Average Model Cost: $0.0000
Number of Runs: 5,538
Models by this creator
SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. 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. 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. You can use the raw model for semantic segmentation. See the model hub to look for fine-tuned versions on a task that interests you. Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: For more code examples, we refer to the documentation.
Model description SN-GAN implementation with PyTorch-Lightning to generate Documents. Generated samples Project repository: DocuGAN. Usage You can see the tool to generate document on HuggingFace by trying the space demo. Training data For training, I used the invoices subpart of RVL-CDIP dataset. Find the full dataset here