edgetpu
YOLOv6
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edgetpu | YOLOv6 | |
---|---|---|
34 | 11 | |
382 | 5,498 | |
0.0% | 1.5% | |
2.7 | 6.7 | |
over 2 years ago | 16 days ago | |
C++ | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
edgetpu
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Chromebook Plus: more performance and AI capabilities
I know the tensor power pixelbook was shutdown and I never heard the actual reason just a bunch of speculation about costs/profitability which is probably true.
It's a shame that there isn't more competition and development in the neural asic world to harness the power of llms/generative AI on a low power, cheap hardware platform like the pixelbook line. For someone that invented the TPU they have done a not so great job of ensuring it's commercialization and support. Both on the hardware and software side.
The coral edge tpu seemed to be the right high level idea but without proper execution.
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Show HN: RISC-V core written in 600 lines of C89
> even in the 80s I wanted an FPGA accelerators in every machine
Mostly unrelated, but I recently discovered that you can buy TPUs, right now, as a consumer product, from https://coral.ai.
The stock firmware already allows you to run these things so hard they overheat, which is amazing.
But yes, I also want FPGA accelerators.
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Sony backs Raspberry Pi with fresh funding, access to A.I. chips
Chips optimized to perform the type of calculations used for NN inference at high parallelism. A good example would be the google spinoff https://coral.ai/ (though their usecase is highly limited by sub-par software constraints)
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How can I speed up predictions [RPI, TFLite]?
https://coral.ai, and it looks really neat, but the USB accelerator has a wait time of 81 weeks and the PCIe modules have a wait time of around 14-50 weeks. A wait time that long isn't really an option, are there any alternatives?
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YOLOv6: Redefine state-of-the-art for object detection
Is this available for https://coral.ai/ somehow? Would it be difficult to convert it?
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ZMEventNotification Failure when running install.sh
if [ "${INSTALL_CORAL_EDGETPU}" == "yes" ] then # Coral files #echo #echo "Installing pycoral libs, if needed..." #${PY_SUDO} apt-get install libedgetpu1-std -qq #${PY_SUDO} ${INSTALLER} install python3-pycoral -qq echo 'Checking for Google Coral Edge TPU data files...' targets=('coco_indexed.names' 'ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite' 'ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite' 'ssd_mobilenet_v2_face_quant_postprocess_edgetpu.tflite') sources=('https://dl.google.com/coral/canned_models/coco_labels.txt' 'https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite' 'https://github.com/google-coral/test_data/raw/master/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite' 'https://github.com/google-coral/test_data/raw/master/ssd_mobilenet_v2_face_quant_postprocess_edgetpu.tflite') for ((i=0;i<${#targets[@]};++i)) do if [ ! -f "${TARGET_DATA}/models/coral_edgetpu/${targets[i]}" ] then ${WGET} "${sources[i]}" -O"${TARGET_DATA}/models/coral_edgetpu/${targets[i]}" else echo "${targets[i]} exists, no need to download" fi done fi
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NUC 10 BXNUC10i7FNH Core i7: how to expand it?
I am a happy owner of a NUC 10 Core i7! so far it has been amazing! now I need run frigate and to optimize this I need to add Google Coral TPU
- Adding PCIe "Bifurcation" to an old Dell R720XD
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Building a rackmount server in the EU
FYI the dual edge coral TPU m.2 module needs a very unique pic-e interface as theyβre each brought to a single PCI-e lane but in a single interface (so itβs 2x1X PCIe lanes not a 1x2X)β¦ subtle difference but very few m.2 e interfaces support it (read about it on the github page)
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[Request/suggestions] DIY mini PC / low power consumption
Also, the 720/920 tinys also have a m.2 e key slot for wifi; I'm intending to try out a https://coral.ai/products/m2-accelerator-dual-edgetpu/ accelerator in this slot for frigate once the card actually becomes available.
YOLOv6
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I want to make a Class monitoring system. is it possible in the conditions I'm in ??
Some resources to get you started...https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606https://github.com/OlafenwaMoses/ImageAIhttps://towardsdatascience.com/yolo-object-detection-with-opencv-and-python-21e50ac599e9https://github.com/meituan/YOLOv6
- [P] Any object detection library
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DeepSort with PyTorch(support yolo series)
meituan/YOLOv6
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YOLOv6: Redefine state-of-the-art for object detection
https://github.com/meituan/YOLOv6/blob/main/docs/About_namin...
> P.S. We are contacting the authors of YOLO series about the naming of YOLOv6.
You should ask _before_ publishing, not _after_.
They claim it runs faster and is more accurate than YOLOv5, yet requires 3x as much computation (GFLOPs)? Something doesn't add up here.
There is unbelievably little information about the architecture too. Unfortunately it's not in a format I can easily throw the cfg in as visualize it: https://gitlab.com/danbarry16/darknet-visual
This appears to be on purpose to advertise DagsHub: https://dagshub.com/pricing
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[D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
Saved you the time: https://github.com/meituan/YOLOv6
What are some alternatives?
yolov5 - YOLOv5 π in PyTorch > ONNX > CoreML > TFLite
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
scrypted - Scrypted is a high performance home video integration and automation platform
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
frigate - NVR with realtime local object detection for IP cameras
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
yolov7_d2 - π₯π₯π₯π₯ (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! π₯π₯π₯
keras-yolo3 - Training and Detecting Objects with YOLO3
Dual-Edge-TPU-Adapter - Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot
homebridge-wyze-connected-home-op - Wyze Connected Home plugin for Homebridge with support for the Wyze Outdoor Plug