You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
pytorch-image-models/results
Ross Wightman c53ec33ae0
Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly.
4 years ago
..
README.md Fix incorrect dataset name, add new datasets to results delta script 4 years ago
generate_csv_results.py Fix incorrect dataset name, add new datasets to results delta script 4 years ago
imagenet_a_indices.txt Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly. 4 years ago
imagenet_a_synsets.txt Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly. 4 years ago
imagenet_r_indices.txt Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly. 4 years ago
imagenet_r_synsets.txt Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly. 4 years ago
imagenet_real_labels.json Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly. 4 years ago
imagenet_synsets.txt Add synset/label indices for results generation. Add 'valid labels' to validation script to support imagenet-a/r label subsets properly. 4 years ago
results-imagenet-a.csv set precision=2 in param_count 5 years ago
results-imagenet.csv Update results csv files 5 years ago
results-imagenetv2-matched-frequency.csv set precision=2 in param_count 5 years ago
results-sketch.csv set precision=2 in param_count 5 years ago

README.md

Validation Results

This folder contains validation results for the models in this collection having pretrained weights. Since the focus for this repository is currently ImageNet-1k classification, all of the results are based on datasets compatible with ImageNet-1k classes.

Datasets

There are currently results for the ImageNet validation set and 5 additional test/label sets.

ImageNet Validation - results-imagenet.csv

The standard 50,000 image ImageNet-1k validation set. Model selection during training utilizes this validation set, so it is not a true test set. Question: Does anyone have the official ImageNet-1k test set classification labels now that challenges are done?

ImageNetV2 Matched Frequency - results-imagenetv2-matched-frequency.csv

An ImageNet test set of 10,000 images sampled from new images roughly 10 years after the original. Care was taken to replicate the original ImageNet curation/sampling process.

ImageNet-Sketch - results-sketch.csv

50,000 non photographic (or photos of such) images (sketches, doodles, mostly monochromatic) covering all 1000 ImageNet classes.

ImageNet-Adversarial - results-imagenet-a.csv

A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occuring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1.

ImageNet-Rendition - results-imagenet-r.csv

Renditions of 200 ImageNet classes resulting in 30,000 images for testing robustness.

ImageNet-"Real Labels" - results-imagenet-real.csv

TODO