imagenet-x 0.0.4

Imagenet factors of variation annotations

ImagenetX : Understanding model mistakes with factors of variation annotations

Code to load annotations, evaluate models and reproduce paper plots. See [paper], [website], [colab]

Installation

If you just want to load the annotations :

pip install imagenet-x

Other installations:

• Install from repo clone : pip install -e .
• Reproduce plots from the paper and use dataset loader: pip install 'imagenet-x[all]'

Usage

To load the annotations

from imagenet_x import load_annotations

annotations = load_annotations()

This will output the following table

file_name	class	multiple_objects	background	color	brighter	darker	style	larger	smaller	object_blocking	person_blocking	partial_view	pattern	pose	shape	subcategory	texture	justification	one_word	metaclass
ILSVRC2012_val_00004487.JPEG	762	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	close up of a pan fried sea bass.	sea bass close up	structure
ILSVRC2012_val_00003963.JPEG	292	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	sepia image of tiger	digitally altered	other
ILSVRC2012_val_00041992.JPEG	718	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	the bridge is brown	rare view	device
ILSVRC2012_val_00028056.JPEG	635	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	the magnetic compass is on the bronze container	wood shape	device

See this notebook for some sample images and annotations

One can also directly download the raw annotation files stored in the annotations folder. There are 4 json line files imagenet_x_[train|val]_[multi|top]_factor.jsonl that have entries such as the following:

{"file_name":"ILSVRC2012_val_00004487.JPEG","class":762,"multiple_objects":0,"background":1,"color":0,"brighter":0,"darker":0,"style":0,"larger":1,"smaller":0,"object_blocking":0,"person_blocking":0,"partial_view":0,"pattern":0,"pose":1,"shape":0,"subcategory":0,"texture":0,"justification":"close up of a pan fried sea bass. ","one_word":"sea bass close up"}

To generate plots for a new model

Generate the predictions of your model on the Imagenet Validation set in a csv file with 3 columns

file_name	predicted_class	predicted_probability
ILSVRC2012_val_00000293.JPEG	0	0.634764
ILSVRC2012_val_00002138.JPEG	391	0.360206
ILSVRC2012_val_00003014.JPEG	0	0.951837
ILSVRC2012_val_00006697.JPEG	0	0.999731
ILSVRC2012_val_00007197.JPEG	0	0.998473

Then store the list of model CSVs in a directory "path/to/model/predictions"

from imagenet_x import get_factor_accuracies, error_ratio

factor_accs = get_factor_accuracies("path/to/model/predictions")
error_ratios = error_ratio(factor_accs)

This gives the following table

Error ratios

model	pose	background	pattern	color	smaller	shape	partial_view	subcategory	texture	larger	darker	object_blocking	person_blocking	style	brighter	multiple_objects
DINO	0.726197	1.06799	0.927478	1.1779	1.54369	1.64228	1.12906	1.95486	2.15032	1.24805	1.46777	1.93051	2.03486	1.70361	0.924938	1.4244
ResNet50	0.694739	1.1417	0.771442	1.18883	1.49743	1.74423	1.13236	2.10548	2.39386	1.31592	1.71502	1.92327	2.17128	1.92798	1.16639	1.97389
SimCLR	0.774029	1.0867	0.911171	1.11955	1.54176	1.47304	1.01247	1.61814	2.0584	1.0121	1.12238	1.75552	2.03412	1.17686	0.879497	1.1907
ViT	0.74067	1.09097	0.862456	1.15164	1.64401	1.53992	0.917235	2.01538	2.04465	1.29087	1.83403	1.98596	1.93631	1.60108	0.782348

We also provide some plotting utilities

from imagenet_x import plots

plots.model_comparison(factor_accs.reset_index(), fname="/path/to/save/fig.pdf|png")

Finally, we also provide a ImagenetX pytorch dataset that loads the imagenet samples and appends the factors of validation as a one hot encoded vector of 16 elements.

from imagenet_x.evaluate import ImageNetX, get_vanilla_transform

# Declare dataset
imagenet_val_path = '/path/to/imagenet'
transforms = get_vanilla_transform()
dataset = ImageNetX(imagenet_val_path, transform=transforms)

See this notebook to run the previous commands and for a sample evaluation loop on a resnet-18.

Paper results

To reproduce plots for models in the paper

You need python>=3.8 for plots and evaluation to work

python -m imagenet_x plots [--use-tex]

Generate aggregate results from model predictions

python -m imagenet_x aggregate --model-dirs path/to/model/predictions 

License

License file in root of directory.