SlowGuess/NeurIPS_2023_OCR huggingface dataset:
(a) Human accuracy per presentation time on COCO.
(b) Difficulty score for COCO images normalized by number of collected responses per image. A difficulty score of 1.0 here corresponds to 42 in figure 1.
Figure 9: MVT experiment results on a subset of the COCO dataset.
Informed consent to participate in this study
This HIT is part of a MIT scientific research project. Your decision to complete this HIT is voluntary. There is no way for us to identify you.
The only information we will have, in addition to your responses, is the time at which you completed the survey and generic non-identifiable about your computer such as its resolution and browser version number.
The results of the research may be presented at scientific meetings or published in scientific journals.
The responses collected in this experiment will be released to the scientific community and the public.
Clicking on the 'SUBMIT' button on the bottom of this page indicates that you are at least 18 years of age and agree to complete this HIT voluntarily.
** This experiment contains flashing videos. If you have photosensitive epilepsy or any sensitivity to flashing lights you are not eligible and cannot participate in this study.
□ I do not have photosensitive epilepsy or any sensitivity to flashing lights
Figure 10: Informed consent page shown to participants before beginning the experiment.
6.9.2 Image classes
We selected a set of 41 classes such that no image contained more than one of the classes.
6.9.3 Experimental procedure
We conducted the MVT experiment as described in the text, asking participants to perform a 1-of-41 forced-choice single-object recognition task.
Below, we present preliminary results computed on 340 of the images. The final manuscript will include all 732 images. The results in fig. 9 are striking in their similarity to those presented for ImageNet and ObjectNet in the main text. The accuracy of human workers at each of the presentation times while performing the COCO classification task is almost the same as that of the ImageNet experiments. Given that both ImageNet and COCO originate from the same online pool of images, this is to be expected.
Similarly, the difficulty scores of COCO images (the counterpart of fig. 1) is skewed toward easy images, perhaps more so than either ImageNet or ObjectNet. These results indicate that our conclusions about the difficulty distributions of individual object recognition tasks in vision datasets generalize. Of course, COCO has images where multiple object classes are present, which involves visual search in addition to recognizing individual image instances, but, for the quantity that we measure here, how hard are objects themselves to recognize, it COCO and ImageNet are essentially the same.
Task Overview
Please read the following:
You are participating in an experiment in which we are studying people's ability to recognize what object is in an image given varying amounts of image viewing time.
Included below is an example of what we will ask you to view. Look at the center of the cross. When the video plays, the cross will briefly change to an image of an object and then it will change to an image of a random pattern. After you see the image we will ask you what object you saw in the image. You will only be able to play the video once, so be ready and focused before pressing play. The image may disappear too quickly for you to immediately be able to name the object that you saw, that's okay. Look at the multiple choice list of possible objects, think about what you saw, and take your best guess. There is no time limit for your response, but you have to respond before you can move on to the next image.
Play example video
The multiple choice list of objects will be the same for all the images. Take a minute to read all these object names. If you are unfamiliar with a category or find it confusing, do a quick search on the web to see some example images. YOU DO NOT NEED TO MEMORIZE THESE CATEGORIES, they will be shown to you again after you watch each video.
butcher's knife / cleaver
doorma
power drill
envelo
clothe
Jampshade
match (i.e. matchstick)
neckla
paintbrush
pill bottle
pillow.
plunger
portable heater
prnnter
rules (i.e. measuring stick)
safety pin
1. 实验原理
spatula
racket (i.e. tennis racket)
waste container (i.e. trash bin)
televis
On the response page, you will see a table with these 50 options displayed. The order of the options in the table will be randomized but all 50 options will always be visible. Please take the time to find the correct answer if you know what it is. If you do not know what you saw, take your best guess. We will know if you are not trying to answer correctly and your submission will be rejected. Please take this task seriously.
In this experiment you will look at some images and tell us what objects you saw. We expect this to take about 60 minutes. You can take breaks at any time. If you feel like you are tired or having trouble focusing please take a break and resume the experiment later. Please try to take your breaks after submitting a response for an image and before clicking play on the next image. Responses are saved as you submit them so as long as you return to the same link, you will be able to pick up where you left off.
We would also like you to view our experiment in a quiet and well-lit room if possible.
Thank you for participating
Figure 11: Instructions given to participants before beginning the experiment.
6.10 Finetuned Models
Here we list details regarding training/finetuning procedures for the model results reported in the paper.
