* Update Dockerfile `FROM pytorch/pytorch:latest`
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* isort
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Setting `master_port` to 1 may cause `Permission denied` due to failure to bind the port. So it is better to set it to a port greater than 1024.
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* Update streams.txt default
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* Change streams list extension to .streams
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* Missed one
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* Update dataloaders.py
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* Update README.md
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* Added a tutorial notebook for classification.
* Split a cell so that there is less room for error when a user pastes their own code snippet. Also added an active learning section at the end.
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* Updated link to colab
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* New global TQDM_BAR_FORMAT
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* update coco128-seg comments
* Enables detect.py to use Triton for inference
Triton Inference Server is an open source inference serving software
that streamlines AI inferencing.
https://github.com/triton-inference-server/server
The user can now provide a "--triton-url" argument to detect.py to use
a local or remote Triton server for inference.
For e.g., http://localhost:8000 will use http over port 8000
and grpc://localhost:8001 will use grpc over port 8001.
Note, it is not necessary to specify a weights file to use Triton.
A Triton container can be created by first exporting the Yolov5 model
to a Triton supported runtime. Onnx, Torchscript, TensorRT are
supported by both Triton and the export.py script.
The exported model can then be containerized via the OctoML CLI.
See https://github.com/octoml/octo-cli#getting-started for a guide.
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* Deploy category
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* Update detect.py
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* Update predict.py
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* Update triton.py
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* fixed triton model query over grpc
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* Fix likely remote URL bug
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* Fix 2x download attempt on http://path/to/model.pt
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* Standardize warnings
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Update ClassificationModel default training imgsz=224
To match classify/val.py and classify/predict.py
Helps https://github.com/ultralytics/yolov5/issues/9462
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* allow image dirs
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* Update predict.py
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