Posts tagged Computer Vision

Panoptic segmentation and instance segmentation with Detectron2 on AMD GPUs

This blog gives an overview of Detectron2 and the inference of segmentation pipelines in its core library on an AMD GPU.

Read more ...


Unlocking Vision-Text Dual-Encoding: Multi-GPU Training of a CLIP-Like Model

In this blog, we will build a vision-text dual encoder model akin to CLIP and fine-tune it with the COCO dataset on AMD GPU with ROCm. This work is inspired by the principles of CLIP and the Hugging Face example. The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their descriptions into the same embedding space, such that the text embeddings are located near the embeddings of the images they describe. The objective during training is to maximize the similarity between the embeddings of image and text pairs in the batch while minimizing the similarity of embeddings for incorrect pairs. The model achieves this by learning a multimodal embedding space. A symmetric cross entropy loss is optimized over these similarity scores.

Read more ...


Speech-to-Text on an AMD GPU with Whisper

Whisper is an advanced automatic speech recognition (ASR) system, developed by OpenAI. It employs a straightforward encoder-decoder Transformer architecture where incoming audio is divided into 30-second segments and subsequently fed into the encoder. The decoder can be prompted with special tokens to guide the model to perform tasks such as language identification, transcription, and translation.

Read more ...


Interacting with Contrastive Language-Image Pre-Training (CLIP) model on AMD GPU

Contrastive Language-Image Pre-Training (CLIP) is a multimodal deep learning model that bridges vision and natural language. It was introduced in the paper “Learning Transferrable Visual Models from Natural Language Supervision” (2021) from OpenAI, and it was trained contrastively on a huge amount (400 million) of web scraped data of image-caption pairs (one of the first models to do this).

Read more ...


ResNet for image classification using AMD GPUs

In this blog, we demonstrate training a simple ResNet model for image classification on AMD GPUs using ROCm on the CIFAR10 dataset. Training a ResNet model on AMD GPUs is simple, requiring no additional work beyond installing ROCm and appropriate PyTorch libraries.

Read more ...


Total body segmentation using MONAI Deploy on an AMD GPU

Medical Open Network for Artificial Intelligence (MONAI) is an open-source organization that provides PyTorch implementation of state-of-the-art medical imaging models, ranging from classification and segmentation to image generation. Catering to the needs of researchers, clinicians, and fellow domain contributors, MONAI’s lifecycle provides three different end-to-end workflow tools: MONAI Core, MONAI Label, and MONAI Deploy.

Read more ...


Image classification using Vision Transformer with AMD GPUs

The Vision Transformer (ViT) model was first proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ViT is an attractive alternative to conventional Convolutional Neural Network (CNN) models due to its excellent scalability and adaptability in the field of computer vision. On the other hand, ViT can be more expensive compared to CNN for large input images as it has quadratic computation complexity with respect to input size.

Read more ...


Building semantic search with SentenceTransformers on AMD

In this blog, we explain how to train a SentenceTransformers model on the Sentence Compression dataset to perform semantic search. We use the BERT base model (uncased) as the base transformer and apply Hugging Face PyTorch libraries.

Read more ...