Comments (5)
BichenWuUCB
commented on June 24, 2024
1
@cedricve Thanks for your interest. As a matter of fact, though SqueezeDet's speed definitely satisfies normal video's frame rate, but since it takes each frame as an independent input, in our previous experiments, we observed some discontinuity in prediction results between neighboring frames. We are currently working on improving SqueezeDet's performance on videos. So, stay tuned!
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cedricve
commented on June 24, 2024
Ok looks like video related functionality and GTK is not supported.
- video related functionality is not supported (not compiled with FFmpeg)
- for example cv.imshow() will not work (not compiled with GTK+ 2.x or Carbon support)
https://pypi.python.org/pypi/opencv-python
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cedricve
commented on June 24, 2024
ended up with compiling opencv from source and using the .so library directly.
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cedricve
commented on June 24, 2024
@BichenWuUCB cool, I'll use your classifier in the @kerberos-io project (www.kerberos.io). If you need some data, let me know..
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acmaheri
commented on June 24, 2024
@BichenWuUCB are you already done on improving SqueezeDet's performance on videos?
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Related Issues (20)
- Gpu occupancy rate
- where is base_model_config.py?
- Will random initialization parameters have no precision? HOT 1
- Fine-tune SqueezeDet from sparse labels
- How to do hard negative mining HOT 1
- Publish frozen model? HOT 3
- Problem converting to TFLite HOT 3
- low GPU usage
- 8-bit weights
- Deploying squeezeDet on mobile HOT 3
- How to convert checkpoint of squeezedet to frozen graph for tflite conversion?! HOT 1
- Image resolution problem
- How to run demo.py using train.py checkpoint model HOT 1
- Train with different size and Inference with different size.
- Fine tuning with the model
- Train error and Eval error
- Using negative samples for training.
- print weights per layer during training
- Performance issue in src/eval.py (by P3) HOT 1
- The loss plateaus after 100 Epoch
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