SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Figure 6: Example errors that the CLIP [24] model makes on GeoDE images (the ground truth label on the left, CLIP prediction at the bottom). There are some systematic errors, e.g., classifying "house" as a "religious building", particularly on images from Asia. (product labels in figure are blurred).
In seeking to understand the accuracy variation across geographic regions, we compute the minimum and maximum per-region accuracy for each object (Fig. 8 right). We also compute the confidence interval for the expected distribution of per-region accuracy for each object based on the object's overall accuracy.4 We find that 31 of 40 objects have a minimum and/or maximum region accuracy that falls outside the corresponding  $95\%$  confidence interval, suggesting that these objects (including for example "house" and "dustbin") exhibit significant geographic variation (at least with respect to the visual distribution learned by CLIP).5 We further note some differences on individual classes. "Fence" is  $88\%$  accurate for images from Europe, but only  $60\%$  and  $59\%$  for images from Africa and SE Asia respectively. Similarly, "stove" is  $95\%$  accurate in the Americas but only  $67\%$  in East Asia. Visualizing classes using TSNE plots of the features (Fig. 7), we see that these objects are region specific, e.g., "religious buildings" from East and SE Asia uniquely include buildings like monasteries and temples; similarly, single- and two-burner "stoves" are primarily from Africa and SE Asia.
7 Impact of training with GeoDE
Finally, we attempt to answer how training with GeoDE can improve the performance of models trained on web-scraped data. Concretely, we investigate training a model on jointly GeoDE and subsets of ImageNet [8], and demonstrate the combination improves results across geographic regions.
7.1 Training a model with GeoDE
We would like to understand how training a model with data from GeoDE affects object recognition. We train a linear model using pre-trained features on a dataset comprised entirely of ImageNet images and a dataset comprised of both ImageNet and GeoDE images with the same number of images. The feature extractor is a ResNet50 [16] model trained on PASS [2] using SwAV [5].<sup>6</sup>
Implementation details. We split GeoDE into train (4,970 images per region), validation (between 1,657 and 2,188 images per region), and test (between 1,657 and 2,189 images per region). We use
<sup>4</sup>We draw 500 random partitions into 6 regions, and compute the resulting per-region accuracies.
5Considering the problem of multiple hypothesis testing, we can apply the Bonferonni correction for 40 objects, with  $\alpha = 0.05 / 40 = 0.001$  , i.e,  $99.9\%$  confidence intervals. Still, 21 of 40 objects fall outside their intervals, confirming that GeoDE as a whole does exhibit statistically significant geographic variation.
<sup>6</sup>We also try a model trained from scratch and with finetuning, with similar conclusions (supp. mat.).
Figure 7: We show the TSNE plots of objects which have large regional disparities in accuracy in the CLIP trained model, with images embedded. We see differences based on region, e.g., "religious buildings" contains a cluster of monasteries and temples, mostly from East and Southeast Asia.
Model	WAsia	Africa	EAsia	SEAsia	Americas	Europe
ImageNet	69.4	62.7	63.3	67.3	68.6	69.9
CLIP	84.0	78.7	79.9	81.9	84.4	85.8
Figure 8: (left) Accuracies (in %) on GeoDE of a model trained on a subset of ImageNet [8] (details in Sec. 6) and of CLIP [24]. The models perform best on images from Europe, and worst on images from Africa. (right) We compute the maximum and minimum accuracy of CLIP [24] for each object across the 6 regions in GeoDE(sorted by overall accuracy). 31 of 40 objects have at least one region whose accuracy falls outside the  $95\%$  CI, suggesting significant differences across regions.
the validation dataset to select training hyperparameters. The training set for our ImageNet only model is the same 38,353 image training set constructed in Sec. 6. To construct the training set of our ImageNet and all regions in GeoDE model, we add in the training sets for all 6 regions in GeoDE while removing the same number of images per class from ImageNet. This procedure gives a training set of 29,820 GeoDE images and 8,533 ImageNet [8] images. The models are trained using an SGD optimizer  $(\mathrm{lr} = 0.1$ , momentum  $= 0.9)$  for 500 epochs with cross entropy loss. Models were trained using a single GPU (RTX1080 or 2080) and took less than 1 hour. Results are reported on the test set.
Results. We first report results on the GeoDE test set, and notice a significant improvement in accuracy across all regions, as a result of training with both GeoDE and ImageNet (Tab. 5). However, this improvement could come from the ImageNet + GeoDE dataset matching the domain of the GeoDE evaluation set and may not generalize to other datasets. Thus, we also test these models on a different dataset: the DollarStreet dataset [25]. This dataset has been used before as an evaluation benchmark [7], to understand if current object recognition models can perform well on objects from a diverse set of regions. Tab. 5 lists the accuracy for the 4 different regions in DollarStreet, along with the per class accuracies for the object categories that overlap between GeoDE and DollarStreet. We see an increase in performance across most categories, suggesting that GeoDE is more geo-diverse than ImageNet and that there is an advantage to using geo-diverse data in the training set.
7.2 Cost-vs-Diversity tradeoffs
The main drawback of GeoDE is the cost of this dataset: images collected in this way cost more than the standard pipeline of web-scraping and crowd-sourcing annotations. Thus, it is important to identify which classes and regions contribute most to the overall model. To investigate this, we start with the filtered ImageNet dataset described above, vary the amount of GeoDE data from a particular region, and analyze the change in overall regional performance.
Implementation Details. We start with the 38,353 filtered ImageNet images and add a region of GeoDE's data back into the dataset and remove the same number of ImageNet images to keep the number of images and class balance the same. Other training details remain the same as in Sec. 7.1.