6.10.1 Model training procedure
Pretrained models weights were instantiated using publicly available model checkpoints, either using torchvision or found on the model's source repository. The models—with the exception of CLIP—were then finetuned using subsets of the ImageNet training and validation sets containing only the 50 classes we chose to use in the psychophysics experiments. The models were finetuned for 90 epochs with an SGD optimizer and initial learning rate of 0.1 with momentum value of 0.9 and weight decay coefficient of 0.0001. The learning rate decayed by a factor of 2 every 9 epochs. Training, finetuning, and inference were performed on a cluster of 8 Nvidia Titan RTX graphics cards.
6.10.2 Model Performance
We evaluate our finetuned models on the same cropped images used in our psychophysics experiments. See table 4 for model accuracy reports on the image difficulty reported in the paper and table 2 and table 3 for model performance on the full ImageNet and ObjectNet subsets of the experiment images.
Figure 12: Images of the experiment calibration steps. The credit card task was used to measure the pixel-to-inches ratio of the subject's screen. The blind spot task provided an estimate of the subjects distance from their screen.
6.11 Metric calculation procedure
In this section, we go through the details in computing c-score, prediction depth, and adversarial robustness for our experiment images.
6.11.1 C-score
C-score [7] identifies individual image difficulty by characterizing the expected accuracy or a held-out image given training sets of varying size sampled from the data distribution. In particular, c-score is the frequency of classifying an example correctly when it is omitted from the training set. However, computing c-score for each image by brute force is computationally infeasible since we must train a separate model for each image. Instead, we computed the learning speed proxy as recommended by the authors. Learning speed measures the epoch at which an image is correctly classified by a model. Intuitively, a training example that is consistent with the training set should be learned quickly because the gradient step for all consistent examples should be similar. The authors found high Spearman rank correlation between c-score and cumulative learning speed based proxies.
We trained a ResNet-50 [43] from scratch on ImageNet1k [16] for 90 epochs with an SGD optimizer and initial learning rate of 0.1 with momentum value of 0.9 and weight decay coefficient of 0.0001. The learning rate decayed by a factor of 2 every 9 epochs and the batch size was 256. The standard ImageNet transforms were applied to all images, and the network was initialized randomly. We then evaluated our experiment images at each epoch and used the average of correct predictions as an estimated c-score for each image. fig. 13 shows the average c-scores for ImageNet and ObjectNet experiment images split by whether the ResNet-50 correctly predicted the image. C-score serves as an efficient predictor for human recognition difficulty only for images classified by the model in both ImageNet and ObjectNet. C-scores for images misclassified by the model do not reveal information about the human recognition difficulty and remain consistently low across all MVT difficulty subsets.
Table 2: Model accuracy on ImageNet per MVT subset. Models are named to include architecture, training objective, and training dataset where appropriate. ResNet-X-Y% indicates a ResNet with depth X and trained on a random Y% subset of the ImageNet-1k dataset. Model names ending in 21k were pretrained on ImageNet-21k. All other models with the exception of SWSL and CLIP models were pre-trained on the full ImageNet-1k dataset.
MVT subset	<= 17	<= 50	<= 100	<= 150	<= 250	<= 10000
ResNet-18	94.4	91.8	81.1	77.2	61.3	49.0
ResNet-18-20%	81.9	77.2	63.7	58.2	39.8	34.9
ResNet-18-40%	84.4	85.0	67.8	63.5	50.5	46.2
ResNet-18-60%	87.5	88.3	72.6	70.4	53.8	46.2
ResNet-18-80%	88.1	86.5	76.7	68.8	54.8	48.6
ResNet-50	94.4	95.5	85.2	84.7	79.6	64.7
ResNet-50-20%	86.9	82.9	72.2	65.1	48.4	41.0
ResNet-50-40%	93.8	89.1	74.1	74.1	60.2	48.2
ResNet-50-60%	91.2	90.2	78.5	79.4	60.2	59.4
ResNet-50-80%	90.6	91.5	83.0	80.4	68.8	59.0
ResNet-101	95.0	95.2	90.0	87.8	79.6	71.5
ResNet-101-20%	86.2	85.4	70.0	66.1	50.5	48.2
ResNet-101-40%	90.6	90.0	78.1	77.8	63.4	50.6
ResNet-101-60%	93.1	89.9	84.4	77.2	62.4	59.0
ResNet-101-80%	92.5	94.1	83.0	82.5	64.5	61.8
ResNet-152	93.8	96.4	93.7	86.8	78.5	72.7
ResNet-152-20%	86.9	84.4	73.0	71.4	52.7	44.2
ResNet-152-40%	93.1	88.4	76.3	76.7	62.4	52.6
ResNet-152-60%	93.1	90.4	82.2	78.8	66.7	59.4
ResNet-152-80%	90.6	91.9	86.3	85.7	76.3	60.6
CORNet-S	93.8	92.6	81.9	78.8	58.1	52.2
VOneNet-Resnet50	93.8	94.4	84.4	82.5	67.7	56.6
VOneNet-CORNet-S	91.9	92.3	82.2	77.2	63.4	53.4
VGG-19	91.9	90.2	80.7	79.4	62.4	55.4
Noisy Student (EfficientNet-L2)	95.0	93.3	87.8	86.8	68.8	65.5
DenseNet-121	94.4	93.3	83.3	80.4	72.0	58.6
MSDNet Classifier 0	78.8	76.0	60.0	54.0	40.9	33.7
MSDNet Classifier 1	89.4	86.2	73.7	67.7	53.8	45.8
MSDNet Classifier 2	91.9	89.9	77.8	72.5	62.4	51.4
MSDNet Classifier 3	91.9	90.4	79.3	69.8	63.4	51.4
MSDNet Classifier 4	94.4	91.3	79.3	78.8	62.4	52.2
SimCLR ResNet50	88.1	86.3	73.7	69.8	60.2	54.6
SimCLR ResNet101	93.1	89.6	79.3	83.6	72.0	57.8
SimCLR ResNet152	93.8	92.1	83.7	81.0	72.0	63.9
CLIP-ViT-B/32	95.6	90.8	79.3	74.6	67.7	48.6
CLIP-ViT-B/16	97.5	94.7	83.3	81.0	80.6	52.2
CLIP-ViT-L/14	98.1	97.1	92.6	91.0	86.0	72.3
CLIP-ViT-L/14@336px	98.1	96.9	91.5	92.6	89.2	73.9
CLIP-ResNet-50	92.5	84.4	69.6	67.7	55.9	34.5
CLIP-ResNet-101	94.4	88.1	71.9	68.8	67.7	41.0
CLIP-ResNet-50x4	93.8	88.8	75.6	72.5	71.0	41.8
CLIP-ResNet-50x16	94.4	91.5	81.1	77.2	68.8	43.8
CLIP-ResNet-50x64	98.8	95.9	87.0	85.2	77.4	59.0
EfficientNet-S	91.2	92.5	84.1	78.3	64.5	62.2
EfficientNet-M	90.6	91.5	80.7	73.5	69.9	61.4
EfficientNet-L	95.0	93.0	87.4	83.6	75.3	64.3
EfficientNet-S-21	96.9	95.6	92.6	87.8	81.7	71.5
EfficientNet-M-21	97.5	97.0	93.7	88.9	84.9	72.3
EfficientNet-L-21	98.1	96.7	93.0	90.5	86.0	73.5
ViT-T/16	67.5	72.3	57.8	54.5	38.7	34.5
ViT-S/16	95.0	94.5	82.6	85.2	68.8	58.6
ViT-B/16	96.2	95.6	85.2	87.3	67.7	63.5
ViT-L/16	98.8	97.5	97.4	96.8	84.9	80.7
MoCo-V3	92.5	92.6	85.6	84.7	75.3	64.7
SWSL-ResNext101-32x16d	96.9	98.1	97.4	95.8	87.1	85.5
SWSL-ResNet50	96.2	97.7	96.7	95.2	84.9	77.9
MAE-ViT-B/16	94.4	95.6	88.9	89.9	77.4	75.1
6.11.2 Prediction depth
Prediction depth [8] represents the number of hidden layers after which the network's final prediction is already determined. The authors showed that prediction depth is larger for examples that visually appear to be more difficult and is consistent between architectures and random seeds.
We trained a linear decoder at the end of each block of a ResNet-50 on the 50 experiment classes using the ImageNet training and validation set. We used the same ResNet-50 used to calculate c-scores to ensure consistency of our results. There are 16 convolutional layers in a ResNet-50; and each linear decoder follows a convolution layer and consists of a pooling layer, flatten layer, and fully-connected layer. We use the same hyperparameters as section 6.11.1 and only updated the weights of the linear decoder.
A prediction is defined to be made at depth  $L = l$  if the linear classifier after layer  $L = l - 1$  is different from the network's final prediction, but the classification of the linear decoder after every
Table 3: Model accuracy on ObjectNet per MVT subset.
MVT subset	<=17	<=50	<=100	<=150	<=250	<=10000
ResNet-18	76.1	65.1	49.1	41.2	25.3	20.6
ResNet-18-20%	46.2	44.8	29.6	27.5	11.5	12.5
ResNet-18-40%	58.1	53.8	43.0	35.2	20.7	16.2
ResNet-18-60%	67.5	60.0	43.3	37.9	19.5	17.4
ResNet-18-80%	66.7	63.4	45.7	37.4	26.4	17.1
ResNet-50	80.3	79.7	62.9	53.3	44.8	27.0
ResNet-50-20%	58.1	51.1	36.4	35.2	24.1	15.7
ResNet-50-40%	70.1	61.7	45.4	42.9	29.9	20.3
ResNet-50-60%	70.9	70.1	54.0	45.1	33.3	19.7
ResNet-50-80%	76.9	70.4	53.3	49.5	31.0	22.0
ResNet-101	86.3	81.0	68.7	54.9	47.1	29.6
ResNet-101-20%	50.4	53.3	41.2	33.0	21.8	13.6
ResNet-101-40%	66.7	61.5	47.4	35.7	27.6	18.3
ResNet-101-60%	76.9	70.3	52.9	46.7	36.8	22.6
ResNet-101-80%	75.2	75.2	62.9	46.2	34.5	25.2
ResNet-152	85.5	83.8	68.7	60.4	46.0	30.7
ResNet-152-20%	58.1	53.7	39.2	34.6	19.5	13.6
ResNet-152-40%	66.7	65.1	49.5	42.9	25.3	19.1
ResNet-152-60%	72.6	68.4	57.0	41.8	35.6	22.6
ResNet-152-80%	74.4	73.5	55.7	50.0	35.6	24.6
CORNet-S	75.2	71.5	53.6	45.1	36.8	20.3
VOneNet-Resnet50	77.8	75.7	59.1	45.6	35.6	20.9
VOneNet-CORNet-S	72.6	67.0	51.9	42.9	31.0	16.5
VGG-19	76.1	66.3	50.9	46.7	34.5	18.3
Noisy Student (EfficientNet-L2)	76.9	68.7	54.3	45.1	26.4	20.9
DenseNet-121	77.8	74.9	57.0	49.5	33.3	22.3
MSDNet Classifier 0	45.3	39.0	31.3	28.0	17.2	10.7
MSDNet Classifier 1	62.4	56.4	41.9	36.3	23.0	15.9
MSDNet Classifier 2	70.9	64.3	52.9	44.0	28.7	22.3
MSDNet Classifier 3	76.9	68.7	51.5	46.7	29.9	21.7
MSDNet Classifier 4	73.5	70.9	51.5	47.3	37.9	22.3
SimCLR ResNet50	60.7	61.0	49.8	45.6	27.6	17.4
SimCLR ResNet101	75.2	70.3	59.5	55.5	32.2	25.8
SimCLR ResNet152	78.6	70.3	57.0	55.5	35.6	28.7
CLIP-ViT-B/32	88.9	80.5	61.2	61.0	43.7	33.6
CLIP-ViT-B/16	92.3	88.2	78.0	69.8	50.6	48.4
CLIP-ViT-L/14	97.4	93.8	88.7	81.3	78.2	70.1
CLIP-ViT-L/14@336px	96.6	94.2	91.1	85.2	80.5	70.1
CLIP-ResNet-50	78.6	73.5	58.1	54.9	34.5	27.5
CLIP-ResNet-101	86.3	76.9	63.6	58.2	41.4	32.5
CLIP-ResNet-50x4	83.8	79.3	67.4	64.8	43.7	36.8
CLIP-ResNet-50x16	92.3	85.0	74.2	69.2	52.9	48.7
CLIP-ResNet-50x64	89.7	90.3	84.2	81.9	69.0	55.9
EfficientNet-S	68.4	66.2	52.9	40.1	23.0	20.6
EfficientNet-M	71.8	66.3	47.8	39.0	20.7	18.8
EfficientNet-L	75.2	71.5	54.0	45.1	31.0	23.2
EfficientNet-S-21	94.0	83.9	72.9	64.8	41.4	30.4
EfficientNet-M-21	88.9	87.5	71.1	67.0	46.0	34.2
EfficientNet-L-21	89.7	86.2	70.1	68.7	50.6	36.5
ViT-T/16	43.6	43.4	29.6	25.3	10.3	10.7
ViT-S/16	83.8	76.8	57.7	52.2	31.0	23.5
ViT-B/16	86.3	80.2	65.6	58.2	37.9	26.4
ViT-L/16	96.6	92.5	84.9	81.3	58.6	44.9
MoCo-V3	82.9	73.3	59.1	54.9	34.5	24.6
SWSL-ResNext101-32x16d	94.0	94.5	89.3	85.7	62.1	57.4
SWSL-ResNet50	94.0	89.4	83.8	72.0	52.9	47.2
MAE-ViT-B/16	84.6	82.7	69.4	63.2	37.9	32.5
layer  $L \geq l$  are equal to the final classification of the network. Images classified by all decoders are said to be predicted at layer 0. Note that prediction depth is independent of whether the final prediction is correct or not. It measures the layer at which an image's prediction converges.
Figure 13 shows the average c-scores for ImageNet and ObjectNet experiment images split by whether the ResNet-50 correctly predicted the image. Like c-score, prediction depth serves as an efficient predictor for human recognition difficulty only for images classified by the model in both ImageNet and ObjectNet.
6.11.3 Adversarial robustness
We measured an image's distance to the decision boundary of a network using fast gradient sign method (FGSM) [9]. FGSM creates an modified example that maximizes the loss using the gradients of loss with respect to the input image:
$$
m o d _ {x} = x + \epsilon \cdot \operatorname {s i g n} (\nabla_ {x} J (\theta , x, y))
$$
where  $adv_{x}$  is the modified image,  $x$  is the original image,  $y$  is the original input label,  $\epsilon$  is a multiplier adjusted accordingly to control the size of modification step,  $\theta$  is the model parameters, and  $J$  is the
Figure 13: Top: left and right are average c-score over subsets for experiment ImageNet and ObjectNet images respectively. Orange shows the images that are correctly predicted by the ResNet-50 while blue shows the images that are incorrectly predicted. Bottom: prediction depth plots shown in the same way as top.
loss function. Note that gradients are taken with respect to the input image, and model parameters remain constant.
For an image classified by a model, we define its distance to the closest decision boundary of the model as the minimum  $\epsilon$  needed for the model to misclassify the modified image. On the other hand, for an image misclassified by a model, we define its distance to the closest decision boundary of the model as the minimum  $\epsilon$  needed for the model to classify the modified image.
We used the same ResNet-50 used to calculate c-scores to ensure consistency of our results. We finetuned the ResNet-50 on the 50 experiment classes using the ImageNet training and validation set. We used the same hyperparameters as section 6.11.1 and only updated the weights of the final pooling, flatten, and fully-connected layer. We used this finetuned ResNet-50 as the backbone for adversarial perturbation and correction.
While perturbing each classified image, we searched for the smallest  $\epsilon$ , from 0 to 0.02 incrementing by  $1.25\mathrm{e}-5$  and from 0.02 to 2.5 incrementing by 0.005, that would result in a misclassification. We only applied only one gradient step when perturbing. While correcting each misclassified image, we searched for the smallest  $\epsilon$ , from 0 to 0.001 incrementing by  $1.25\mathrm{e}-6$  and from 0.001 to 0.05 incrementing by  $1.25\mathrm{e}-5$ . We applied two gradient steps when correcting because correction requires finer and more steps.
Note that the search range depends on the backbone model and the dataset. One must choose them through manual trial-and-errors to yield interesting and significant results. Recall that after removing images that were incorrectly annotated, incorrectly cropped, etc section 3, we reduced to 4,771 images from the original 5,000. Of these, 3,296 and 1,475 images were classified and misclassified by the finetuned ResNet-50 respectively. We were not able to find an  $\epsilon$  for every image while perturbing and correcting in the corresponding search range. We omitted these images in our analysis. We were able to successfully perturb 2,815 out of 3,296 classified images and correct 1,114 out of 1,475 misclassified images.
We hypothesized that difficult images that are classified and misclassified would be closer and further from the decision boundary respectively. fig. 8 confirms the prior hypothesis. We could not confirm
Figure 14: Average  $\epsilon$  magnitude required to correct misclassified images back to their correct class per subset
Figure 15: Distribution of difficulty score for each MVT subsets in ImageNet (left) and ObjectNet (right).
the latter hypothesis due to the smaller number of misclassified images across all subsets, as shown through the higher error bars in fig. 14
6.12 What factors effect MVT? Imagenet-x analysis
We found no clear trends across MVT subsets for the 16 dimensions labeled in the imagenet-x dataset. The results of our analysis can be found in table 5.
