CUDA reproduction of the CIFAR-10 architecture matrix, trained on an NVIDIA
A40 with batch size 1024, seed 42, GPU-resident tensor shards, GPU-side
augmentation, and a 200-epoch target (R-CNN-B uses 30 epochs). JSON files are
the authoritative per-run metadata; checkpoints are provided alongside them.
These results are intentionally separate from the MPS reference leaderboard:
the accelerator, batch size, and input pipeline differ, so a CUDA/MPS gap is
not evidence of a hardware-only effect.
Attention analysis
The CUDA DenseCNN checkpoint was inspected with the same Grad-CAM procedure as
the MPS analysis. The target layer is DenseCNN.features.5; the channel panels
rank the top 10 channels by absolute gradient-weighted mean activation.
Test index
True label
DenseCNN prediction
CNN-C + BN prediction
Interpretation
0
cat
cat (97.2%)
cat (100.0%)
Both models focus on a correctly classified object.
1
ship
ship (95.9%)
ship (100.0%)
Both correctly identify the dominant ship evidence.
3957
truck
airplane (89.8%)
airplane (85.2%)
Shared false positive: background/shape evidence dominates class evidence.
5808
dog
cat (95.0%)
horse (60.4%)
Both fail on a fine-grained animal; the low-resolution crop is ambiguous.
The current artifact was rendered with CPU visualization because the publishing
environment has neither CUDA nor MPS available; the weights and predictions are
from the CUDA-trained checkpoints. This affects rendering speed, not the model
weights or selected class.
