Let's start by installing 🤗 Transformers.
We'll perform inference on the familiar cat and dog image.
Next, we load an OWLv2 checkpoint from the hub. Note that the authors released several, with various patch sizes, and training schemes (self-trained only, self-trained + fine-tuned, and an ensemble). We'll load an ensemble checkpoint here since it performs best. Note that the authors also did release larger checkpoints, which have even better performance.
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).
Finally let's plot the results! Important to note here is that the authors visualize the bounding boxes on the preprocessed (padded + resized) image, rather than the original one. Hence here we'll take the pixel_values created by the processor and "unnormalize" them. This gives us the preprocessed image, minus normalization.
As can be seen, the preprocessed image is an image that pads the original one to a square.
The "get_owlv2_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.
OWLv2 f zero-shot object detection, a process that eliminates the need for manually annotated bounding boxes, thereby making the detection process more efficient and less tedious. The model is built upon the foundations of its predecessor, OWL-ViT v1, and utilizes a transformer-based architecture. What distinguishes OWLv2 is its capability to leverage self-training on a web-scale dataset, using pseudo labels generated by an existing detector to enhance performance significantly.
By building upon the OWL-ViT framework and employing self-training techniques, OWLv2 has set new benchmarks in the performance of zero-shot object detection, demonstrating the model's scalability and efficiency in handling web-scale datasets.
OWLv2's versatility makes it applicable across a wide range of industries, from retail and e-commerce to safety, security, telecommunications, and transportation. Its ability to accurately detect objects without prior labeled data makes it a powerful tool for developing innovative solutions in various sectors.
One of the primary strengths of OWLv2 is its exceptional performance in zero-shot object detection, significantly reducing the need for labor-intensive manual annotations. Moreover, the model's self-training capability allows it to scale to web-sized datasets, further enhancing its utility and application potential.
While OWLv2 represents a significant advancement, the reliance on large-scale datasets and the complexity of its transformer-based architecture may pose challenges in terms of computational resources and the expertise required for customization and optimization.
OWLv2 employs a zero-shot learning approach, utilizing self-training techniques that leverage existing detectors to generate pseudo-box annotations on image-text pairs. This method enables the model to improve its detection capabilities through exposure to vast amounts of unannotated data, thereby broadening its applicability and performance in real-world scenarios.
