yolov7_d2
edgetpu
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| yolov7_d2 | edgetpu | |
|---|---|---|
| 4 | 34 | |
| 3,129 | 397 | |
| - | 3.8% | |
| 0.0 | 2.7 | |
| 5 months ago | over 2 years ago | |
| Python | C++ | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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yolov7_d2
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YOLOv7: Trainable Bag-of-Freebies
Especially hilarious considering some other people ALSO jumped on the "we made an object detector so let's call it YOLOvX" wagon and released...
Something called YOLOv7.
https://github.com/jinfagang/yolov7
- YOLOv7: YOLO with Transformers and Instance Segmentation
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How to Train YOLOv6 on a Custom Dataset
You're 9 months late https://github.com/jinfagang/yolov7
- YOLOv6: Redefine state-of-the-art for object detection
edgetpu
- The Pixel 8 Pro's Tensor G3 off-loads all generative AI tasks to the cloud
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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.
https://github.com/google-coral/edgetpu/issues/668
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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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Another PCIe A+E card in place of wifi in M900 tiny
I'm looking at the coral.ai cards and they have a M.2 A+E card, same form factor as the wifi slot in the m900 tiny. Has anyone tried another card in that slot other than wifi?
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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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Any ML accelarator chips?
By no means an expert, but I have seen prototypes using a raspberry pi and a dongle from Coral Ai. They have PCIE and USB based modules.
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Is Google coral getting abandoned
Last news on https://coral.ai/ was on May 5 2022
Activity on the github project seems to have stopped. https://github.com/google-coral
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Ask HN: Worth it to buy 4x Nvidia Tesla K40 for AI?
https://coral.ai/ -
How do you effectively test accuracy of your software product?
Your problem statement still needs more clarification. If the above applies, the best way is to evaluate your ML-based pattern matcher on high-level scenarios. One approach to speed up the evaluation is to lift and shift the execution of scenarios into cloud. Another approach is to use an AI accelerator, such as http://coral.ai or other.
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Cluster AIs - low cost (lower performance) super/minicomputing
You probably could but not with raspis. Maybe the TPUs they sell. https://coral.ai/
What are some alternatives?
yolov3 - YOLOv3 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
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
scrypted - Scrypted is a high performance home video integration and automation platform
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
frigate - NVR with realtime local object detection for IP cameras
BCNet - Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
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.
yolact - A simple, fully convolutional model for real-time instance segmentation.
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
CATNet - 🛰️ Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images (TNNLS 2023)
homebridge-wyze-connected-home-op - Wyze Connected Home plugin for Homebridge with support for the Wyze Outdoor Plug