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moto

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failed 227504
graded 227500
graded 227495

Understand semantic segmentation and monocular depth estimation from downward-facing drone images

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What data should you label to get the most value for your money?

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graded 177196
failed 177195
graded 177192

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graded 183405

Behavioral Representation Learning from Animal Poses.

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graded 186662
graded 186659
graded 186360

Airborne Object Tracking Challenge

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graded 153702
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ASCII-rendered single-player dungeon crawl game

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graded 150030
graded 150025
graded 148030

Machine Learning for detection of early onset of Alzheimers

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graded 143382
graded 143379
graded 143378

3D Seismic Image Interpretation by Machine Learning

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Play in a realistic insurance market, compete for profit!

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graded 127187
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graded 126085

A benchmark for image-based food recognition

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graded 116233
graded 116232
graded 116215

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graded 128320
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5 Puzzles, 3 Weeks | Can you solve them all?

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Participant Rating
Johnowhitaker 149
Participant Rating

ESCI Challenge for Improving Product Search

Simple baseline with simple transformer

Over 1 year ago

For task 3, it is 0.538.

PS: I am happy with this score given the fact that I only trained for 2 epochs with only limited number of rows.

Simple baseline with simple transformer

Over 1 year ago

Looking at the provided baseline, I found it hard to follow. Therefore I decided to publish my simple baseline.

Not all data were used nor any fine-tune has been done.

It just took more than 1 hour for training and 15 minutes for inference.

Hope it helps newcomers to understand the data and the problem.

Data Purchasing Challenge 2022

Purchase with anomaly detection

Over 1 year ago

Not so sure why the discussion is somehow quiet in round 2. I am sharing my first success.

I love anomaly detection so I tried to apply it here. It has been quite surprise that few tries have even worse result than the random purchase.

However, I finally manage to beat the random purchase. Insight could be found here
AIcrowd | Purchase with anomaly detection | Posts.

Please give me a vote for my hard work :slight_smile:

:aicrowd: [Update] Round 2 of Data Purchasing Challenge is now live!

Over 1 year ago

Many thanks for your quick reply.

“You will still have to train your models from scratch” => Could we use pre-trained weights as in round 1 ?

:aicrowd: [Update] Round 2 of Data Purchasing Challenge is now live!

Over 1 year ago

@snehananavati : Many thanks for your update. As far I understood, we actually don’t need to provide code for the training phase and the prediction phase. The system will use the same training pipeline - B4 with 10 epochs and then generate predictions and the scores by itself.

In other words, we only need to focus on the purchase phase. Do I understand correctly ?

0.9+ Baseline Solution for Part 1 of Challenge

Over 1 year ago

Oh, I missed this thread. Many thanks for your sharing @mark_haoxiang . It is quite interesting that the simple approach works well.

Experiments with “unlabelled” data

Over 1 year ago

@sergey_zlobin : Thanks for your information.

I am wondering if you tried to purchase all 10K then what the score could be.

Tensorflow/Keras folks, you are not being left behind in this competition

Over 1 year ago

@huynhngoc : Many thanks. I did not know that we could access labels_df.

Pseudo-labeling

Over 1 year ago

Indeed. I don’t see why we can’t use pseudo-labels. One of the naive approach for the purchase policy is to predict all images. 1) If the confidence is high => use the pseudo-label 2) if the confidence is low => purchase the label.

Baseline 0.84

Over 1 year ago

In order to support my request (Request to have the same baseline for everyone!), I shared my baseline with densenet. You could access the submission at AIcrowd Submission Received #174462 - v0p5 (#4) · Issues · moto / data-purchasing-optimization · GitLab.

If my submission can access the pre-trained weights for efficient net B6 from https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/, it could score 0.87+.

Request to have the same baseline for everyone!

Over 1 year ago

Dear organizer, all,

This challenge is a data-centric competition and the main purpose is to optimize the purchase. However, given our experiments the upper bound for the purchase policy is less than 3%. It means that we need to have a good baseline before we could work on the purchase policy.

Therefore, in my opinion, we should share the same training procedure. Our customer DL pipeline is only to optimize the purchase. I know it would be unfair for the current top teams but it would drive the competition in the way it should be.

Well, I am sharing my baseline with densetnet. It scores 0.84. The same pipeline with B6 could get 0.87+ (however, the pre-trained weights are not from the official site). The code is here AIcrowd

If it is impossible to force all participants, please allow us to use weights from other popular sources! I will try to publish a baseline 0.88+ so that people could focus more on the purchase optimization.

Many thanks.
M

The mystery of 0.489 and how to beat 2 deep-learning baselines with a single line of code

Over 1 year ago

If you look at my notebook AIcrowd | Baseline + Exploration: random purchase vs full purchase | Posts you could see that the zero-prediction solution got a score of 0.478 locally.

And that solution will score 0.489 in the LB, beating 2 public baselines.

How to do that, just replace

by

np.zeros(4).astype(int)

That’s it.

Baseline submission

Over 1 year ago

Update: The code is now using pre-trained weights. It has a better score now.

Baseline submission

Over 1 year ago

I am very happy to share my baseline Files · submission-v0p1p5 · moto / data-purchasing-hello · GitLab

The most challenging job is to use both 2 datasets (5K+3K) to train a model.

Note that the low score might be due to the fact that I haven’t included the pre-trained weights. Just submitted that one.

Right now, you guys could focus on

  • DL techniques such as different augmentations, network archs, schedulers …
  • Optimize purchase

Enjoy.

What did you get so far?

Over 1 year ago

Guys in the LB are fast but not me. I still need to understand how to submit.

What did you get so far?

Over 1 year ago

I guess you guys haven’t been able to defeat the random purchase :wink:

My experiment showed that 10K purchase is only a little bit better than 3K random purchase (https://www.aicrowd.com/showcase/exploration-random-purchase-vs-full-purchase). So, it is super hard to beat the random purchase.

Updated:

  1. The new link is https://www.aicrowd.com/showcase/baseline-exploration-random-purchase-vs-full-purchase
  2. The same code is included in my baseline

Files · submission-v0p1p5 · moto / data-purchasing-hello · GitLab

Airborne Object Tracking Challenge

🚨 Baseline released, SiamMOT: Siamese Multi-Object Tracking

About 2 years ago

@shivam: any way to run the baseline in colab for local validation ?

Max Number of Submissions?

About 2 years ago

@shivam: I am unable to submit today after 1 failed submission.

🚨 Clarification: Which are valid submissions?

About 2 years ago

@shivam: Many thanks for your quick reply.

Does it also mean using historical frames is accepted ?

moto has not provided any information yet.

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