models
onnx-tensorflow
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models | onnx-tensorflow | |
---|---|---|
96 | 6 | |
76,598 | 1,234 | |
0.2% | 1.0% | |
9.5 | 0.0 | |
2 days ago | 30 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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models
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Changing box prediction head on SSD from TF2 model zoo
I am using SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50) from TF model zoo .
- Labeling question
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I'm looking for article for object detection explanation with working code
I spent some time looking for an article that explains object detection, but it seems that there are a lot of articles out there that are not very helpful. Some of these articles focus on specific things like mAP or UoI, but without the broader context, they are not very useful. The main issue with these articles is that they either don't provide any code, or they give examples that are not very helpful, like terminal commands to download a framework and train a model. I started from this link https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md, but it id not very useful. What I really need is a comprehensive explanation of how object detection works, along with working code that I can use to see the results for myself. I know that there are many different approaches to object localization, such as one-stage or two-stage detection, Faster R-CNN, or SSD, but I don't really care which approach will be described. I just need a starting point with clear explanations and working code that I can run.
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good computer vision or deep learning projects in github
TensorFlow Models (GitHub: https://github.com/tensorflow/models) is a collection of diverse TensorFlow-based ML and DL models for tasks like image classification, object detection, and text classification.
- [D] I just realised: GPT-4 with image input can interpret any computer screen, any userinterface and any combination of them.
- [D]Custom Trained Networks for EasyOCR
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Has anyone tried reverse engineering Google Tensor's AI-specific instruction set?
Assuming you're talking about leveraging the device's the device's Tensor Processing unit for machine learning then there then you're in luck because Google designed the TPU to work extremely well with the machine learning solutions developed by Google such as easy to use SDKs, robust runtimes and APIs ( e.g. - which you probably aren't going to need to touch). If you're a researcher there's plenty of lower level stuff floating about - but developers would be, again, better off staying away from it.
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Tensorflow for M1 macs with GPU support
Thank you so that worked and I was able to install it 😅. But when I try to run the test script as mentioned here, I get an error ModuleNotFoundError: No module named 'object_detection'. Am I doing something wrong, I’m using a conda environment and I have tensorflow-macos and tensorflow-metal plug-in installed in the same environment as tf-models.
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Object detection API deprecated
I've noticed while implementing tensorflow object detection API for a client that they have deprecated the repo and will not be updating it: https://github.com/tensorflow/models/tree/master/research/object_detection
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NVIDIA's Rip-Off - RTX 4070 Ti Review & Benchmarks
I implore you, download a model from Tensorflow’s model repo and try training it on your conventional GPU. See how much your memory bandwidth and memory count will severely bottleneck performance, in addition see how long it takes to get any decent results.
onnx-tensorflow
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How to Solve "BackendIsNotSupposedToImplementIt: Unsqueeze version 13 is not implemented."?
How to solve this? I found below github issue which they solved i think, but im not to able to find the solution https://github.com/onnx/onnx-tensorflow/pull/1022
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[D] Library to transfer PyTorch to TF
Okay, maybe it worked some years ago. The issue currently is that the trainable weights get lost...which is by design https://github.com/onnx/onnx-tensorflow/issues/1002
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Has anyone successfully converted an onnx model to tensorflow? Here's the problems I'm having...
TLDR: I'm using onnx-tf to convert an onnx model to tensorflow. During the conversion I lose important information such as inputs, outputs and the names of operators. Please read on if you have experience with this library or you've experienced similar issues. :)
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Portability of Rust in 2021
We had a few small issues with ONNX. Export worked but when running with e.g. tflite stumbled for example across this https://github.com/onnx/onnx-tensorflow/issues/853 Also the support for sampling from distributions is generally still pretty weak, but we were able to work around that.
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[D] How to reduce latency of DL models
https://pytorch.org/tutorials/advanced/super\_resolution\_with\_onnxruntime.html https://github.com/onnx/onnx-tensorflow
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Possible to retrain onnx model?
https://github.com/onnx/onnx-tensorflow Haven’t tried it, let me know if it works.
What are some alternatives?
netron - Visualizer for neural network, deep learning and machine learning models
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2 - This repository contains the script and process to create custom SSD Mobilenet model for object detection
redisai-examples - RedisAI showcase
tokenizers - 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
guesslang - Detect the programming language of a source code
tensorboard - TensorFlow's Visualization Toolkit
pytorch2keras - PyTorch to Keras model convertor
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x