Primary competition visual

Classification for Landslide Detection by ITU

1 000 CHF
Completed (~1 year ago)
Classification
Earth Observation
Python
GIS
Computer Vision
Machine Learning
Deep Learning
1028 joined
303 active
Starti
17 Apr 25
Closei
04 Aug 25
Reveali
04 Aug 25
User avatar
Koleshjr
Multimedia university of kenya
Final Landlslide Detection ML Stream – We Hit 14th Place!
Platform · 29 Jul 2025, 19:03 · 12

What a journey it's been!

Today marked the final stream in our Landslide Detection challenge, and I'm proud to share that we closed things off by reaching 14th place on the leaderboard! 💪🏽

In this last session, I covered:

  • My personal tips and tricks that helped us break into the Top 20
  • Techniques to boost the F1-score even further. Goal is to get to top 1 or top 3 worst case scenario
  • A bonus tip at the end (might be genius... or completely useless 😂)
  • Tomorrow we start a new competition

🎥 Watch the Final Stream Replay on YouTube: https://youtu.be/qvI-b-bbMQc?si=Y5uAvqGK8teFfJTm

🧠 Full Playlist & Other ML Streams: https://www.youtube.com/@koleshjr

📺 Catch future live sessions on Twitch (schedule here): https://www.twitch.tv/koleshjr/schedule

To everyone who watched, joined live, or supported this journey in any way — thank you. This wasn’t just about building models — it was about building community. I’ve learned a lot and hope you did too.

Until next time — keep learning, keep competing, and keep showing up. 🙌🏽

Update: 6th placed position is based on the stream!!!

Discussion 12 answers
User avatar
CodeJoe

Thank you boss! Truly appreciate it. Now time to go the fastai way! Thanks for the shoutout too in the previous stream😅

Upvotes 1
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Sodiq_Babawale_

That's why we call him big brains 😅

Upvotes 2
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CodeJoe

@Koleshjr, I am suffering to get reproducible code with fastai eventhough I used this:

def random_seed(seed_value, use_cuda):
    np.random.seed(seed_value)
    torch.manual_seed(seed_value)
    random.seed(seed_value)
    if use_cuda:
        torch.cuda.manual_seed(seed_value)
        torch.cuda.manual_seed_all(seed_value)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

random_seed(42, True)

When I re-run the training loop cell, I get a way different log from the previous one.

Upvotes 0
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Koleshjr

a simple hack would be to save the model weights in every experiment if the code is not reproducible so that you can reproduce your inference score during evaluation. I am not really sure why it is not reproducible though , I will look into it

Upvotes 0
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CodeJoe

Wouldn't that affect me in the case of a review. That was one reason I avoided Fastai. I tried so many times. If you run your cell multiple times, do you still the get the same results?

Upvotes 0
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Koleshjr

The essence of saving the finetuned weights it to reproduce your score without retraining again since you already have the weights(There is no like time limit in this competition, therefore there is no need to retrain your training notebook to make sure it fits the time limit like in other competitions) . Infact it makes the review easier since they won't need to re-run your solution from the training phase, they will just do inference with the saved weights. I haven't tried rerunning either of my notebooks so I haven't tested if I get the same results.

Upvotes 1
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CodeJoe

Alright, makes perfect sense. Thank you @Koleshjr. Now let the competition begin🤣🔥

Upvotes 0
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Koleshjr

goodluck!

Upvotes 1
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CodeJoe

Good Luck too bro!

Upvotes 0
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i am sufering from missing id error i need helper on this

Upvotes 1
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CodeJoe

Check whether the IDs in your subs follow the sample submission file. Then try using index = False in the block of code. Eg:

sub.to_csv(f"{CFG.model_name}_all_folds.csv", index= False)
Upvotes 0
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Upvotes 0