DM-Diaz/SimCLR-ResNet18-ImageNet100

SimCLR ResNet-18 — ImageNet-100

This repository contains the ImageNet-100 SimCLR ResNet-18 checkpoint trained as a non-egocentric reference model for:

Diaz, D. M., & Henderson, M. M. (2026). Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field. Proceedings of the 9th Conference on Cognitive Computational Neuroscience.

DOI: 10.32470/0416gfsq
arXiv: 2607.19316
Contributed Talk: CCN 2026 presentation on YouTube

The model was pretrained using SimCLR with a ResNet-18 backbone and served as one of the non-egocentric reference models in the associated study. It was evaluated alongside models pretrained on ImageNet-1K and STL-10 as comparison models for representations learned from naturalistic egocentric visual experience.

Training was implemented using the Lightly self-supervised learning framework. The training images were obtained from the clane9/imagenet-100 dataset on Hugging Face.

Code, preprocessing, analysis, and other related material associated with the paper are hosted on Github: DM-Diaz/eccentricity-constrained-simclr

Model Architecture

The model uses a standard ResNet-18 encoder with the classification head removed and a SimCLR projection head attached during self-supervised pretraining.

Component	Configuration
Backbone	ResNet-18
Backbone representation	512 dimensions
Projection head	Lightly SimCLRProjectionHead
Projection dimensions	512 → 512 → 128
Projection output	128 dimensions
SSL objective	NT-Xent
Temperature	0.1

The released checkpoint contains both the ResNet-18 backbone and the SimCLR projection head. For downstream applications, the 512-dimensional backbone representation can be extracted independently of the projection head.

Training Configuration

Parameter	Value
Dataset	ImageNet-100
Dataset source	clane9/imagenet-100
Number of classes	100
Epochs	120
Batch size	64
Input resolution	224 × 224
Optimizer	LARS
Initial learning rate	0.075
Momentum	0.9
Weight decay	1e-6
LR schedule	Cosine warmup
Warmup	10 epochs
Precision	16-bit mixed precision
Distributed training	No

The learning rate was linearly scaled from a base learning rate of 0.3 according to batch size:

0.3 × (64 / 256) = 0.075

Training Data

Training data were obtained from the Hugging Face dataset:

clane9/imagenet-100

The dataset was downloaded locally and organized into training and validation directories. The training split was used for self-supervised representation learning.

The dataset itself is not redistributed through this repository and remains subject to its original access conditions and terms.

Checkpoint

File: checkpoint_120-resnet18-simclr-imagenet100.ckpt

The released file is a full PyTorch Lightning checkpoint, rather than a backbone-only state dictionary.

Checkpoint inspection confirmed:

Property	Value
PyTorch Lightning version recorded	2.6.1
Stored epoch	119
Training epochs completed	120
Global step	237,480
State-dict entries	132
Backbone output	512 dimensions
Projection output	128 dimensions
Strict architecture loading	Successful

The stored epoch is zero-indexed, so epoch = 119 corresponds to the completion of epoch 120.

The checkpoint also contains optimizer, learning-rate scheduler, training-loop, callback, and mixed-precision state in addition to the model parameters.

Loading the Checkpoint

The checkpoint can be loaded by reconstructing the ResNet-18 backbone and SimCLR projection head used during training.

import torch
import torch.nn as nn
import torchvision
from lightly.models.modules import heads


class SimCLRResNet18(nn.Module):
    def __init__(self):
        super().__init__()

        resnet = torchvision.models.resnet18(weights=None)
        feature_dim = resnet.fc.in_features  # 512

        # Remove the classification head
        self.backbone = nn.Sequential(
            *list(resnet.children())[:-1]
        )

        # SimCLR projection head: 512 -> 512 -> 128
        self.projection_head = heads.SimCLRProjectionHead(
            feature_dim,
            feature_dim,
            128,
        )

    def forward(self, x):
        features = self.backbone(x).flatten(start_dim=1)
        projections = self.projection_head(features)
        return projections


checkpoint = torch.load(
    "checkpoint_120-resnet18-simclr-imagenet100.ckpt",
    map_location="cpu",
    weights_only=False,
)

model = SimCLRResNet18()
model.load_state_dict(checkpoint["state_dict"], strict=True)
model.eval()

Extracting Backbone Features

For most downstream applications, the 512-dimensional ResNet-18 representation can be extracted without using the SimCLR projection head:

with torch.no_grad():
    features = model.backbone(images).flatten(start_dim=1)

print(features.shape)
# [batch_size, 512]

The 128-dimensional SimCLR projection can instead be obtained with:

with torch.no_grad():
    projections = model(images)

print(projections.shape)
# [batch_size, 128]

Input tensors should have shape [batch_size, 3, 224, 224].

