Toward Errorless Training ImageNet-1k
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911113193455616 |
|---|---|
| author | Deng, Bo Heath, Levi |
| author_facet | Deng, Bo Heath, Levi |
| contents | In this paper, we describe a feedforward artificial neural network trained on the ImageNet 2012 contest dataset [7] with the new method of [5] to an accuracy rate of 98.3% with a 99.69 Top-1 rate, and an average of 285.9 labels that are perfectly classified over the 10 batch partitions of the dataset. The best performing model uses 322,430,160 parameters, with 4 decimal places precision. We conjecture that the reason our model does not achieve a 100% accuracy rate is due to a double-labeling problem, by which there are duplicate images in the dataset with different labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Errorless Training ImageNet-1k Deng, Bo Heath, Levi Computer Vision and Pattern Recognition Machine Learning 68T07 In this paper, we describe a feedforward artificial neural network trained on the ImageNet 2012 contest dataset [7] with the new method of [5] to an accuracy rate of 98.3% with a 99.69 Top-1 rate, and an average of 285.9 labels that are perfectly classified over the 10 batch partitions of the dataset. The best performing model uses 322,430,160 parameters, with 4 decimal places precision. We conjecture that the reason our model does not achieve a 100% accuracy rate is due to a double-labeling problem, by which there are duplicate images in the dataset with different labels. |
| title | Toward Errorless Training ImageNet-1k |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T07 |
| url | https://arxiv.org/abs/2508.04941 |