Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →
Top 14 Python Tflite Projects
-
-
TensorFlowTTS
:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
PINTO_model_zoo
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
-
tensorflow-yolov4-tflite
YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
-
optimum
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
-
AnimeGANv3
Use AnimeGANv3 to make your own animation works, including turning photos or videos into anime.
-
yolov4-custom-functions
A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
-
-
tflite2tensorflow
Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support. Supports inverse quantization of INT8 quantization model.
-
tensorflow-lite-YOLOv3
YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
-
mousehunter-edge
Cat with prey detection on Raspberry Pi. Lock cat pet flap if prey is detected. Object detection implemented in TFLite with ImageNet v1 SSD. Inference on EdgeTPU (Google Coral USB). Stores images on AWS S3 and sends notifications to iOS device.
-
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data
Hey HN, has anyone found a viable solution for doing this locally and offline on iOS? I'd like to offer a privacy-friendly text to speech feature to my App, and Apple's speech synthesis sounds awful compared to some newer models and TTS engines. The only thing I've found is an older TensorflowTTS example here: https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/ios
Any pointers or tips appreciated.
Project mention: FastEmbed: Fast and Lightweight Embedding Generation for Text | dev.to | 2024-02-02Shout out to Huggingface's Optimum – which made it easier to quantize models.
Project mention: I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros | news.ycombinator.com | 2024-01-07Conceptually, to the best of my understanding, nothing too serious; perhaps the inefficiency of processing a larger input than necessary?
Practically, a few things:
If you want to have your cake & eat it too, they recommend Enumerated Shapes[1] in their coremltools docs, where CoreML precompiles up to 128 (!) variants of input shapes, but again this is fairly limiting (1 tok, 2 tok, 3 tok... up to 128 token prompts.. maybe you enforce a minimum, say 80 tokens to account for a system prompt, so up to 200 tokens, but... still pretty short). But this is only compatible with CPU inference, so that reduces its appeal.
It seems like its current state was designed for text embedding models, where you normalize input length by chunking (often 128 or 256 tokens) and operate on the chunks — and indeed, that’s the only text-based CoreML model that Apple ships today, a Bert embedding model tuned for Q&A[2], not an LLM.
You could used a fixed input length that’s fairly large; I haven’t experimented with it once I grasped the memory requirements, but from what I gather from HuggingFace’s announcement blog post[3], it seems that is what they do with swift-transformers & their CoreML conversions, handling the details for you[4][5]. I haven’t carefully investigated the implementation, but I’m curious to learn more!
You can be sure that no one is more aware of all this than Apple — they published "Deploying Transformers on the Apple Neural Engine" in June 2022[6]. I look forward to seeing what they cook up for developers at WWDC this year!
---
[1] "Use `EnumeratedShapes` for best performance. During compilation the model can be optimized on the device for the finite set of input shapes. You can provide up to 128 different shapes." https://apple.github.io/coremltools/docs-guides/source/flexi...
[2] BertSQUAD.mlmodel (fp16) https://developer.apple.com/machine-learning/models/#text
[3] https://huggingface.co/blog/swift-coreml-llm#optimization
[4] `use_fixed_shapes` "Retrieve the max sequence length from the model configuration, or use a hardcoded value (currently 128). This can be subclassed to support custom lengths." https://github.com/huggingface/exporters/pull/37/files#diff-...
[5] `use_flexible_shapes` "When True, inputs are allowed to use sequence lengths of `1` up to `maxSequenceLength`. Unfortunately, this currently prevents the model from running on GPU or the Neural Engine. We default to `False`, but this can be overridden in custom configurations." https://github.com/huggingface/exporters/pull/37/files#diff-...
[6] https://machinelearning.apple.com/research/neural-engine-tra...
Project mention: Introducing Nobuco: PyTorch to Tensorflow converter. Intuitive, flexible, efficient. | /r/pytorch | 2023-07-02Hence, [Nobuco](https://github.com/AlexanderLutsenko/nobuco). It's designed with simplicity and hackability in mind and automatically solves mismatching channel orders offering near-optimal performance. Try it, and spread the word if you like it. If not, feel free to open an issue or feature request. Any forms of contribution are highly welcome!
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Tflite related posts
-
10$ Full Body Tracking! I'm proud to release ToucanTrack (in Beta!). Get decent FBT with the power of 2 PS3 Eye Cameras and AI!
-
How can i count Enter and Exit in Yolov Object Detection?
-
How do I enable Zoom virtual background without green screen in Linux?
-
Object tracking on Android
-
Tensorflow Yolo Implementation
-
Contagem de motocicletas no evento do Bozo em SP. Total: 6253.
-
Contagem em tempo real das motos durante a motocada promovida pelo presidente Bolsonaro (contagem final: 6253)
-
A note from our sponsor - InfluxDB
www.influxdata.com | 10 May 2024
Index
What are some of the best open-source Tflite projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | yolov5 | 47,202 |
| 2 | TensorFlowTTS | 3,710 |
| 3 | PINTO_model_zoo | 3,314 |
| 4 | tensorflow-yolov4-tflite | 2,225 |
| 5 | optimum | 2,174 |
| 6 | AnimeGANv3 | 1,583 |
| 7 | yolov4-custom-functions | 596 |
| 8 | exporters | 537 |
| 9 | tflite2tensorflow | 249 |
| 10 | nobuco | 192 |
| 11 | tensorflow-lite-YOLOv3 | 107 |
| 12 | mousehunter-edge | 65 |
| 13 | efficientnet-lite-keras | 38 |
| 14 | ftc_ladel | 2 |
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com