PINTO_model_zoo
edgetpu
| PINTO_model_zoo | edgetpu | |
|---|---|---|
| 5 | 34 | |
| 3,301 | 397 | |
| - | 3.8% | |
| 9.7 | 2.7 | |
| 3 days ago | over 2 years ago | |
| Python | C++ | |
| MIT License | Apache License 2.0 |
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.
PINTO_model_zoo
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YOLOv7 object detection in Ruby in 10 minutes
Download the ONNX model from this project: 307_YOLOv7
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stereodemo: compare several recent stereo depth estimation methods in the wild
Hope it might be useful to more people, and thanks to PINTO0309 and ibaiGorordo for converting several pre-trained models to ONNX!
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Loading Saved Models for transfer learning
Check it out https://github.com/PINTO0309/PINTO_model_zoo
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[R][P]MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis
Someone reported, that he converted MobileStyleGAN to tfjs (https://github.com/PINTO0309/PINTO_model_zoo), but i didn't check it
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Can we increase the output class in transfer learning?
model:-https://github.com/PINTO0309/PINTO_model_zoo/blob/main/053_BlazePose/01_float32/02_pose_landmark_upper_body_tflite2h5_weight_int_fullint_float16_quant.py
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?
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 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!
scrypted - Scrypted is a high performance home video integration and automation platform
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
frigate - NVR with realtime local object detection for IP cameras
tensorflow-onnx - Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
yolov7_d2 - 🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
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
CREStereo - Official MegEngine implementation of CREStereo(CVPR 2022 Oral).
homebridge-wyze-connected-home-op - Wyze Connected Home plugin for Homebridge with support for the Wyze Outdoor Plug