Evaluation. As we are evaluating on the GeoDE test set, there are two possible sources of performance gain: (1) the model is able to take advantage of the additional regional information from the GeoDE data; and (2) the GeoDE images were collected using the same collection method as the test set and from Sec. 5, we saw that there is a difference in the feature space that can be attributed
Table 5: We compare the performance of a model trained on ImageNet [8] versus one that is trained on both ImageNet and GeoDE. We report results on the test set of GeoDE as well as the DollarStreet [25] images. We see an improvement across all regions for both test datasets. We also report the per-class accuracies for the DollarStreet dataset and see improvement across most objects as well.
Tested on GeoDE
West Asia Africa East Asia SE Asia Americas Europe Overall	Africa America Asia Europe bicycle chair clean.equipment cooking pot dustbin hand soap house light fixture light switch medicine plate of food stove toy Overall
ImageNet +GeoDE	69 63 63 67 69 70 67 88 87 86 87 89 90 88	45 64 58 75 92 86 19 49 76 49 88 36 77 80 84 89 50 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 61 68 65 91 63 79 78 96 85 58 69
100	Africa (▲)	East Asia (▼)	W. Asia (+)	Europe (+) Americas (+) SE Asia (+)	Classes with largest % inc. in AP
Africa waste cont., spices, dustbin, clean. equip.
E. Asia relig. blg., spices, dustbin, waste cont.
W. Asia dustbin, hand soap, clean. equip., spices, Americas dustbin, spices, clean. equip., medicine
SE. Asia waste cont., spices, medicine, clean. equip.
Figure 9: (left) We measure the relative improvement in AP per object when GeoDE images from that region are included in training. Each vertical line represents an object (sorted by region of max. improvement). Africa and East Asia see the largest improvement for the most classes. (right) We highlight the classes with the largest increases in the AP when adding in training images from GeoDE.
to the collection method itself (deliberately taking photos rather than web-scraping). In order to distinguish between these two sources, we measure the accuracy on both the region in the train set and accuracy on the images from Europe<sup>7</sup>. We also measure the increase in AP for specific objects to better understand which objects benefit most from GeoDE data.
Results. We find that the performance within each specific region and in Europe increase with the additional GeoDE data. The relative increase in performance for the specific region is larger than the increase for Europe, showing the value of data for each region, moreover, the improvements do not saturate, suggesting that more data could lead to further gains. Full results are presented in the supp. mat. We examine the classes that have the largest increase in average precision (AP) as the regional GeoDE images are added to the dataset in Fig. 9 (left). We also present the object classes that see the most improvement in Fig. 9 (right). In general, we see that specific objects such as "spices", "waste container" and "cleaning equipment" benefit most from regional GeoDE data.
We introduced a new dataset GeoDE which uses crowd-sourcing for image collection, a significant departure from the popular computer vision dataset collection paradigm of web-scraping for image collection. Through this collection method, we ensured that this dataset does not contain personally identifiable information, we own the rights to the images, the image creators were compensated for their work, and were able to control for geographic diversity and object distribution in the dataset. We show that GeoDE is a useful dataset for highlighting shortcomings in common models (e.g., CLIP) and can improve performance when added to the training dataset. Also, GeoDE shows that crowd-sourcing is a viable image collection method for creating diverse and responsible datasets.
Acknowledgements. This material is based upon work partially supported by the National Science Foundation under Grant No. 2145198. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We also acknowledge support from Meta AI and the Princeton SEAS Howard B. Wentz, Jr. Junior Faculty Award to OR. We thank Dhruv Mahajan for his valuable insights during the project development phase. We also thank Jihoon Chung, Nicole Meister, Angelina Wang and the Princeton Visual AI Lab for their helpful comments and feedback during the writing process.
We use Europe as this region had the best performance when using a model trained on just ImageNet.
[25] William A Gaviria Rojas, Sudnya Diamos, Keertan Ranjan Kini, David Kanter, Vijay Janapa Reddi, and Cody Coleman. The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world. In NeurIPS Datasets&Benchmarks Track, 2022.
[26] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. IJCV, 2015.
[27] Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D Sculley. No classification without representation: Assessing geodiversity issues in open data sets for the developing world. NeurIPS Workshop on Machine Learning for the Developing World, 2017.
[28] Gunnar A. Sigurdsson, Gül Varol, X. Wang, Ali Farhadi, Ivan Laptev, and Abhinav Kumar Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. ECCV, 2016.
[29] Pierre Stock and Moustapha Cisse. Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases. In ECCV, 2018.
[30] Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in multimedia research. 2016.
[31] Antonio Torralba and Alexei A Efros. Unbiased look at dataset bias. In CVPR, 2011.
[32] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. In arXiv preprint arXiv:2302.13971, 2023.
[33] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. JMLR, 2008.
[34] Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, and Olga Russakovsky. REVISE: A tool for measuring and mitigating bias in visual datasets. *IJCV*, 2022.
[35] Angelina Wang, Arvind Narayanan, and Olga Russakovsky. REVISE: A tool for measuring and mitigating bias in visual datasets. In ECCV, 2020.
[36] Kaiyu Yang, Clint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky. Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy. FAT*, 2020.
[37] Kaiyu Yang, Clint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky. Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy. In FAT, 2020.
[38] Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, and Olga Russakovsky. A study of face obfuscation in imagenet. CoRR, abs/2103.06191, 2021.
[39] Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, and Olga Russakovsky. A study of face obfuscation in imagenet. In ICML, 2022.
[40] Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In EMNLP, 2017.