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• Network Type : Detectnet_v2
• TLT Version: v5.5.0
We see a strange observation on detectnetv2 model. The model was trained for 300 epochs and was evaluated with NMS and DBSCAN+NMS. We see acceptable results in hdf5 models with mAP values
When converted to ONNX and checked mAP, we see there is a huge drop in mAP value. We are attaching the accuracy values of each scenario below:
- mAP for nms hdf5_model
Car AP: 77.16%
Pedestrian AP: 51.01%
Cyclist AP: 74.26%
mAP (mean AP): 67.48%
2)mAP for DBSCAN hdf5 model
Car AP: 78.22%
Pedestrian AP: 52.11%
Cyclist AP: 75.49%
mAP (mean AP): 68.60%
3)mAP for DBSCAN Onnx model
Car AP: 6.28%
Pedestrian AP: 1.79%
Cyclist AP: 0.97%
mAP (mean AP): 3.01%
We observe the accuracy has reduced from 70% to 3%. Why there is so much drop observed in Detectnetv2 model.
It will be helpful if you could let us know the evaluation methos for ONNX model to compare our results.
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