Baseten’s Post

Quality of images from the FLUX image generation models look extremely promising. Extremely fast too. Will almost certainly replace the Stable Diffusion models in many image generation workflows, I think. https://lnkd.in/grXKRsju

Intuitive way of finding the gain without using small signal models .. but looking at short circuit current and output conductance ..

It's raining depth estimation models ☔️ DepthPro is a zero-shot depth estimation model by Apple 🍏 It's fast, sharp and accurate 🔥 Links are in the comments 💬 The model consists of two encoders: an encoder for patches and an image encoder. The outputs of both are merged to decode to depth maps and get the focal length. The model outperforms the previous state-of-the-art models in average of various benchmarks 👏

Knowing float point precision is essential in understanding how quantization works in ML and why you shouldn't quantize the model hastily. The more the precision the bigger the memory needed to store them and fine-tune them so reducing precision without losing information becomes challenging. There are various techniques for quantizing ML models, but understanding why you need quantization and how much information you lose when you go from FP32 to FP16 is essential. For example in this image below 7.567856 is represented as a signed 32-bit but when quantized to 16 bits the information available is just 7.566, meaning we lot the rest of the FP values. Doing this for billions of weights can lead to severe performance degradation.

How to Perform a Linear Mixed Effects Model (LMM) in R  https://lnkd.in/gQuCtFW2

Min Samples Leaf could affect your model performance. Do you know that? The tree-based model has become a go-to algorithm for many business use cases. We can set many hyperparameters to train the model, one of which is the Min Samples Leaf. We often experiment with the parameter, but have you already understood the parameter of the model? In my latest NBD Lite series, I will explain how Min Samples Leaf works and why it could affect the model complexity. Don't miss it! Read it all here👉👉👉 https://lnkd.in/gw9WttwV

🎉 FLUX.1: A few days ago two text-to-image models (dev, schnell) were released on Hugging Face from the FLUX.1 family. 💡 What are FLUX.1 models? The FLUX.1 family of models comes with three varieties following recent trend of model-releases from various companies. The FLUX.1 schnell is the cheapest (and smallest) in terms of cost. It is also permissively licensed with Apache 2.0 on Hugging Face model hub. The FLUX.1 models are basically transformers powered flow models. Quoting the official blog: "All public FLUX.1 models are based on a hybrid architecture of multimodal and parallel diffusion transformer blocks and scaled to 12B parameters. We improve over previous state-of-the-art diffusion models by building on flow matching, a general and conceptually simple method for training generative models, which includes diffusion as a special case. In addition, we increase model performance and improve hardware efficiency by incorporating rotary positional embeddings and parallel attention layers." Checkout the blog for more info. 🤗 HF Space for FLUX.1-schnell: https://lnkd.in/g_siD8BA ⚡️ Blog: https://lnkd.in/gipHxT7e 🦋 Model: https://lnkd.in/gMy98Dms #LLMs #VLMs #multimodal #ml #vision #flux

YOLOv11 was just released 2 days ago. I tested this major update for my lithofacies segmentation model. Compared to the previous YOLOv8m model, the YOLOv11m shows slight improvement from 0.384 to 0.428 mask mAP@50. The Precision is higher in YOLOv11m but Recall is higher in YOLOv8m. It was an early promising result. I'm proud to announce that YOLOv11 model has been added to Corel, so user can try it themselves by calling: download_model(select_model='yolo11-basic') Link to GitHub of Corel: https://lnkd.in/dGmj4pyv

From Sequential Generation to Diffusion: A New Era in Motion Planning Reading about the transition from traditional RNNs/LSTMs and Transformers to diffusion models in motion planning truly opened my eyes. Diffusion models, with their iterative denoising process, not only bring high-fidelity results but also solve the limitations of sequential dependency found in previous methods. Unlike step-by-step predictions, diffusion models generate the entire trajectory in a more flexible and efficient way. This shift represents a leap forward in handling complex, real-time tasks, like human motion planning. It’s fascinating to see how this method bridges data-driven imitation learning with real-time physical interaction. https://lnkd.in/esXD_PD7

Explore how to implement real-time object detection with YOLOv9 and OpenCV. Our guide covers running inference, webcam integration, and tracking your experiments. Read here: https://lnkd.in/eaurtiJq