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mohanty
Sharada Mohanty

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AIcrowd

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Geneva, CH

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Challenges Entered

Improve RAG with Real-World Benchmarks

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graded 257157
graded 257156
failed 257155

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graded 265232
failed 265231
graded 264852

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graded 248138
failed 248137
graded 248067

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graded 252074

Multi-Agent Dynamics & Mixed-Motive Cooperation

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No submissions made in this challenge.

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failed 238783

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failed 211911
failed 211910

Small Object Detection and Classification

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No submissions made in this challenge.

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

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graded 208984
failed 208983

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failed 208761

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failed 209217
failed 209215
failed 209214

A benchmark for image-based food recognition

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graded 198220

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failed 197325
failed 197129
failed 197122

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

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Interactive embodied agents for Human-AI collaboration

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failed 196525
failed 196516
failed 196513

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graded 199672
graded 196837

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failed 192389
graded 178805
graded 178804

Behavioral Representation Learning from Animal Poses.

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Airborne Object Tracking Challenge

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ASCII-rendered single-player dungeon crawl game

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Improving the HTR output of Greek papyri and Byzantine manuscripts

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No submissions made in this challenge.

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Machine Learning for detection of early onset of Alzheimers

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5 Puzzles 21 Days. Can you solve it all?

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Sample Efficient Reinforcement Learning in Minecraft

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Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

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Robustness and teamwork in a massively multiagent environment

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3D Seismic Image Interpretation by Machine Learning

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graded 82855
graded 82679
graded 82664

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Play in a realistic insurance market, compete for profit!

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Multi-Agent Reinforcement Learning on Trains

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A dataset and open-ended challenge for music recommendation research

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failed 197464

A benchmark for image-based food recognition

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graded 109590
failed 109586
failed 109578

Sample-efficient reinforcement learning in Minecraft

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Predicting smell of molecular compounds

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graded 101429
graded 101420
graded 81550

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failed 5795
graded 392
failed 391

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5 Problems 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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Robots that learn to interact with the environment autonomously

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5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

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Grouping/Sorting players into their respective teams

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graded 18446
graded 10385
graded 10099

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graded 21050
graded 13148
graded 13117

Sample-efficient reinforcement learning in Minecraft

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graded 18575
graded 15152
graded 11232

Multi Agent Reinforcement Learning on Trains.

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failed 18455
failed 18370
failed 18105

Recognise Handwritten Digits

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failed 60098

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No submissions made in this challenge.

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5 Problems 15 Days. Can you solve it all?

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graded 198009

Project 2: Road extraction from satellite images

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Robots that learn to interact with the environment autonomously

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graded 11208
failed 11198
failed 11193

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A new benchmark for Artificial Intelligence (AI) research in Reinforcement Learning

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graded 7057
graded 664
graded 663

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graded 1137
graded 1135
graded 1134

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graded 9169
graded 9164

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graded 9168
graded 9163

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graded 9167
failed 9165
failed 9162

Predict if users will skip or listen to the music they're streamed

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No submissions made in this challenge.

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graded 239544
failed 239542

5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

Latest submissions

No submissions made in this challenge.

Predict if users will skip or listen to the music they're streamed

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failed 197463

Predict viewer reactions from a large-scale video dataset!

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No submissions made in this challenge.

Detect Multi-Animal Behaviors from a large, hand-labeled dataset.

Latest submissions

No submissions made in this challenge.

Multi-Agent Reinforcement Learning on Trains

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No submissions made in this challenge.

Use an RL agent to build a structure with natural language inputs

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failed 196525
failed 196516
failed 196513

Estimate depth in aerial images from monocular downward-facing drone

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Perform semantic segmentation on aerial images from monocular downward-facing drone

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graded 208984
failed 208983

Music source separation of an audio signal into separate tracks for vocals, bass, drums, and other

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failed 219226

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graded 252074

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Testing RAG Systems with Limited Web Pages

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failed 257155
failed 257148
graded 257147

Make Informed Decisions with Shopping Knowledge

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graded 259446
failed 259407
graded 251060

Understand Dynamic Customer Behaviour

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graded 259447
failed 259408
graded 251061

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graded 261318
Participant Rating
20161302_animesh 132
shivam 136
semih_catal 0
SHARDA 0
LazyPanda 0
Shubhamaicrowd 0
vrv 0
omkarkur 0
singstad90 0
amitabh 0
krishna_kaushik 0
ydesign12 0
victim_vict 0
nutansahoo 0
lyz22233 0
ozgur 0
Participant Rating

Meta Comprehensive RAG Benchmark: KDD Cup 2

How to use Mock API in submission

About 1 month ago

Please refer to the example here:

You do not need to include the MockAPI in your submission, it will be automatically made available in your evaluation environment, and the correct URI will be populated in the environment variable referenced in the example above (CRAG_MOCK_API_URL)

Function calling arguments in local_evaluation.py is mismatching with dummy_model's interface

3 months ago

@evelynintech : Can you please ensure that you are using git lfs pull to pull the example dataset files via git lfs ?
You can also verify the contents of the example data files in the example_data folder to ensure that they are locally available.

