CoreML-Models
gluon-cv
CoreML-Models | gluon-cv | |
---|---|---|
1 | 1 | |
1,148 | 5,751 | |
- | 0.9% | |
6.3 | 1.8 | |
2 months ago | over 1 year ago | |
Python | ||
- | Apache License 2.0 |
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CoreML-Models
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Mochi Diffusion - generate images using Apple's Stable Diffusion using Core ML (open source)
This is a link of existing CoreML models (found in the Freescaler 2 readme) https://github.com/john-rocky/CoreML-Models
gluon-cv
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FOSS self-hosted image-to-text gpu accelerated object recognition ? Is there anything on the table yet ?
https://github.com/amusi/awesome-object-detection https://mmdetection.readthedocs.io/en/latest/index.html https://github.com/thtrieu/darkflow https://github.com/OlafenwaMoses/ImageAI https://github.com/dmlc/gluon-cv https://github.com/aim-uofa/AdelaiDet/ https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md https://github.com/wizyoung/YOLOv3_TensorFlow
What are some alternatives?
MochiDiffusion - Run Stable Diffusion on Mac natively
Video-Dataset-Loading-Pytorch - Generic PyTorch dataset implementation to load and augment VIDEOS for deep learning training loops.
swift-coreml-diffusers - Swift app demonstrating Core ML Stable Diffusion
photo2cartoon - 人像卡通化探索项目 (photo-to-cartoon translation project)
UniFormer - [ICLR2022] official implementation of UniFormer
darkflow - Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
FreeScaler-CoreML - Upscaler for macOS
VolleyVision - Applying Deep Learning Approaches to Volleyball Data
hagrid - HAnd Gesture Recognition Image Dataset
PeekingDuck - A modular framework built to simplify Computer Vision inference workloads.
AgML - AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
YOLOv3_TensorFlow - Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.