>>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> ...
>>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> ...
Taming Transformers for High-Resolution Image Synthesis Patrick Esser*,Robin Rombach*,Björn Ommer * equal contribution tl;drWe combine the efficiancy of convolutional approaches with the expressivity of transformers by introducing a convolutional VQGAN, which learns a codebook of context-rich visual ...
MRA 模型由 Zhanpeng Zeng、Sourav Pal、Jeffery Kline、Glenn M Fung 和 Vikas Singh 在Multi Resolution Analysis (MRA) for Approximate Self-Attention中提出。 ApacheCN_飞龙 2024/06/26 1730 Transformers 4.37 中文文档(六十三) 配置索引sizetorch模型 XLM-RoBERTa-XL 模型是由 Naman Goyal、Jingfei Du、Myle...
Taming Transformers for High-Resolution Image Synthesis Patrick Esser*,Robin Rombach*,Björn Ommer * equal contribution tl;drWe combine the efficiancy of convolutional approaches with the expressivity of transformers by introducing a convolutional VQGAN, which learns a codebook of context-rich visual ...
Sogang University 2LG Innotek Abstract While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for sin- gle image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolu...
DeiT-b 384: Fine tuned model for larger resolution of 384x384 DeiT: Uses distillation process In the image below, we can assess the efficacy of hard distillation, as the accuracy reaches nearly 83%, a level unattainable through soft distillation. Additionally, the distillation tokens brings sl...
def visualize_instance_seg_mask(mask): # Initialize image with zeros with the image resolution # of the segmentation mask and 3 channels image = np.zeros((mask.shape[0], mask.shape[1], 3)) # Create labels labels = np.unique(mask) label2color = { label: ( random.randint(0, 255),...
>>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> ...
The image data had a resolution of 256 × 256 in Portable Network Graphics (PNG) format, as lossless compression was used. The preprocessing and NN training were implemented in Python 3.8 and TensorFlow 2.5. Figure 5. Flowchart of frequency range analysis procedure. 2.4. NN Training In this...