Open pjspillai opened 5 years ago
@pjspillai Hi,
What mAP
did you get before 150k and after 150k?
What soft/script did you use to measure accuracy?
Can you reneame your cfg-file to txt-file and drag-n-drop it to your message?
Did you check that you correctly have merged 2 datasets into 1?
Did you train for 5 or 6 classes?
I have been training yolo3 on the COCO+Pascal dataset (on a subset of 5 classes, out of the 80).
[yolo] mask = 0,1,2 anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 classes=6
Hi Alexey,
mAP
did you get before 150k and after 150k?
1000: 7.94
10000: 36.8
20000: 45.69
100000: 47.3
150000: 37.28
200000: 43.84
300000: 46.98
350000: 37.20(It keeps on fluctuating up/down with every 50k iterations)
What soft/script did you use to measure accuracy? I actually used your fork of the darknet for the mAP score computation.
Can you rename your cfg-file to txt-file and drag-n-drop it to your message? Attached yolov3.txt
Did you check that you correctly have merged 2 datasets into 1? I checked them yes.
Did you train for 5 or 6 classes? I trained for 6 classes [person, bicycle, bus, car, motorbike, truck]
@pjspillai Hi,
- What soft/script did you use to measure accuracy?
- Can you reneame your cfg-file to txt-file and drag-n-drop it to your message?
- Did you check that you correctly have merged 2 datasets into 1?
- Did you train for 5 or 6 classes?
I have been training yolo3 on the COCO+Pascal dataset (on a subset of 5 classes, out of the 80).
[yolo] mask = 0,1,2 anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 classes=6
@pjspillai
What validation dataset do you use?
Why did you set batch=16 subdivisions=8
instead of, for example, batch=64 subdivisions=32
?
What validation dataset do you use? It's the subset of the COCO 5k val images having the 6 classes of interest + the Pascal subset
Why did you set batch=16 subdivisions=8
instead of, for example, batch=64 subdivisions=32
?
It gave me cuda memory error on the 1070 GPU. So I lowered the batch/subdivision size for the training to work.
I am re-running the training with the batch=64,subdivision=32 for the multi-gpu and it's training so far without any cuda errors. Will wait and see how this performs.
Not sure if the batch/subdivision size will affect the mAP score significantly(though ideally it's supposed to).
@pjspillai
What validation dataset do you use?
- Is it the same as training dataset?
- Or is it combined PascalVOC valid dataset + MS COCO valid dataset?
Why did you set
batch=16 subdivisions=8
instead of, for example,batch=64 subdivisions=32
?
Hi, I have been training yolo3 on the COCO+Pascal dataset (on a subset of 6 classes, out of the 80). I tried on two different experiment setups: a ) GTX 1070 b) Two GTX 1080 Ti's Both the experiments show a drop in the overall mAP score as well as the individual Pr Recall Scores for the 5 classes
I changed the yolov3.cfg file with the new filters at the appropriate places. Any idea on why this might be occurring?