Add Stage 1 training config

config.yaml

@@ -0,0 +1,45 @@
+# Stage 1 Binary Detection — accuracy + recall oriented
+# Target: 90–95% overall accuracy with strong cancer sensitivity.
+
+model_name: "efficientnet_b0"
+pretrained: true
+in_channels: 3
+num_classes: 1
+img_size: 224
+seed: 42
+
+batch_size: 16
+learning_rate: 0.0003
+weight_decay: 0.0001
+epochs: 14
+# Train classifier head only for N epochs, then fine-tune full net
+freeze_epochs: 2
+amp_enabled: true
+# Print progress every N train batches (CPU runs are long)
+log_every: 25
+
+threshold: 0.35
+# FN penalty to push recall higher on ambiguous cancer patches
+pos_weight_boost: 1.5
+# Clip exploding gradients during full fine-tune
+grad_clip: 1.0
+# Stop if val accuracy stalls (0 = disabled)
+early_stopping_patience: 5
+device: "cpu"
+
+data:
+  dataset_ids:
+    - lc25000
+    - patch_camelyon
+    - breast_histopathology_patches
+    - skin_lesion_hm10000_binary_2k
+    - camelyon17_jxie
+    - camelyon17_djghosh
+  # Increased sample count for multi-organ histology coverage
+  max_per_dataset: 4000
+  balanced_sampling: true
+  apply_stain_norm: false
+  num_workers: 0
+
+output:
+  artifacts_dir: "models/stage1/artifacts"