Comments (4)
We implemented evaluation by first extracting features from all class templates (class mages) and then using these features to detect everywhere. My hypothesis is that your dataset has too many classes to detect. I think that you can do one of the two things: 1) split classes in several "class batches" such that each batch would fit in GPU memory; 2) disable caching the class features and recompute everything on the fly - however this approach might slow down detection.
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Yes, you're correct. I have a lot of classes (16k with 0.7M images). For the first approach that you suggested, is there any provision in the code to do so? For the second approach, is setting cache_images to False all that's needed?
from os2d.
I'm afraid none of these are supported in the code. cache_images seems to do something different.
Probably the easiest thing to do is to split data manually for the first approach. For the second approach, you'll need to changes the iterators over data.
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Okay. I'll give it a shot. Thanks
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Related Issues (20)
- Same class images with different class_ids get different results? HOT 1
- Attributes of grozi.csv HOT 3
- Error on using multi GPU setting HOT 2
- What is 3264 in the src? HOT 2
- Cannot find 'net' in the checkpoint file HOT 2
- Need for OrderedDict HOT 2
- Security alert from dependabot HOT 3
- Multi-GPU training for detector HOT 6
- Segmentation fault HOT 4
- Error in visualisation HOT 1
- New form of evaluation HOT 3
- some new ideas about YOLO to os2d HOT 1
- Memory Leak when training and long evaluation time HOT 3
- Long Evaluation time and question about environment for training HOT 4
- Dataset scales when training with custom data HOT 2
- Evaluation Dataset HOT 1
- Unable to reproduce correct results from demo.ipynb HOT 2
- read image with cv2.imread
- how can I train my custom dataset base on OS2D V2-train model?
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