-
Notifications
You must be signed in to change notification settings - Fork 18
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
My results are lower than yours. #22
Comments
Sorry, this config is supposed to be run on 8 GPUs with |
Thanks, is there any config supposed to be run on one GPU? Or, what parameters should I changed in the config? |
Dear @yuan738, Thank you for your insightful question. Currently, the semi-supervised method is heavily dependent on a large batch size, and as a result, reducing the number of GPUs could significantly impact performance. Unfortunately, we have not yet found an effective solution to this issue. One potential workaround could be to implement "gradient accumulation," or you might consider using Best Regards, |
hi @Adamdad
it seems the mAP is too low. |
Dear @zimenglan-sysu-512 , I recommend increasing the ratio of labeled to unlabeled samples, such as a It's important to keep in mind that performance may still be lower with a reduced GPU setup. We are aware of this challenge and will make efforts to overcome it in our future endeavors. Best. |
hi @Adamdad
|
Dear @zimenglan-sysu-512, Yes, setting data.sampler.train.sample_ratio to
Best, |
hi @Adamdad |
We only has a file called |
thanks, another question, where to find |
Dear @zimenglan-sysu-512, Apologies for any confusion caused. I wanted to inform you that the file Best regards, |
hi @Adamdad
|
Dear @zimenglan-sysu-512,
If you have any further questions or need additional information, please let me know. Best regards, |
using 4 GPUs, and batch size is 6 in which sample_ratio =
|
Dear @zimenglan-sysu-512 Great 😃 |
|
hi @Adamdad |
dear @zimenglan-sysu-512 |
dear @zimenglan-sysu-512 |
When I train the model with consistent_teacher_r50_fpn_coco_180k_10p_2x8.py on one GPU, the result is too low. And I didn't change the parameters.
`[>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 43.4 task/s, elapsed: 115s, ETA: 0s2023-05-20 11:33:56,083 - mmdet.ssod - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=1.21s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=17.84s).
Accumulating evaluation results...
DONE (t=5.17s).
2023-05-20 11:34:22,052 - mmdet.ssod - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.123
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.197
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.126
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.067
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.142
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.156
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.146
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.359
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.496
[>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 43.1 task/s, elapsed: 116s, ETA: 0s2023-05-20 11:36:22,808 - mmdet.ssod - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=1.24s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=15.87s).
Accumulating evaluation results...
DONE (t=6.67s).
2023-05-20 11:36:48,355 - mmdet.ssod - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.098
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.165
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.099
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.051
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.113
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.125
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.305
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.305
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.305
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.131
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.315
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.446
2023-05-20 11:36:48,895 - mmdet.ssod - INFO - Exp name: consistent_teacher_r50_fpn_coco_180k_10p_2x8.py
2023-05-20 11:36:48,898 - mmdet.ssod - INFO - Iter(val) [180000] teacher.bbox_mAP: 0.1230, teacher.bbox_mAP_50: 0.1971, teacher.bbox_mAP_75: 0.1262, teacher.bbox_mAP_s: 0.0674, teacher.bbox_mAP_m: 0.1415, teacher.bbox_mAP_l: 0.1562, teacher.bbox_mAP_copypaste: 0.1230 0.1971 0.1262 0.0674 0.1415 0.1562, student.bbox_mAP: 0.0984, student.bbox_mAP_50: 0.1653, student.bbox_mAP_75: 0.0991, student.bbox_mAP_s: 0.0513, student.bbox_mAP_m: 0.1129, student.bbox_mAP_l: 0.1249, student.bbox_mAP_copypaste: 0.0984 0.1653 0.0991 0.0513 0.1129 0.1249
wandb: Waiting for W&B process to finish... (success).`
The text was updated successfully, but these errors were encountered: