Open stellating opened 5 years ago
@stellating I suggest you visualize your detection results to ensure your model works well.
@stellating the same with you,how do you deal with it?
same as me
same as me
I got the same problem. Did you fix it?
same as me
I got the same problem. Did you fix it?
no, I think it is the problem that the num of training class is 81 in source code, but mine is different, and he didn't say how to change it. When i was training, i just loaded all backbone weights, but still doesn't work, XD. So i decide to build a trainer by myself
no, I think it is the problem that the num of training class is 81 in source code, but mine is different, and he didn't say how to change it. When i was training, i just loaded all backbone weights, but still doesn't work, XD. So i decide to build a trainer by myself
Well, you can change the category number in fcos_core/config/default.py. But...I still didn't figure out why this issue happened.
oh...I found the problem..the fetch_data function that I wrote has a bug....
@liudanyang I met similar problem, and could you tell me the how wrong about fetch_data function detailly? Maybe, i can learn from it, thank you.
@JerryIndus Hi.Have you solved the problem?
@liudanyang Could you tell me how do you fix the problem?
Hello, Does anyone fix it?
Does anyone fix it?
I have trained FCOS on my own dataset which has 3 classes, and I'v got a model with 87500 iterations. But when I try to take an inference on my own datasets, I get the following massage:
Running per image evaluation... Evaluate annotation type bbox DONE (t=0.62s). Accumulating evaluation results... DONE (t=0.22s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Maximum f-measures for classes: [-0.0, -0.0, 0.0] Score thresholds for classes (used in demos for visualization purposes): [-1.0, -1.0, 0.9576103687286377] 2019-09-06 10:20:00,552 fcos_core.inference INFO: OrderedDict([('bbox', OrderedDict([('AP', 0.0), ('AP50', 0.0), ('AP75', 0.0), ('APs', -1.0), ('APm', -1.0), ('APl', 0.0)]))])
Could you please help me?