Comments (3)
Hi !
You can train a YOLOv5 model on whatever dataset you like by following the instructions provided here : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
As long as you follow these instructions and select an architecture that is supported, all you have to do is run the explain.py module in the command line and provide your custom model weights via the --weights argument.
ok Thank you so much..
from yolo-heatmaps.
Hi !
You can train a YOLOv5 model on whatever dataset you like by following the instructions provided here : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
As long as you follow these instructions and select an architecture that is supported, all you have to do is run the explain.py module in the command line and provide your custom model weights via the --weights argument.
from yolo-heatmaps.
How to implement this for customize dataset??
from yolo-heatmaps.
Related Issues (8)
- Paper release HOT 2
- NotImplementedError: Relevance propagation not implemented for layer type <class 'models.common.SPP'> HOT 5
- Python lib versions HOT 3
- IndexError in lrp.utils::LayerRelevance.scatter(-1).size(1) caused in lrp.common::prop_C3 using Custom YOLOv5 v6.1/6.2 HOT 4
- Gradual increase in processing time
- Kernels relevancy score
- Please add venv installation
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