Comments (4)
Hi,
Actually, for a fair comparison with our previous version (SINet-V1), we adopt the same training settings [1] for the baselines.
In Table1, 6000 training images are the total number in our COD10K. However, we didn't use the non-camouflaged images as the training set. In addition, the test image is also 2026 in our experiments. The 4000 image is only statistics of our COD10K.
You can follow our code to train your model.
Best,
from sinet-v2.
Thank you for your prompt reply. So, in a word, the provided 4040 training/validation images and 2026 testing images are actually used by your PAMI paper, right? And I can employ the same setting to prepare for my own paper.
from sinet-v2.
from sinet-v2.
Great, thank you so much!
from sinet-v2.
Related Issues (19)
- Can I use this to train my custom dataset? HOT 1
- about instance-level image segmentation HOT 3
- What is the difference between RF and TEM? HOT 2
- About the presentation of camouflaged object detection HOT 2
- 指标计算代码 HOT 3
- Do you provide the result map corresponding to the test set? I don't seem to see it on github HOT 1
- 请问COD10K中bounding box的标注在哪,只看到了object annotation,即GT HOT 1
- 为什么训练过程的loss一直为NaN,降不下来,用了pretrained的res2net HOT 6
- gt = randomPeper(gt) - Data augmentation of the ground truth? HOT 1
- 评估S-measure时候出现Nan错误 HOT 1
- Error about E-measure. HOT 5
- 关于TEM模块的细节 HOT 2
- 关于Coarse Map 是如何生成的
- training details HOT 2
- What's the difference between Neighbor Connection Decoder (NCD) and Cascade Partial Decoder (CPD) ? HOT 1
- FPS HOT 1
- coco api HOT 2
- data structure HOT 1
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