Let's start by installing 🤗 Transformers.
We'll perform inference on the familiar cat and dog image.
Next, we load the raw model CVT checkpoint from the hub. See the model hub to look for fine-tuned versions on a task that interests you.
We'll define some texts which the model can detect. We can prepare the image and the texts for the model using the processor:
Next we perform a forward pass. As we're at inference time, we use the torch.no_grad() operator to save memory (we don't need to compute any gradients).
The get_cvt_base function, decorated with @cache, is our key player. This function uses snapshot_download to fetch the specific backbone.
The use of @cache is a clever optimization; it ensures that once the model and processor are loaded, they are stored in memory. This significantly speeds up future calls to this function, as it avoids reloading the model and processor from scratch each time, making it ideal for deployments.
modelbitYou can test your REST Endpoint by sending single or batch production images to it for inference.
Use the requests package to POST a request to the API and use json to format the response to print nicely:
⚠️ Replace the ENTER_WORKSPACE_NAME placeholder with your workspace name.
You can also test your endpoint from the command line using:
⚠️ Replace the ENTER_WORKSPACE_NAME placeholder with your workspace name.
CvT incorporates a unique architecture that merges the local processing capabilities of convolutions with the dynamic attention mechanisms of transformers. This hybrid approach allows CvT to efficiently process images while retaining contextual information over varying scales.
The model's architecture is designed to capitalize on the strengths of both CNNs and ViTs, providing a robust framework for handling image classification tasks with improved accuracy and lower computational costs.
Developed by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang, the CvT model was introduced to address the limitations inherent in pure transformer models for vision tasks. By integrating convolutional elements, CvT achieves superior performance metrics on benchmark datasets like ImageNet, setting new standards for image classification and analysis.
CvT's architecture features a novel convolutional token embedding mechanism and a convolutional transformer block. These components work in tandem to enhance the model's ability to capture local spatial contexts while maintaining the global receptive field provided by transformers. The architecture supports hierarchical representation learning, enabling efficient processing of images at different resolutions.
CvT has demonstrated exceptional performance in various vision-based tasks, including image classification, object detection, and semantic segmentation. Its versatility and efficiency make it suitable for applications ranging from autonomous driving to medical image analysis.
The primary strength of CvT lies in its ability to combine the local processing advantages of CNNs with the global context awareness of transformers. This leads to superior performance on image recognition tasks, outperforming traditional CNNs and ViTs in terms of accuracy and efficiency.
While CvT offers numerous advantages, its performance can be contingent on the availability of large-scale datasets for training. Additionally, the integration of convolutions into the transformer architecture might introduce complexity, potentially requiring more resources for model training and fine-tuning.
CvT employs supervised learning, utilizing a blend of convolutional operations and self-attention mechanisms. This hybrid approach allows for effective feature extraction and representation learning, making CvT a powerful tool for tackling complex vision tasks.
