Comments (2)
When I changed "--node_dim" and "node_in_dim" back to the original 2048, the model ran normally.
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I adjusted the FASTREID section, changing the dimension of the output feature vectors to 256, and retrained the REID network. This allowed the model to run smoothly.
from sushi.
Related Issues (20)
- support single object tracking? HOT 1
- Unable to compute embeddings HOT 3
- RuntimeError: CUDA out of memory. Tried to allocate 121.04 GiB (GPU 0; 47.54 GiB total capacity; 2.57 GiB already allocated; 13.33 GiB free; 32.50 GiB reserved in total by PyTorch) HOT 4
- There are 4 levels in paper while 9 levels in code. HOT 1
- Weight sharing between levels HOT 1
- bdd100k HOT 1
- Zero_nodes option HOT 1
- Release 4 level SUSHI trained models HOT 1
- How can I reproduce the detection results HOT 1
- Online version HOT 1
- Cuda out of Memory HOT 2
- Oline or Offline HOT 1
- Choose specific CUDA for training or inference HOT 1
- frame-by-frame GNN
- list index out of range HOT 14
- Pretrained model weights for 7 level / 128 frames config HOT 1
- Training Command Correction HOT 2
- What is my pretrained model? HOT 1
- threshold detections? HOT 1
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