Comments (2)
ZhouQianang
commented on May 25, 2024
Hello? This is the first 2000 steps of my training loss. Is this decline rate normal? I am using gmflow_scale1_train.sh with batch_size=8, and my dataset is a sparse flow dataset.
step: 000100 epe: 32.581
step: 000200 epe: 17.243
step: 000300 epe: 12.278
step: 000400 epe: 10.447
step: 000500 epe: 9.192
step: 000600 epe: 7.654
step: 000700 epe: 6.763
step: 000800 epe: 6.227
step: 000900 epe: 5.693
step: 001000 epe: 5.022
step: 001100 epe: 4.580
step: 001200 epe: 4.256
step: 001300 epe: 3.908
step: 001400 epe: 3.654
step: 001500 epe: 3.544
step: 001600 epe: 3.247
step: 001700 epe: 3.109
step: 001800 epe: 2.928
step: 001900 epe: 2.851
step: 002000 epe: 2.692
from unimatch.
haofeixu
commented on May 25, 2024
Hi, this looks reasonable, you can check the flow visualization results as well.
from unimatch.
Related Issues (20)
- FEATURE REQUEST: Support 'depth' task using two images with different Intrinsics. HOT 5
- About weights HOT 2
- is this method suitable for panoramas which is about up and down?? HOT 1
- nice work! and plan to live camera gui？ HOT 2
- Resetting hidden state during refinement HOT 2
- Running in Windows? HOT 1
- Why the value of optical flow is opposite? HOT 1
- gmstereo_scale1 vs gmstereo_scale2 Inference speed HOT 1
- Decoding RGB Optical Flow for Motion Information Extraction in Video Action Recognition HOT 3
- What is the best resolution for inferencing optical flow? HOT 4
- The optical flow between two identical images looks like random noise. Is this normal? HOT 2
- Request for new weights HOT 1
- gmstereo_scale1_train.sh HOT 1
- The problem of optical flow prediction HOT 1
- unimatch.py line 257 HOT 1
- [GMflow] What do the ’train_dataset ‘ equation coefficients mean when stage=sintel？ HOT 1
- Question about model and model_without_ddp HOT 2
- Question on upsampling during training. HOT 4
- tartan air dataset HOT 5
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