6.13 Constructing a metric for image difficulty
We propose two metrics:
1. Difficulty score which provides an exact ranking from most difficult to recognize to least difficult to recognize based on each response
2. six minimum viewing time (MVT) subsets that quantify the minimum amount of time required for the majority of participants to reliably recognize an image.
Difficulty score is a value from 0 to 42 that represents the number of incorrect predictions given by participants in our experiment across all timings for a particular image. Each image in our experiment was seen an equal number of times per timing and and only rarely were images that were recognizable at shorter timings also recognizable at longer timings. This results in a low difficulty score indicating that an image is easy to recognize and a high difficulty score indicating that an image is hard to recognize. These scores correlate well with the MVT difficulty subsets as shown in fig. 15. Difficulty score varies significantly by object class as well (see fig. 16).
Figure 16: Difficulty distribution by object class sorted in order of increasing mean
6.14 Difficulty score distribution by object class
Figure 17: Robustness gap for our finetuned models on two randomly sampled subsets of our experiment data, balanced between ImageNet and ObjectNet. Lines connect model families with arrows pointing in direction of increasing model capacity. Compare with fig. 7.
Figure 18: Robustness gap for our finetuned ResNets trained on varying percentages of the ImageNet training set. Lines connect the same architectures with arrows pointing in direction of increasing dataset percentage. Compare with fig. 7.
Figure 19: Human accuracy vs Image presentation time from Mechanical Turk results. Time is log-scale.
Table 4: Model accuracy on the ImageNet and ObjectNet subsets of our 4,771 images.
ResNet-18	80.4	48.8
ResNet-18-20%	65.0	31.1
ResNet-18-40%	71.5	39.9
ResNet-18-60%	75.3	43.6
ResNet-18-80%	75.6	45.2
ResNet-50	87.1	60.0
ResNet-50-20%	71.0	38.3
ResNet-50-40%	77.7	46.4
ResNet-50-60%	80.4	51.6
ResNet-50-80%	82.9	52.6
ResNet-101	89.2	62.7
ResNet-101-20%	73.2	37.8
ResNet-101-40%	79.5	45.0
ResNet-101-60%	81.9	52.7
ResNet-101-80%	84.6	56.7
ResNet-152	90.3	64.5
ResNet-152-20%	73.4	38.5
ResNet-152-40%	78.7	47.9
ResNet-152-60%	82.3	51.4
ResNet-152-80%	84.6	54.8
CORNet-S	81.6	52.6
VOneNet-Resnet50	84.0	54.8
VOneNet-CORNet-S	81.3	48.8
VGG-19	81.1	50.2
Noisy Student (EfficientNet-L2)	86.2	51.2
DenseNet-121	83.7	55.7
MSDNet Classifier 0	62.8	29.3
MSDNet Classifier 1	74.6	41.6
MSDNet Classifier 2	78.6	49.6
MSDNet Classifier 3	78.9	51.6
MSDNet Classifier 4	81.0	52.8
SimCLR ResNet50	76.4	46.1
SimCLR ResNet101	82.1	55.4
SimCLR ResNet152	84.3	56.0
CLIP-ViT-B/32	80.0	63.3
CLIP-ViT-B/16	84.5	73.6
CLIP-ViT-L/14	92.1	85.8
CLIP-ViT-L/14@336px	92.0	86.9
CLIP-ResNet-50	71.6	56.8
CLIP-ResNet-101	75.3	61.4
CLIP-ResNet-50x4	77.5	64.6
CLIP-ResNet-50x16	80.1	72.4
CLIP-ResNet-50x64	87.1	79.4
EfficientNet-S	83.1	48.6
EfficientNet-M	81.7	47.2
EfficientNet-L	85.8	52.7
EfficientNet-S-21	90.2	66.4
EfficientNet-M-21	91.4	68.2
EfficientNet-L-21	91.5	68.8
ViT-T/16	59.4	29.9
ViT-S/16	84.4	57.1
ViT-B/16	86.5	61.3
ViT-L/16	94.1	78.1
MoCo-V3	85.5	56.6
SWSL-ResNext101-32x16d	95.1	82.3
SWSL-ResNet50	93.4	75.4
MAE-ViT-B/16	89.5	65.2