Comparative Evaluation Results

The table below reproduces the summary metrics reported in the associated paper across all VEDB-trained conditions and reference models. Rows corresponding to this repository's ImageNet-100 checkpoint are bolded.

Task	Condition	Val Loss	Top-1 (%)	Top-5 (%)	Best Macro-F1 (%)
SimCLR	Baseline	0.4331	87.60	—	—
SimCLR	Fovea-Gaze	0.3749	90.43	—	—
SimCLR	Periph-NF	0.4548	90.04	—	—
SimCLR	Periph	0.4545	89.26	—	—
In-Domain	Baseline	0.9811	—	—	42.17
In-Domain	Fovea-Gaze	1.2031	—	—	43.64
In-Domain	Periph-NF	1.3090	—	—	30.93
In-Domain	Periph	1.0623	—	—	36.56
In-Domain	STL-10	1.6666	—	—	25.41
In-Domain	ImageNet-100	1.2342	—	—	41.23
In-Domain	ImageNet-1K	0.9713	—	—	43.33
VGGFace2	Baseline	7.8101	5.21	11.73	3.26
VGGFace2	Fovea-Gaze	7.9104	4.58	10.76	2.70
VGGFace2	Periph-NF	8.0232	3.39	8.17	1.90
VGGFace2	Periph	8.1681	2.54	6.39	1.35
VGGFace2	STL-10	6.9973	9.55	18.96	7.43
VGGFace2	ImageNet-100	6.7985	10.77	21.07	8.71
VGGFace2	ImageNet-1K	6.7964	10.74	21.08	8.77
Places365	Baseline	3.9690	25.63	51.90	23.16
Places365	Fovea-Gaze	4.2347	21.86	46.21	19.14
Places365	Periph-NF	4.2621	20.51	44.58	17.86
Places365	Periph	4.2671	20.26	44.10	17.65
Places365	STL-10	3.8281	26.57	53.47	24.82
Places365	ImageNet-100	3.9207	24.99	51.21	23.32
Places365	ImageNet-1K	3.6264	30.17	58.46	28.36

Note: SimCLR Top-1 is computed from the self-supervised pretraining evaluation and is not directly comparable to downstream supervised classification accuracy. For downstream tasks, the pretrained ResNet-18 backbone was frozen and only a linear classifier was trained; the backbone weights were not fine-tuned. Classifier checkpoints were selected by best validation Macro-F1. In-domain Top-1 accuracy is omitted because label imbalance across VEDB frame categories can make accuracy misleading; Macro-F1 is reported as the primary class-balanced metric. STL-10, ImageNet-100, and ImageNet-1K are treated as out-of-domain reference models because they were not pretrained on VEDB.

Intended Use

This checkpoint is provided for research and downstream applications involving self-supervised visual representations, including:

• reproducing the reference-model analyses reported in Diaz and Henderson (2026),
• extracting ResNet-18 representations for comparison with the VEDB-pretrained models,
• reproducing the associated NSD voxelwise encoding analyses,
• linear-probe or fine-tuned image classification,
• transfer learning to other visual recognition tasks, and
• representation-learning and visual-neuroscience research.

The released checkpoint contains a self-supervised ResNet-18 encoder and SimCLR projection head rather than a trained classification head. For image classification, users can attach and train an appropriate classifier on the learned backbone representations or fine-tune the encoder for the target task.

Related Models

This model was used as a non-egocentric reference model in the study associated with the Eccentricity-Constrained SimCLR Models (VEDB) collection.

• VEDB SimCLR ResNet-18 — Baseline
• VEDB SimCLR ResNet-18 — Fovea-Gaze
• VEDB SimCLR ResNet-18 — Periph
• VEDB SimCLR ResNet-18 — Periph-NF
• VEDB NSD ResNet-18 — Encoding Models
• SimCLR ResNet-18 — ImageNet-1K
• SimCLR ResNet-18 — ImageNet-100
• SimCLR ResNet-18 — STL-10 (external pretrained reference model; checkpoint provided by Spijkervet/SimCLR and not redistributed by this project)

Computational Resources

Model training and computational analyses for this study were conducted primarily using Carnegie Mellon University Neuroscience Institute's MiND computing cluster.

Citation

If you use this checkpoint or representations derived from it in academic work, please cite the associated study:

@inproceedings{diaz2026eccentricity,
  author    = {Diaz, Dylan M. and Henderson, Margaret M.},
  title     = {Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field},
  booktitle = {Proceedings of the 9th Conference on Cognitive Computational Neuroscience},
  address   = {New York, NY, USA},
  year      = {2026},
  doi       = {10.32470/0416gfsq}
}

Proceedings: Diaz & Henderson (2026)
Preprint: arXiv:2607.19316

License

The released checkpoint and repository materials are provided under the Apache License 2.0.

The ImageNet-100 training dataset and third-party software used to produce the model remain subject to their respective licenses, access requirements, and terms of use.