Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fixing the train-test resolution discrepancy
Authors: Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herve Jegou
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet50 trained on 128x128 images, and 79.8% with one trained at 224x224. A ResNeXt-101 32x48d pre-trained with weak supervision on 940 million 224x224 images and further optimized with our technique for test resolution 320x320 achieves 86.4% top-1 accuracy (top-5: 98.0%). |
| Researcher Affiliation | Industry | Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herv´e J´egou Facebook AI Research |
| Pseudocode | No | The paper describes the methods in text and does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | An open-source implementation of our method is available at https://github.com/facebookresearch/FixRes. |
| Open Datasets | Yes | We experiment on the ImageNet-2012 benchmark [29] |
| Dataset Splits | Yes | We experiment on the ImageNet-2012 benchmark [29], reporting validation performance as top-1 accuracy. To assess the significance of our results, we compute the standard deviation of the top-1 accuracy: we classify the validation images, split the set into 10 folds and measure the accuracy on 9 of them, leaving one out in turn. |
| Hardware Specification | Yes | We ran our training experiments on machines with 8 Tesla V100 GPUs and 80 CPU cores. |
| Software Dependencies | No | The paper mentions 'PyTorch library' and 'PyTorch hub repository' but does not specify version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | We train ResNet-50 with SGD with a learning rate of 0.1 B/256, where B is the batch size... The learning rate is divided by 10 every 30 epochs. With a Repeated Augmentation of 3, an epoch processes 5005 * 512/B batches... In the initial training, we use B = 512, 120 epochs and the default PyTorch data augmentation: horizontal flip, random resized crop (as in section 3) and color jittering. To finetune, the initial learning rate is 0.008 same decay, B = 512, 60 epochs. |