CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock
yolov7
CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock | yolov7 | |
---|---|---|
2 | 33 | |
1 | 12,796 | |
- | - | |
4.9 | 3.2 | |
8 months ago | 20 days ago | |
Jupyter Notebook | Jupyter Notebook | |
- | GNU General Public License v3.0 only |
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.
CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock
yolov7
- FLaNK Stack Weekly 16 October 2023
-
Train a ML model able to identify animal species
If you want something off-the-shelf, try YoloV7.
-
A video based Latin dictionary: get what you see in Latin (beta) - What do you think?
The current dictionary is still in a beta state and has only been trained on 80 words (e.g. 'man', 'dog', 'car', 'keyboard', 'book', etc.; see list of words, see dataset). I used the object detection model Yolov7 (paper, all credits to them).
-
[D] Extracting the class labels and bounding boxes for objects, from a YOLO7 model after converting to an ONNX model
(Please note, this is a re-post of my original question here, I think this subreddit might be more appropriate for asking this question)At work, we use Unity, we have a project that needs object detection and classification. We decided to use this YOLO7 model (for non-technical reasons, It had to be the exact same model as the company does have pre-trained weights for this exact model). However, Unity only supports ONNX so I exported the model as an ONNX model, using the code provided in the repo:
- Coding Question Help
-
DL for the Web: Repository of Models
Github Projects offering pretrained weights and train / run scripts. Example
- [OC] Football Player 3D Pose Estimation using YOLOv7 and Matplotlib
-
Finding a good Tiny Yolo to train in Python
The only project I found is this one that implements Yolov7
-
Visualizing image augmentations from YOLOV7
I'm wondering if there's an efficient way to visualize the image augmentations from the Yolov7 hyperparameters list here
-
Train YOLOv8 ObjectDetection on Custom Dataset Tutorial
yolov7: https://github.com/WongKinYiu/yolov7#performance
What are some alternatives?
llm-awq - [MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
JsonGenius - Get structured JSON data from any page.
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
fastkafka - FastKafka is a powerful and easy-to-use Python library for building asynchronous web services that interact with Kafka topics. Built on top of Pydantic, AIOKafka and AsyncAPI, FastKafka simplifies the process of writing producers and consumers for Kafka topics.
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
deep-chat - Fully customizable AI chatbot component for your website
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
milvus-lite - A lightweight version of Milvus
darknet - Convolutional Neural Networks
inference - A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model