Closed joshua-atolagbe closed 2 months ago
Hi, you do not need to build model from scratch. Whenever a pretrained model is used, the weights are loaded. For example, if you use fasterrcnn_resnet50_fpn_v2
, then the respective pretrained weights are loaded.
UPDATE: I have modified the print statement to "Building model from models folder..." during training so that its less confusing.
Thank you so much for your hard work @sovit-123 . Also, I am having issue when making inference. The finetuned fasterrcnn_resnet50_fpn_v2
model detects multiple objects even though it is trained to identify just one object type. Say if the number of that objects that i want to detect in an image is 2, the model detects about 1000 of that object in the image. What could be the issue?
Also, how can i use the finetune model with SAHI to perform tiled inference?
I think it may boil down to the training. May I know what is the dataset, how many epochs you are training for, and what mAP you are getting?
Sorry for the earlier confusion @sovit-123 . I had a test mAP@50 of 77% using the eval.py
. The issue stems from me trying to perform SAHI inference with the finetune model.
Oh. Understood. So, is the issue solved for you now. Can I close it?
@sovit-123 Please how do I use pretrained weights from torch vision as initialization weights to train the fasterRCNN on my custom data? I don’t want to build the model from scratch.