Upload config.json

config.json

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+{
+  "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
+  }
+}