Comments (3)
@IAmSaad hello,could I know which jetpack you used in the TX2 ? And how you deploy the ENet on the TX2 ? We also tried to deploy the ENet on the TX2 but maybe there is something wrong. Thank you very much!
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We used the Jetpack 3.0 I guess. Try to find the use_cpu or use_gpu (I don't remember exactly what they were called) but the basic problem with our setup was that the these were not setup properly. We were running in the CPU mode before we found out about the switches. After that, we were getting like 10fps for 480*360 input.
from enet.
We used the Jetpack 3.0 I guess. Try to find the use_cpu or use_gpu (I don't remember exactly what they were called) but the basic problem with our setup was that the these were not setup properly. We were running in the CPU mode before we found out about the switches. After that, we were getting like 10fps for 480*360 input.
@IAmSaad
Can I ask that how did you run the scripts by using pre-trained model? I test the time that the code
"prediction = net.blobs['deconv6_0_0'].data[0].argmax(axis=0)" needs 2s while inference just uses 0.3s.
Can you tell me the script you used? Thank you!
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Related Issues (20)
- benchmark result on val HOT 2
- input image HOT 1
- Check failed: status == CUDNN_STATUS_SUCCESS (4 vs. 0) CUDNN_STATUS_INTERNAL_ERROR HOT 5
- OpenCV Error
- Dataset ratio to be used in Fine-Tuning own Dataset
- Test Speed
- BN-absorber-enet
- Do you train the model on CamVid Dataset? HOT 1
- No Module named caffe HOT 1
- Why do I get wrong predictions when I train the coding and decoding network directly?That is I havn't train encoder network in advance。
- Bad test
- why i can download your cityscapes_weights
- why i can download your cityscapes_weights
- object segmentation
- use ENet to train Pascal_voc, and the result wasn't convergency. HOT 4
- transfer learning HOT 1
- Your example ENet prediction looks wrong
- TypeError HOT 1
- Question about the params and FLOPs of ENet
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