ImageNet

You can view the ImageNet leaderboard here.

Getting Started

You'll need the following in the root of your repository:

• sotabench.py file - contains benchmarking logic; the server will run this on each commit
• requirements.txt file - Python dependencies to be installed before running sotabench.py
• sotabench_setup.sh (optional) - any advanced dependencies or setup, e.g. compilation

You can write whatever you want in your sotabench.py file to get model predictions on the ImageNet dataset. For example, PyTorch users might use torchvision to load the dataset.

But you will need to record your results for the server, and you'll want to avoid doing things like downloading the dataset on the server. So you should:

• Point to the server ImageNet data paths - popular datasets are pre-downloaded on the server.
• Include an Evaluation object in sotabench.py file to record the results.
• Use Caching (optional) - to speed up evaluation by hashing the first batch of predictions.

We explain how to do these various steps below.

Server Data Location

The ImageNet validation data is located in the root of your repository on the server at .data/vision/imagenet. In this folder is contained:

• ILSVRC2012_devkit_t12.tar.gz - containing metadata
• ILSVRC2012_img_val.tar - containing the validation images

Your local ImageNet files may have a different file directory structure, so you can use control flow like below to change the data path if the script is being run on sotabench servers:

from sotabencheval.utils import is_server

if is_server():
    DATA_ROOT = './.data/vision/imagenet'
else: # local settings
    DATA_ROOT = '/home/ubuntu/my_data/'

This will detect if sotabench.py is being run on the server and change behaviour accordingly.

How Do I Initialize an Evaluator?

Add this to your code - before you start batching over the dataset and making predictions:

from sotabencheval.image_classification import ImageNetEvaluator

evaluator = ImageNetEvaluator(model_name='My Super Model')

If you are reproducing a model from a paper, then you can enter the arXiv ID. If you put in the same model name string as on the leaderboard then you will enable direct comparison with the paper's model. For example:

from sotabencheval.image_classification import ImageNetEvaluator

evaluator = ImageNetEvaluator(model_name='FixResNeXt-101 32x48d',
paper_arxiv_id='1906.06423')

The above will directly compare with the result of the paper when run on the server.

How Do I Evaluate Predictions?

The evaluator object has an .add() method to submit predictions by batch or in full.

For ImageNet the expected input as a dictionary of outputs, where each key is an image ID from ImageNet and each value is a list or 1D numpy array of logits for that image ID. For example:

    evaluator.add({'ILSVRC2012_val_00000293': np.array([1.04243, ...]),
    'ILSVRC2012_val_00000294': np.array([-2.3677, ...])})

You can do this all at once in a single call to add(), but more naturally, you will probably loop over the dataset and call the method for the outputs of each batch. That would like something like this (for a PyTorch example):

for i, (input, target) in enumerate(test_loader):
    input = input.to(device='cuda', non_blocking=True)
    target = target.to(device='cuda', non_blocking=True)
    output = model(input)

    image_ids = [get_img_id(img[0]) for img in test_loader.dataset.imgs[i*test_loader.batch_size:(i+1)*test_loader.batch_size]]

    evaluator.add(dict(zip(image_ids, list(output.cpu().numpy()))))

When you are done, you can get the results locally by running:

evaluator.get_results()

But for the server you want to save the results by running:

evaluator.save()

This method serialises the results and model metadata and stores to the server database.

How Do I Cache Evaluation?

Sotabench reruns your script on every commit. This is good because it acts like continuous integration in checking for bugs and changes, but can be annoying if the model hasn't changed and evaluation is lengthy.

Fortunately sotabencheval has caching logic that you can use.

The idea is that after the first batch, we hash the model outputs and the current metrics and this tells us if the model is the same given the dataset. You can include hashing within an evaluation loop like follows (in the following example for a PyTorch repository):

with torch.no_grad():
    for i, (input, target) in enumerate(test_loader):
        input = input.to(device='cuda', non_blocking=True)
        target = target.to(device='cuda', non_blocking=True)
        output = model(input)

        image_ids = [get_img_id(img[0]) for img in test_loader.dataset.imgs[i*test_loader.batch_size:(i+1)*test_loader.batch_size]]

        evaluator.add(dict(zip(image_ids, list(output.cpu().numpy()))))

        if evaluator.cache_exists:
            break

evaluator.save()

If the hash is the same as in the server, we infer that the model hasn't changed, so we simply return hashed results rather than running the whole evaluation again.

Caching is very useful if you have large models, or a repository that is evaluating multiple models, as it speeds up evaluation significantly.

A full sotabench.py example

Below we show an implementation for a model from the torchvision repository. This incorporates all the features explained above: (a) using the server data root, (b) using the ImageNet Evaluator, and (c) caching the evaluation logic:

import numpy as np
import PIL
import torch
from torchvision.models.resnet import resnext101_32x8d
import torchvision.transforms as transforms
from torchvision.datasets import ImageNet
from torch.utils.data import DataLoader

from sotabencheval.image_classification import ImageNetEvaluator
from sotabencheval.utils import is_server

if is_server():
    DATA_ROOT = './.data/vision/imagenet'
else: # local settings
    DATA_ROOT = '/home/ubuntu/my_data/'

model = resnext101_32x8d(pretrained=True)

input_transform = transforms.Compose([
    transforms.Resize(256, PIL.Image.BICUBIC),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(
    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

test_dataset = ImageNet(
    DATA_ROOT,
    split="val",
    transform=input_transform,
    target_transform=None,
    download=True,
)

test_loader = DataLoader(
    test_dataset,
    batch_size=128,
    shuffle=False,
    num_workers=4,
    pin_memory=True,
)

model = model.cuda()
model.eval()

evaluator = ImageNetEvaluator(
                 model_name='ResNeXt-101-32x8d',
                 paper_arxiv_id='1611.05431')

def get_img_id(image_name):
    return image_name.split('/')[-1].replace('.JPEG', '')

with torch.no_grad():
    for i, (input, target) in enumerate(test_loader):
        input = input.to(device='cuda', non_blocking=True)
        target = target.to(device='cuda', non_blocking=True)
        output = model(input)

        image_ids = [get_img_id(img[0]) for img in test_loader.dataset.imgs[i*test_loader.batch_size:(i+1)*test_loader.batch_size]]

        evaluator.add(dict(zip(image_ids, list(output.cpu().numpy()))))

        if evaluator.cache_exists:
            break

evaluator.save()

Need More Help?

Head on over to the Computer Vision section of the sotabench forums if you have any questions or difficulties.