models
tensorboard
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models | tensorboard | |
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96 | 11 | |
76,598 | 6,539 | |
0.2% | 0.9% | |
9.5 | 9.4 | |
3 days ago | 6 days ago | |
Python | TypeScript | |
GNU General Public License v3.0 or later | 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.
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 ๐ . 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, Iโm 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 Tensorflowโs 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.
tensorboard
- Tensorboard
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[D] Visualizing layer weights
Some form of 3D histograms? And then "discretized"/binned for each layer too. Apparently Tensorboard has them: https://github.com/tensorflow/tensorboard/blob/master/docs/r1/histograms.md
- I think I broke PIP
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[D] Unpopular Opinion: I hate the tensorboard Smoothing algorithm and always set the slider to 0.
Consider filing an issue? https://github.com/tensorflow/tensorboard/issues
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Parts of Tensorboard are being rewritten in Rust for a 100ร to 400ร speedup
The async code is in our server.rs and cli.rs, because this exposes a Tonic server and Tonic is all-in on async.
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[D] Comparison of experiment tracking tools
A quick google search is telling me that this is possible but very poorly documented / communicated: https://github.com/tensorflow/tensorboard/issues/767
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๐ ๐๐ป Making the Printed Links Clickable Using TensorFlow 2 Object Detection API
The cool part about TensorBoard is that we may run it directly in Google Colab. However, if you're running the notebook in your local installation of Jupyter you may also install it as Python package and launch it from the terminal.
What are some alternatives?
netron - Visualizer for neural network, deep learning and machine learning models
aim - Aim ๐ซ โ An easy-to-use & supercharged open-source experiment tracker.
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
wandb - ๐ฅ A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
onnx-tensorflow - Tensorflow Backend for ONNX
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.
redisai-examples - RedisAI showcase
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
rustboard - just-for-fun reimplementation of TensorBoard backend in Rust
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
guildai - Experiment tracking, ML developer tools