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Comment by sillysaurusx | Hacker News Reader

Suppose someone wanted to train a model to identify which decade a photo was taken in. What would be the current SOTA architecture for that type of task? (Suppose also that you had a few million labeled examples.)

I like yolo because it’s a production grade object defector. It seems harder to find a production grade classifier.

One amusing but dumb idea would be to use yolo for this: train the model on “photo from 1930,” “photo from 1940,” etc, where the bounding boxes cover the entire photo. But I’m curious what the professional solution might be.

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Easy, just use an ImageNet classifier architecture with one category for each decade. No need to bother with object detection at all.

You could fine-tune or train from scratch. Image classification is probably the single most-researched task with the widest variety of models available. You could select an architecture based on ease of implementation, efficiency, absolute accuracy, or any combination. Papers With Code has great lists of state-of-the-art models. Take your pick: https://paperswithcode.com/sota/image-classification-on-imag...

I am guilty of using your dumb idea with satisfactory performance.

I'd recommend EfficientNet (or one of its may variations) for an off-the-shelf SOTA classifier. https://paperswithcode.com/sota/image-classification-on-imag...

Yolo isn't really suitable for classification - it's an object detector.

For maximum accuracy there are high-quality well tested ResNet128 or ResNet152 implementations for PyTorch[1] and TensorFlow[2] that most people would use as a basis for classification tasks where the highest accuracy is needed. Lower quality ResNets (eg ResNet50) run a lot faster.

Note that in this post, PP-YOLO replaces the older YOLO backbone with a ResNet50 (ResNet50-vd-dcn to be precise) backbone.

EfficientNet is another good option[3], as is SE-ResNet.

For this specific task it's not entirely clear what/how the classification is supposed to work though. The source of the photos is really, really important: if they are physical photos then the scanner used is important. And things like different film have different tone, and storage of physical photos matters a lot.

[1] https://pytorch.org/hub/pytorch_vision_resnet/

[2] https://tfhub.dev/google/imagenet/resnet_v2_152/feature_vect...

[3] https://tfhub.dev/google/collections/efficientnet/1

It depends on if you're OK with the classifier overfitting on potentially meaningless details (like a shade of color of the paper most commonly used in that decade, etc) or if you actually want to classify the images based on content (style of clothes, etc).

Out of curiosity, what would such a model be used for? I worry that as ML is becoming more popular and powerful, people are jumping to use it on problems from which the answer cannot possibly be identified accurately from the inputs alone. This model's predictions would entirely based on historical trends of what images looked like, rather than something objective like carbon dating.

I am not discouraging someone from building such a model, but it would be really helpful to know the context for which such a model is being developed. If it's just a hobby investigation, it would be cool to see how "predictable" dates are from images. I could even see it being used in forensics to provide a "first guess" as to when an image occurred, and helping with triaging of evidence. However, things become deeply problematic if the result from the image is fed to people as "ground truth" simply because the model was found to be accurate on a validation dataset. I certainly wouldn't want this model to be used to determine whether a suspect is innocent / guilty, or to be used naively by museums to date photographs.

For example, creating a scrapbook of somebody's life from scanned photographs that lack any metadata.

Could be useful for identifying faked historical photos?

Depends on whether you are looking for effectiveness vs efficiency. YOLO et al. are optimised for general feature extraction. They are ridiculously good if you need to build something quickly. If you are looking for the most efficient way e.g. lowest processing power per image then use a network that is optimised for the particular feature you are trying to extract.

Highest accuracy. I was mainly wondering which model ML engineers reach for circa 2020 for classification tasks. Searching for classifiers brings up article after article presenting toy classifiers, but very little with a production focus.

I've seen people get good results with Efficientnet, and it scales to be quite large. In general, I look at whatever the best state of the art is on imagenet, but then look for implementations that look easy to use among the top few.

I would probably first start with examples from different eras and try to build a statistical classifier on it without resorting to detectors. Those seem like big overkill for such a task.

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