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Nothasan

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image classification
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This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the PETA dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.2170 - Accuracy: 0.9107 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).

Image classification
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We have developed the first hybrid model for computer vision which leverages the strengths of Mamba and Transformers. Specifically, our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. In addition, we conducted a comprehensive ablation study on the feasibility of integrating Vision Transformers (ViT) with Mamba. Our results demonstrate that equipping the Mamba architecture with several self-attention blocks at the final layers greatly improves the modeling capacity to capture long-range spatial dependencies. Based on our findings, we introduce a family of MambaVision models with a hierarchical architecture to meet various design criteria. MambaVision demonstrates a strong performance by achieving a new SOTA Pareto-front in terms of Top-1 accuracy and throughput. For each model, we offer two variants for image classification and feature extraction that can be imported with 1 line of code.

Image classification
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mit-b0

NVIDIA

SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.

Image classification
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