Meta KDD Cup 24 - CRAG - Retrieval Summarization

Submit error

3 months ago

@yong_deng : Can you please try submitting again now ?
As the error says, we had a scheduled maintenance yesterday, so we were not accepting submissions when you made your submission.

Amazon KDD Cup 2024: Multi-Task Online Shopping Ch

Track2 Validation failed:No module named 'aicrowd_gym'

3 months ago

@carpe : We have requeued the submission which failed with "No module named ‘aicrowd_gym’”, it was because of a transient error at our end. You are not expected to manually install aicrowd_gym.

Frequently Asked Questions

How to add SSH key to Gitlab?

5 months ago

@momi193 : Thanks for pointing it out, we have updated the links in the main message as well.

Best,
Mohanty

Commonsense Persona-Grounded Dialogue Chall-459c12

Submission failure for Task 2

5 months ago

Hi Iris,

The issue reported seems specific to the submissions made by your account, as there are numerous successful submissions after the timestamp reported in your message.

We acknowledge that the error propagation still has some cluster specific issues that we are trying to resolve as we speak.
In the meantime, please find attached the error for your submission.

submission-248780-evaluation-4331-188153-bl8rn-1190196615: /home/aicrowd/.conda/lib/python3.9/site-packages/transformers/convert_slow_tokenizer.py:515: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   warnings.warn(
submission-248780-evaluation-4331-188153-bl8rn-1190196615: Traceback (most recent call last):
submission-248780-evaluation-4331-188153-bl8rn-1190196615: Evaluation started
submission-248780-evaluation-4331-188153-bl8rn-1190196615: Init wrapper
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/run.py", line 3, in <module>
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     start_test_client()
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/client_launcher.py", line 20, in start_test_client
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     client.run_agent()
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/aicrowd_gym/clients/base_oracle_client.py", line 193, in run_agent
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     raw_response, status, message = self.process_request(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/aicrowd_gym/clients/base_oracle_client.py", line 99, in process_request
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     "data": self.route_agent_request(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/aicrowd_gym/clients/base_oracle_client.py", line 129, in route_agent_request
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self.execute(target_attribute, *args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/aicrowd_gym/clients/base_oracle_client.py", line 142, in execute
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return method(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/aicrowd_wrapper.py", line 30, in classify_link
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self.agent.classify_link(test_data_batch)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/agents/deberta_v3_large_nli_comFact_agent.py", line 56, in classify_link
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     head_logits = self.model(**head_inputs).logits
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self._call_impl(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return forward_call(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 1300, in forward
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     outputs = self.deberta(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self._call_impl(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return forward_call(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 1070, in forward
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     encoder_outputs = self.encoder(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self._call_impl(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return forward_call(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 514, in forward
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     output_states = layer_module(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self._call_impl(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return forward_call(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 362, in forward
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     attention_output = self.attention(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self._call_impl(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return forward_call(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 293, in forward
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     self_output = self.self(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return self._call_impl(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     return forward_call(*args, **kwargs)
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 721, in forward
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     rel_att = self.disentangled_attention_bias(
submission-248780-evaluation-4331-188153-bl8rn-1190196615:   File "/home/aicrowd/.conda/lib/python3.9/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 819, in disentangled_attention_bias
submission-248780-evaluation-4331-188153-bl8rn-1190196615:     p2c_att = torch.gather(
submission-248780-evaluation-4331-188153-bl8rn-1190196615: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.29 GiB. GPU 0 has a total capacty of 14.75 GiB of which 1.02 GiB is free. Process 38611 has 13.73 GiB memory in use. Of the allocated memory 11.06 GiB is allocated by PyTorch, and 2.54 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

Best,
Mohanty

🚨 Important Update: Challenge Deadline Extended & Update to Rules

5 months ago

Hi kevin,

There was indeed an issue with compute node assignment earlier today, but it has been fixed now. We can see that the image building for your latest submission has already started.

