Comments (4)
lrzpellegrini
commented on May 25, 2024
Hi Zhou, thank you for reporting this. That's strange. I'll investigate it ASAP.
from clvision-challenge-2022.
Millielele
commented on May 25, 2024
569716681169025
@lrzpellegrini hello, I also find this problem.So how should i solve that?
from clvision-challenge-2022.
lrzpellegrini
commented on May 25, 2024
There seems to be a single box with an area <= 0.0, which is that one. It seems to be connected to the mug over the fridge, so it's definitely an issue with the annotation. Fortunately, that annotation is not a main object (the main object for that image is the refrigerator), so this only affects the "Category Detection" track. In addition, the test set contains no such problems, so there are no issues on the scoring side.
Let me see if I can somewhat fix this for everyone without re-creating the JSON!
from clvision-challenge-2022.
lrzpellegrini
commented on May 25, 2024
Solved in the main branch. Closing!
from clvision-challenge-2022.
Related Issues (20)
- where is the examples.tvdetection.transforms
- Different classes in train/test experiences
- Detection test set all instances have "category_id": 2, "instance_id": "unlabeled" HOT 4
- the challenge dataset download failed.
- What's this repo actually for? HOT 1
- Why track2 demo results AP seems strange? HOT 4
- mandatory metric only generate output of 1 json file for last experience HOT 1
- Track2 Submission Problem: AttributeError: 'list' object has no attribute 'items' HOT 2
- can you provide a result file JSON format? HOT 3
- can you release the metrics of task1-task5 in leadboard? HOT 2
- #track 2: can you release the upper-bound performance of task1-task5? The test set contains 277 categories, while the training set of task1 only has 159 categories, then the upper-bound of task1 is? HOT 2
- #track 2: 'Solutions can exploit a replay buffer containing data coming from up to 3500 training instances.' For example, in task1, can we randomly sample 3500 images from the full training set (ego_objects_challenge_train.json)? HOT 2
- Track3 baseline doesn't work correctly HOT 1
- Track1: Could you release the upper-bound performance(average mAP) during each experience? HOT 3
- Cannot iterate all data streams in one epoch. HOT 1
- Can we use multiple GPUs in training ? or we must use one GPU (RTX 5000 with 16G GPU-memory)? HOT 3
- Can you provide the evaluation server (RTX 5000 with 16G GPU-memory)?
- Can we add augmentation transforms to the eval transformations? HOT 2
- If Using multiple accounts on CodaLab, but the overall number of submissions are less than 20, is ok?
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