yolov5
transformers
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yolov5 | transformers | |
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128 | 171 | |
46,026 | 122,577 | |
2.8% | 2.7% | |
8.9 | 10.0 | |
7 days ago | 7 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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yolov5
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Changing labels of default YOLOv5 model
I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?
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SS: Events triggered no matter the detection zone specified
What I did is hand off my detections to an Nvidia GPU running Yolov5 https://github.com/ultralytics/yolov5 which does the triggering if it's an object class I'm interested in. Since Synology is always caching at least 5 seconds of camera video the 50-100mS delay of grabbing a frame and analyzing before triggering recording is fine. Wish they'd implement something like this.
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AMD ROCm 5.5 In The Process Of Being Released
this is about all I could find, maybe you've been getting different degrees of optimized python libs that do the image resizes. https://github.com/ultralytics/yolov5/issues/11469
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good computer vision or deep learning projects in github
YOLOv5 (GitHub: https://github.com/ultralytics/yolov5) is a fast, accurate object detection model with code for training, testing, deployment, and pre-trained weights.
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[D] Extracting the class labels and bounding boxes for objects, from a YOLO7 model after converting to an ONNX model
Those dimensions suggest you need to apply (i.e. roll your own) non-max suppresion to the outputs: relevant link
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Thought Dump About Recent AI Advancements And Palantir
- YOLOv5 https://github.com/ultralytics/yolov5 (open source, so not Palantir's)
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How to build computer vision dataset labeling teamย in-house
A team of annotators and the infrastructure described in this article I needed to label my dataset, which was collected from cameras on the road (30k+ photos). This dataset was necessary to train an object detection model on six classes: [person, car, bus, bicycle, motorcycle, truck]. I released the dataset, created in this manner, as open source, and it can be downloaded here (link) together with trained YOLOv5s and YOLOv5x models from a popular repository (link) using this dataset. The license is simple: "Use it well"!
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Flutter Object Detection App + YOLOV5 Model.
Before you can use YOLOv5 in your Flutter application, you'll need to train the model on your specific dataset. You can use an existing dataset or create your own dataset to train the model. For this post I am using the pretrained model of yolov5 available on https://github.com/ultralytics/yolov5 as we are performing object detection we need to converts the pretrained model weights to torchscript format.
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NotImplementedError (YOLOv5)
Thank you Link to the codefor taking the time to reply. I have modified the code as you suggested. And now I see the GPU being utilized. But the precision, recall, mAP is all zero. At least it displays as zero.
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NVIDIA Jetson AGX Orin is now compatible with balena
Read more here about how Theia Scientific is currently using the newest NVIDIA Jetson AGX Orin on their fleet of microscopes. Theia Scientific and Volkov Labs, improved the Jetson AGX Orin inference speeds up to 30FPS. The Jetson AGX Orin running YOLOv5 tripled the Frames-per-Second (FPS) compared with the latest Jetson AGX Xavier.
transformers
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Gemma doesn't suck anymore โ 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
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Paris-Based Startup and OpenAI Competitor Mistral AI Valued at $2B
If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...
If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.
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Self train a super tiny model recommendations
You can train it with the code provided in transformer repo: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
transformers uses accelerate if you call it with device_map='auto'
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Show HN: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
Too much money being thrown around on BS in the LLM space, hardly any of it is going to places where it matters.
For example, the researchers working hard on better text sampling techniques, or on better constraint techniques (i.e. like this https://arxiv.org/abs/2306.03081), or on actual negative prompting/CFG in LLMs (i.e. like this https://github.com/huggingface/transformers/issues/24536) are doing far FAR more to advance the state of AI than dozens of VC backed LLM "prompt engineering" companies operating today.
HN, and the NLP community have some serious blindspots with knowing how to exploit their own technology. At least someone at Anderson Howartz got a clue and gave some funding to Oogabooga - still waiting for Automatic1111 to get any funding.
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๐๐ 23 issues to grow yourself as an exceptional open-source Python expert ๐งโ๐ป ๐ฅ
Repo : https://github.com/huggingface/transformers
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Whisper prompt tuning
From what I know, Whisper already supports prompting (https://github.com/huggingface/transformers/pull/22496). Can I somehow freeze the whole model and tune exclusively the prompt or would I need to write an implementation from scratch?
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A look at Appleโs new Transformer-powered predictive text model
https://github.com/huggingface/transformers/blob/0a55d9f7376...
To summarize how they work: you keep some number of previously generated tokens, and once you get logits that you want to sample a new token from, you find the logits for existing tokens and multiply them by a penalty, thus lowering the probability of the corresponding tokens.
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Can LLMs learn from a single example?
Very cool. This came up in a huggingface transformers issue a while ago and we also determined memorization to be the likely reason. It's nice to see someone else reach the same conclusion.
What are some alternatives?
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
OpenCV - Open Source Computer Vision Library
yolov5-crowdhuman - Head and Person detection using yolov5. Detection from crowd.
CenterNet - Object detection, 3D detection, and pose estimation using center point detection:
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
edge-tpu-tiny-yolo - Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.