Generative Interior Design Challenge 2024

Resources section

5 months ago

@soham17 : The starter kit for the challenge is available here: AIcrowd / Challenges / Generative Interior Design Challenge 2024 / Generative Interior Design 2024 starter kit · GitLab

It is also linked at the top of the Challenge Overview Page here: AIcrowd | Generative Interior Design Challenge 2024 | Challenges

Best of Luck,
Mohanty

Sound Demixing Challenge 2023

No Submission Slots Remaining

About 1 year ago

@crlandsc : The submission quotas have not changed. The submission quotas are checked against the number of submissions made by your (or any of your team members) in the last 24 hour window.
So for example, if you make 3 submissions at 23:55 on Day 1, then you can make only 2 more submissions until 23:55 on Day 2 (assuming the submission quota is 5 submissions / day).
The daily submission quotas are not automatically reset at midnight each day.

In the last few days, these are the only submissions we see from your account:

  • April 28th 04:13 UTC
  • April 28th 04:33 UTC
  • April 28th 04:45 UTC
  • April 28th 15:34 UTC
  • April 29th 19:34 UTC

All of the said submissions failed to evaluate for unrelated reasons (for more details please refer to the relevant debug logs in the issues associated with the failed submissions), and not because of exceeding submission quotas.

Additionally, we are updating the quotas for the competition, to allow up to 5 failed submissions which would not count towards your daily submission quota of 5 submissions.

Submissions are being stuck on "Preparing the cluster for you" for a long time

About 1 year ago

@kimberley_jensen : There is a spike in the number of submissions because of the approaching deadline, and all the submissions are queued on the evaluation servers, and are all being eventually evaluated. Some submissions are timing out because of being in the queue for too long, and we are manually re-queuing them when that happens.

We will keep a close eye on the submission queues, and intervene as required.
Thank you for your patience.

Submission failed : Failed to communicate with the grader

Over 1 year ago

This issue has been resolved now. Please do let us know if you continue to face this issue.

Best,
Mohanty

Feedback and suggestion

Over 1 year ago

@andrey1362010 : A person who is a citizen of Russia and is currently a resident of another eligible country, with an active bank account present in that eligible country, is eligible to receive prizes.

Feedback and suggestion

Over 1 year ago

Apologies for the relative radio silence due to the holiday season.

GPUs

We are still waiting for a response from the organizing team about the provision of GPUs, so we will have to hold off on answering the question until we hear back from the organizing team.

Throughputs

We are investigating the issue with differential throughputs across different evaluations. A new instance is instantiated for every evaluation on our cloud provider, and the instance type is exactly the same - and hence the resources available. We have confirmed that the exact same instance type is being made available to all the submissions as well. We will get back to you with more details on this soon as well.

Timeouts

The current timeouts are 1hr, or 60 mins.

Apologies on the slow response times due to the Holiday season. We will be providing support at full capacity again starting 2nd of January, 2023.

Best,
Mohanty

Data Purchasing Challenge 2022

Has anyone actually got the prize payment?

Over 1 year ago

Dear All,

All the prizes here have been processed except one participant where the said participant is a Russian national with a bank that our banking partners do not support any transactions to.

We are working closely with the said participant and our financial partners to come to a resolution soon. In the meantime, we have the confirmation from the rest of the winners about the receipt of their prizes.

Best,
Mohanty

SUADD'23- Scene Understanding for Autonomous Drone

About my submissions

Over 1 year ago

@victorkras2008 : can you please try again. You should be able to access the submission lists on both the challenges.

About my submissions

Over 1 year ago

@victorkras2008 : You should be able to see all your submissions in the submissions tab, for ex: AIcrowd | Semantic Segmentation | Submissions

💬 Feedback and suggestion

Over 1 year ago

@ricardodeazambuja : Yes the observation is correct.

As described here:

The dataset contains 422 flights, 2056 total frames (5 frames per flight at different AGLs),  Full semantic segmentation annotations of all frames and depth estimations. The dataset has been split into training and (public) test datasets. While the challenge will be scored using a private test dataset, we considered it useful to have this split to allow teams to share their results even after the challenge ends.

Of the total frames available, a subset if used for the (public) test set that you are currently being scored on. There will be an additional (private) test set that the final leaderboards will be based on. And submissions are currently limited to 5 submissions / day.

Best,
Mohanty

Mono Depth Perception

About leaderboard sorting

Over 1 year ago

@victorkras2008 : yes the sorting on the leaderboard is correct. In case of Scale invariant logarithmic error (SILog), a lower score is better.

Bad link to "Baseline for Mono Depth Perception" 📅

Over 1 year ago

@victorkras2008 : Thanks for pointing it out.The links are correct, we realized that theres a few more things that need to be updated in the repo before they can be made public. So sorry for the confusion, we will try to make the repositories public as soon as we can.

Thanks,
Mohanty

mohanty has not provided any information yet.