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diff --git a/scripts/run_cifar10.sbatch b/scripts/run_cifar10.sbatch
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+#!/bin/bash
+#SBATCH --job-name=snn_cifar10
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuA40x4
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus-per-node=1
+#SBATCH --mem=64G
+#SBATCH --time=08:00:00
+#SBATCH --output=runs/slurm_logs/%j_cifar10.out
+#SBATCH --error=runs/slurm_logs/%j_cifar10.err
+
+# ============================================================
+# CIFAR-10 Rate-Coded Experiment
+# ============================================================
+# Challenge:
+# - 3072 input dimensions (32x32x3 flattened)
+# - Requires hierarchical feature learning
+# - Deep networks essential for good accuracy
+#
+# This is the hardest benchmark - real image classification
+# ============================================================
+
+set -e
+
+PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
+cd "$PROJECT_DIR"
+
+mkdir -p runs/slurm_logs data
+
+echo "============================================================"
+echo "CIFAR-10 Rate-Coded Experiment"
+echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
+echo "Start: $(date)"
+echo "============================================================"
+nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
+echo "============================================================"
+
+# CIFAR-10 with deeper networks (needs more capacity)
+python files/experiments/benchmark_experiment.py \
+    --dataset cifar10 \
+    --depths 4 6 8 10 12 \
+    --hidden_dim 256 \
+    --epochs 50 \
+    --batch_size 64 \
+    --lr 0.0005 \
+    --T 100 \
+    --lambda_reg 0.5 \
+    --lambda_target -0.2 \
+    --data_dir ./data \
+    --out_dir runs/benchmark \
+    --device cuda
+
+echo "============================================================"
+echo "Finished: $(date)"
+echo "============================================================"