Inside the RTX 5090 Vision Engine: A Deep Dive into an SOTA Training Framework

if torch.cuda.is_available():
    print(f"Detected: {torch.cuda.get_device_name(0)}")
    torch.backends.cudnn.benchmark = True
    torch.backends.cudnn.allow_tf32 = True  # Enable TF32 for CuDNN
    torch.backends.cuda.matmul.allow_tf32 = True # Enable TF32 for MatMul

• Automatic Mixed Precision (FP16): The training learner is instantly converted to use 16-bit floating-point precision (.to_fp16()). This cuts memory usage in half, allowing for a larger batch size, and dramatically speeds up training on Tensor Cores.

• Hyper-Optimized Data Pipeline: The CFG (Configuration) class sets up a data loading pipeline that's designed to saturate the GPU, leaving no performance on the table. With 12 CPU workers, pin_memory=True, and a prefetch_factor=4, the pipeline ensures that data is always ready for the GPU, eliminating "data starvation" bottlenecks.

2. State-of-the-Art Architecture: ConvNeXt Large

The engine defaults to one of the most powerful vision models available: convnext_large_in22k. This is a modern, Vision Transformer-inspired convolutional model pretrained on the massive 22K ImageNet dataset. Training this beast at a high resolution of 512x512 is no small feat, which is why the optimizations above are so critical.

3. A Robust, Intelligent Training Strategy

This is the most impressive part of the code. It uses a series of advanced techniques to ensure the final model is not only accurate but also robust.

• Stratified Group K-Fold Cross-Validation: The code doesn't just train one model; it trains five (as defined by N_FOLDS = 5). But it uses a highly advanced splitting strategy: StratifiedGroupKFold.
  • Stratified: Ensures that each "fold" (or validation set) has the same percentage of image classes as the whole dataset, preventing class imbalance.
  • Group: This is the genius part. The data is split based on a groups=df['site'] column. This means images from the same "site" (e.g., same patient, same location) are always kept together in either the training or validation set, but never split between them. This prevents the model from "cheating" by learning to recognize the site instead of the actual features, leading to a much more generalized and robust model.
• Adaptive Learning Rate & One-Cycle Scheduling: The script doesn't guess the learning rate. It uses FastAI's learn.lr_find() to discover the optimal rate before training. It then uses the "One-Cycle" policy (learn.fit_one_cycle()), a proven technique that starts with a low learning rate, warms up to the max rate, and then cools down, leading to faster convergence and better accuracy.
• Intelligent Ensemble: After all 5 models are trained, the script doesn't just average their predictions. It creates a performance-weighted ensemble. Models that performed better on their validation set are given a higher "vote" in the final prediction, squeezing out the last few drops of performance.

# Ensemble strategy - weighted by validation performance
print(f"\nExecuting ensemble strategy...")
val_accs = [s['val_acc'] for s in fold_scores]
weights = torch.softmax(torch.tensor(val_accs) * 5, dim=0)
print(f"Fold weights: {[f'{w:.3f}' for w in weights.tolist()]}")

# Weighted ensemble predictions
ensemble_preds = sum(w * pred for w, pred in zip(weights, all_preds))

project_root/
├── train_features.csv      # Training image metadata (id, filepath, site)
├── train_labels.csv        # Multi-class labels (one-hot encoded)
├── test_features.csv       # Test set metadata
├── images/                 # Image directory
│   ├── train/
│   └── test/
└── rtx5090_training.py     # Main training script (or notebook)