: str = "resnet18",
+):
     if not os.path.exists(image_path):
         print(f"Error: File {image_path} not found.")
         return
@@ -33,7 +37,7 @@ def identify_spinda(image_path: str):
 
     # 2. Inference (Model Prediction) using the cropped image
     try:
-        inf = SpindaInference(model_path="models/best_spinda_model.pth")
+        inf = SpindaInference(model_path=model_path, backbone=backbone)
         coords, fingerprint = inf.predict(temp_cropped_path)
     except Exception as e:
         print(f"Error during inference: {e}")
@@ -71,7 +75,10 @@ def identify_spinda(image_path: str):
     print("\nNote: Accuracy depends on model training progress.")
 
 if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Usage: python identify.py <image_path>")
-    else:
-        identify_spinda(sys.argv[1])
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("image_path")
+    parser.add_argument("--backbone",   type=str, default="resnet18", choices=["resnet18", "resnet34", "convnext_tiny"])
+    parser.add_argument("--model_path", type=str, default="models/best_spinda_model.pth")
+    args = parser.parse_args()
+    identify_spinda(args.image_path, model_path=args.model_path, backbone=args.backbone)

src/models/evaluate.py

@@ -30,7 +30,7 @@ def evaluate(model: torch.nn.Module, device: torch.device, name: str, ds: Spinda
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--model_path", type=str, default="models/best_spinda_model.pth")
-    parser.add_argument("--backbone",   type=str, default="resnet18", choices=["resnet18", "resnet34"])
+    parser.add_argument("--backbone",   type=str, default="resnet18", choices=["resnet18", "resnet34", "convnext_tiny"])
     args = parser.parse_args()
 
     if not os.path.exists(args.model_path):

src/models/inference.py

@@ -11,9 +11,13 @@ from src.models.regression_model import SpindaRegressionModel
 class SpindaInference:
     """Loads the trained model and predicts spot coordinates from an image crop."""
 
-    def __init__(self, model_path: str = "models/best_spinda_model.pth"):
+    def __init__(
+        self,
+        model_path: str = "models/best_spinda_model.pth",
+        backbone: str = "resnet18",
+    ):
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.model = SpindaRegressionModel(pretrained=False)
+        self.model = SpindaRegressionModel(pretrained=False, backbone=backbone)
         self.model.load_state_dict(
             torch.load(model_path, map_location=self.device)
         )

src/models/regression_model.py

@@ -1,16 +1,18 @@
 import torch
 import torch.nn as nn
 from torchvision import models
-from torchvision.models import ResNet18_Weights, ResNet34_Weights
+from torchvision.models import ResNet18_Weights, ResNet34_Weights, ConvNeXt_Tiny_Weights
 
+# (factory, default_weights, feature_dim)
 _BACKBONES = {
-    "resnet18": (models.resnet18, ResNet18_Weights.DEFAULT),
-    "resnet34": (models.resnet34, ResNet34_Weights.DEFAULT),
+    "resnet18":      (models.resnet18,      ResNet18_Weights.DEFAULT,      512),
+    "resnet34":      (models.resnet34,      ResNet34_Weights.DEFAULT,      512),
+    "convnext_tiny": (models.convnext_tiny, ConvNeXt_Tiny_Weights.DEFAULT, 768),
 }
 
 
 class SpindaRegressionModel(nn.Module):
-    """ResNet backbone with 8 independent 16-class coordinate heads.
+    """CNN backbone with 8 independent 16-class coordinate heads.
 
     Each of the 8 output coordinates (4 spots × x, y) is treated as a
     16-class classification problem over the [0, 15] nibble grid.
@@ -25,23 +27,29 @@ class SpindaRegressionModel(nn.Module):
         super().__init__()
         if backbone not in _BACKBONES:
             raise ValueError(f"backbone must be one of {list(_BACKBONES)}; got {backbone!r}")
-        factory, default_weights = _BACKBONES[backbone]
+        factory, default_weights, feat_dim = _BACKBONES[backbone]
         weights = default_weights if pretrained else None
         net = factory(weights=weights)
-        # Strip the final FC; keep the feature extractor + average pool.
-        self.features = nn.Sequential(*list(net.children())[:-1])
-        # 8 coordinates × 16 classes each (512-dim output for both resnet18/34)
-        self.classifier = nn.Linear(512, 8 * 16)
+
+        if backbone in ("resnet18", "resnet34"):
+            # Strip the final FC; flatten the (B, 512, 1, 1) avgpool output.
+            self.features = nn.Sequential(*list(net.children())[:-1], nn.Flatten())
+        elif backbone == "convnext_tiny":
+            # Keep features + avgpool + LayerNorm (classifier[0]); drop the final Linear.
+            self.features = nn.Sequential(
+                net.features, net.avgpool, net.classifier[0], nn.Flatten()
+            )
+
+        self.classifier = nn.Linear(feat_dim, 8 * 16)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.features(x)       # (B, 512, 1, 1)
-        x = x.flatten(1)           # (B, 512)
+        x = self.features(x)       # (B, feat_dim)
         x = self.classifier(x)     # (B, 128)
         return x.view(-1, 8, 16)   # (B, 8, 16)
 
 
 if __name__ == "__main__":
-    for name in ("resnet18", "resnet34"):
+    for name in ("resnet18", "resnet34", "convnext_tiny"):
         model = SpindaRegressionModel(pretrained=False, backbone=name)
         out = model(torch.randn(2, 3, 128, 128))
         print(f"{name}: output {out.shape}, predictions {out.argmax(dim=2)}")

src/models/train.py

@@ -198,7 +198,7 @@ if __name__ == "__main__":
     parser.add_argument("--model_path",  type=str,   default="models/best_spinda_model.pth")
     parser.add_argument("--num_workers", type=int,   default=4,      help="DataLoader worker count (0 = main process only)")
     parser.add_argument("--epoch_size",  type=int,   default=200000, help="Virtual dataset size per epoch")
-    parser.add_argument("--backbone",    type=str,   default="resnet18", choices=["resnet18", "resnet34"])
+    parser.add_argument("--backbone",    type=str,   default="resnet18", choices=["resnet18", "resnet34", "convnext_tiny"])
     parser.add_argument("--save_path",   type=str,   default="",     help="Override checkpoint save path")
     args = parser.parse_args()