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scottHDS/
mtgt-finetuned-convnext-base

Name: mtgt-finetuned-convnext-base
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MTGT Fine-Tuned ConvNeXt-Base

Model Overview

This is a fine-tuned version of ConvNeXt-Base. The original ConvNeXt-Base was trained with preprocessing that resized images to 232 and then applied a center crop of 224×224.

For this fine-tuning run, inputs were reprocessed using resize -> 256, followed by center crop -> 224. The fine-tuning was not intended to improve classification accuracy, but rather to align the model’s internal weights and biases more closely with other members of the MTGT ensemble, all of which adopt the 256→224 pipeline.

More details about the overall MTGT project and ensemble methodology can be found on the GitHub repo:
MTGT repo

or in the paper: SPIE conference proceeding

Training Details

Base model: ConvNeXt-Base (ImageNet-1K)
Fine-tuning schedule: 14 epochs (final checkpoint noted as Epoch 15)
Dataset: ImageNet-1K
Preprocessing change: Resize -> 256, Center Crop -> 224

Evaluation Results

Epoch	Preprocessing	Top-1 Accuracy	Top-5 Accuracy
0 (base model)	Resize 232 -> Crop 224	83.7020 %	96.7320 %
14 (fine-tuned final)	Resize 256 -> Crop 224	83.8220 %	96.7660 %

The modest change in accuracy is expected; the primary objective was not a performance gain but improved alignment of internal representations within the MTGT ensemble.

Intended Use

Integration into the MTGT ensemble for joint inference, gradient-map consistency research, and adversarial robustness studies
Use in classification tasks where a standardized 256→224 preprocessing pipeline is required
Analysis of internal representations across architectures

Limitations

The accuracy gain is minimal and was not the primary goal
Fine-tuning was limited to matching preprocessing alignment; no architectural changes or major hyperparameter tuning were undertaken

scottHDS/mtgt-finetuned-convnext-base