পদ্ধতির তুলনা করুন

	সেগমেন্ট এনিথিং মডেল	DETR (ডিটেকশন ট্রান্সফরমার)	ভিশন ট্রান্সফরমার
ক্ষেত্র	গভীর শিখন	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2023	2020	2021
প্রবর্তক	Alexander Kirillov	Nicolas Carion	Dosovitskiy, A. et al.
ধরন	Neural network architecture	Neural network architecture	Transformer architecture for images (self-attention over patches)
মৌলিক উৎস	Kirillov, A., Mintun, E., Darrell, T., & Girshick, R. (2023). Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026).	Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European Conference on Computer Vision (pp. 213-229). Springer, Cham.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
অপর নাম	SAM, Segment Anything	Detection Transformer, DETR	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
সম্পর্কিত	4	4	5
সারসংক্ষেপ	Segment Anything Model (SAM) is a foundation model introduced by Kirillov et al. in 2023 that can segment any object in an image given various forms of prompts. SAM is trained on a massive dataset of diverse images and learns to segment objects based on minimal user input such as points, boxes, or text descriptions.	DETR (Detection Transformer) is an end-to-end framework for object detection introduced by Carion et al. in 2020 that reformulates detection as a direct set prediction problem using transformers. Unlike traditional approaches that use hand-crafted post-processing like non-maximum suppression, DETR treats object detection as a sequence-to-sequence problem where the transformer predicts all objects at once.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).