Comments (2)
Hi @yi94code, you are free to add transformations as long as they don't introduce random behaviors.
For instance, you are free to resize or pad images, but please don't use those transformations that run based on probability.
from clvision-challenge-2022.
Thanks!
from clvision-challenge-2022.
Related Issues (20)
- where is the examples.tvdetection.transforms
- Different classes in train/test experiences
- training set of object detection containing bboxes of 0 area HOT 4
- 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)?
- If Using multiple accounts on CodaLab, but the overall number of submissions are less than 20, is ok?
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from clvision-challenge-2022.