Ross Wightman
d10b071a28
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3 years ago | |
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| .. | ||
| README.md | ||
| generate_csv_results.py | 4 years ago | |
| imagenet21k_goog_synsets.txt | ||
| imagenet_a_indices.txt | ||
| imagenet_a_synsets.txt | ||
| imagenet_r_indices.txt | ||
| imagenet_r_synsets.txt | ||
| imagenet_real_labels.json | ||
| imagenet_synsets.txt | ||
| results-imagenet-a-clean.csv | 3 years ago | |
| results-imagenet-a.csv | 3 years ago | |
| results-imagenet-r-clean.csv | 3 years ago | |
| results-imagenet-r.csv | 3 years ago | |
| results-imagenet-real.csv | 3 years ago | |
| results-imagenet.csv | 3 years ago | |
| results-imagenetv2-matched-frequency.csv | 3 years ago | |
| results-sketch.csv | 3 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.
The test set results include rank and top-1/top-5 differences from clean validation. For the "Real Labels", ImageNetV2, and Sketch test sets, the differences were calculated against the full 1000 class ImageNet-1k validation set. For both the Adversarial and Rendition sets, the differences were calculated against 'clean' runs on the ImageNet-1k validation set with the same 200 classes used in each test set respectively.
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?
- Source: http://image-net.org/challenges/LSVRC/2012/index
- Paper: "ImageNet Large Scale Visual Recognition Challenge" - https://arxiv.org/abs/1409.0575
ImageNet-"Real Labels" - results-imagenet-real.csv
The usual ImageNet-1k validation set with a fresh new set of labels intended to improve on mistakes in the original annotation process.
- Source: https://github.com/google-research/reassessed-imagenet
- Paper: "Are we done with ImageNet?" - https://arxiv.org/abs/2006.07159
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.
- Source: https://github.com/modestyachts/ImageNetV2
- Paper: "Do ImageNet Classifiers Generalize to ImageNet?" - https://arxiv.org/abs/1902.10811
ImageNet-Sketch - results-sketch.csv
50,000 non photographic (or photos of such) images (sketches, doodles, mostly monochromatic) covering all 1000 ImageNet classes.
- Source: https://github.com/HaohanWang/ImageNet-Sketch
- Paper: "Learning Robust Global Representations by Penalizing Local Predictive Power" - https://arxiv.org/abs/1905.13549
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.
For clean validation with same 200 classes, see results-imagenet-a-clean.csv
- Source: https://github.com/hendrycks/natural-adv-examples
- Paper: "Natural Adversarial Examples" - https://arxiv.org/abs/1907.07174
ImageNet-Rendition - results-imagenet-r.csv
Renditions of 200 ImageNet classes resulting in 30,000 images for testing robustness.
For clean validation with same 200 classes, see results-imagenet-r-clean.csv
- Source: https://github.com/hendrycks/imagenet-r
- Paper: "The Many Faces of Robustness" - https://arxiv.org/abs/2006.16241
TODO
- Explore adding a reduced version of ImageNet-C (Corruptions) and ImageNet-P (Perturbations) from https://github.com/hendrycks/robustness. The originals are huge and image size specific.