| { |
| "model_type": "simclr-resnet18", |
| "framework": { |
| "training": "Lightly", |
| "backend": "PyTorch Lightning" |
| }, |
| "architecture": { |
| "backbone": "resnet18", |
| "backbone_feature_dim": 512, |
| "projection_head": { |
| "implementation": "lightly.models.modules.heads.SimCLRProjectionHead", |
| "input_dim": 512, |
| "hidden_dim": 512, |
| "output_dim": 128 |
| } |
| }, |
| "objective": { |
| "loss": "NTXentLoss", |
| "temperature": 0.1 |
| }, |
| "optimizer": { |
| "name": "LARS", |
| "momentum": 0.9, |
| "weight_decay": 1e-06 |
| }, |
| "scheduler": { |
| "name": "CosineWarmupScheduler", |
| "interval": "step", |
| "warmup_epochs": 10 |
| }, |
| "input": { |
| "size": [ |
| 224, |
| 224 |
| ], |
| "channels": 3 |
| }, |
| "paper": { |
| "title": "Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field", |
| "authors": [ |
| "Dylan M. Diaz", |
| "Margaret M. Henderson" |
| ], |
| "year": 2026, |
| "doi": "10.32470/0416gfsq", |
| "arxiv": "2607.19316" |
| }, |
| "notes": [ |
| "This configuration describes the reference-model training setup used in the associated study.", |
| "The exact installed Lightly and PyTorch Lightning package versions were not stored in this configuration." |
| ], |
| "repository": "DM-Diaz/SimCLR-ResNet18-ImageNet100", |
| "checkpoint": "checkpoint_120-resnet18-simclr-imagenet100.ckpt", |
| "training": { |
| "dataset": { |
| "huggingface_id": "clane9/imagenet-100", |
| "num_classes": 100, |
| "local_format": "ImageFolder-style train/val directories" |
| }, |
| "epochs": 120, |
| "batch_size": 64, |
| "base_learning_rate": 0.3, |
| "lr_scaling_rule": "base_learning_rate * batch_size / 256", |
| "initial_learning_rate": 0.075, |
| "distributed": false, |
| "mixed_precision": "16-mixed", |
| "knn_k": 20, |
| "knn_temperature": 0.1 |
| } |
| } |
