SreeEswaran / Image-Captioning-Transformer Star 1 Code Issues Pull requests This project demonstrates an image captioning model using a Transformer architecture. The model takes an image as input and generates a descriptive caption. We use the COCO dataset for training and evaluation. model transf...
pythontensorflowimagecaptioning UpdatedApr 21, 2019 Here are all my code files of Advanced AI/ML architectures built from scratch using Pytorch. machine-learningdeep-learningcnnpytorchartificial-intelligencetransformerlstmganrnnresnetgooglenetimagecaptioningneural-style-transferefficientnet ...
On ResNet-200, we achieve a top-1 accuracy of 81.4%, which is a 0.8% improvement over the state-of-the-art cross-entropy loss using the same architecture (which represents a significant advance for ImageNet). We also compared cross-entropy and SupCon on a Transformer-based ViT-B/16 ...
kaggle上的数据集(实际用于CNN分类和分割)是通过对激活图应用阈值获得的。原始代码是tf1,但可以使用tf2/kers和pytorch实现。此外,还检查了可以解释弗吉尼亚州和DeepSolar tracker采用太阳能的因素的可视化和in-depth分析:使用open-source数据对深度learning-based分布式PV映射的准确性进行无监督评估hyperion_solar_net->在...
Hi, Kaggle community, I recently published a new notebook on Image captioning Here, I used a pre-trained VGG16 network to extract features. On top of that, to reduce the feature dimensionality, I used the Net-VLAD (Network Vector of Locally Aggregated Descriptors), a soft clustering techniqu...
Explore and run machine learning code with Kaggle Notebooks | Using data from [Private Datasource]
(missing: https://www.kaggle.com/static/assets/2334.c9718d31060352fbeaec.js) at r.f.j (https://www.kaggle.com/static/assets/runtime.js?v=59a05b2445c4ad6d2ecf:1:10219) at https://www.kaggle.com/static/assets/runtime.js?v=59a05b2445c4ad6d2ecf:1:1295 at Array.reduce (<...
Concurrently, in the Vision Transformer domain, models included ViT-B/32, ViT-B/16, and ViT-L/14. The choice for all the experiments was the ViT-B/32 architecture. The decision to utilize this architecture in the CLIP framework stemmed from careful consideration of various factors such as ...
Image captioning is a popular topic in the domains of computer vision and natural language processing (NLP). Recent advancements in deep learning (DL) models have enabled the improvement of the overall performance of the image captioning approach. This s
#single image, captioningAZFUSE_TSV_USE_FUSE=1 python -m generativeimage2text.inference -p"{'type': 'test_git_inference_single_image',\'image_path': 'aux_data/images/1.jpg',\'model_name': 'GIT_BASE',\'prefix': '',\}"#single image, question answeringAZFUSE_TSV_USE_FUSE=1 pytho...