![]() We set up Autodistill and CVevals, compared the performance of three different prompts on a dataset of 20 images, then used the best prompt to label the rest of our dataset. In this guide, we have demonstrated how to use CVevals to evaluate prompts for use with Autodistill. For more information on how to train a target model, check out the target model section of our Autodistill guide, or refer to the Autodistill documentation. We can use the labeled images to train a target model. a prompt may be "rubbish" and the label could be "trash"). ![]() While the prompt and label are the same, these would be different if another prompt were more effective (i.e. “trash” is passed to the base model, and all containers are labeled as “trash”. In this code, we map the prompt “trash” to “trash”. ))īase_model.label("./context_images", extension=".jpeg") Let's edit the evals list in our examples/grounding_dino_compare.py file: evals = [ Here are the prompts we’ll test in this example: To set prompts, open up the examples/grounding_dino_compare.py file (or whatever example file from which you are working) and replace the prompts in the `evals` list with a list of prompts you want to test.Īll of the comparison scripts that work with Autodistill have a standard API so the code in this section is applicable no matter the base model with which you are working. Step 3: Evaluate Base Model PromptsĪll of the CVevals scripts accept Roboflow datasets as an input. We are now ready to compare different prompts for use with Autodistill. When you have annotated your images, click “Generate” in the sidebar of the Roboflow platform and create a version with all of your annotated images: When approving your images for inclusion in a dataset, make sure you add all images to your Training Set: Using Roboflow Annotate, create annotations on your dataset: Then, upload ~10-20 images that are representative of the images you want to label with Autodistill. The predictions from the target model are compared against the ground truth data to figure out how accurate the target model was at annotating images in your dataset.īefore we can evaluate prompts for our shipping container model, we need some ground truth data. To evaluate prompts, we need both predictions from our target model – in this case, Grounded SAM, which uses Grounding DINO – and ground truth data from annotations we have made. We have prepared a dataset of shipping containers for use with training the model. In this guide, we are going to use Autodistill to build a model that identifies shipping containers. Run the following commands in the root cvevals folder: git clone We'll also need to install Grounding DINO for this example. In this guide, we’ll use the examples/grounding_dino_compare.py evaluator for use with Autodistill. All starter scripts are in the examples/ directory. Next, we need to install CVevals, the framework we will use for evaluating different prompts for use with Autodistill: git clone ĬVevals is a standalone utility bundled with a set of starter scripts for use with comparing prompts. We’ll distill knowledge from Grounded SAM into a smaller model.įirst, we need to install Autodistill and the relevant base model package, autodistill-grounded-sam: pip install autodistill autodistill-grounded-sam In this guide, we’re going to use Grounded SAM, a combination of Grounding DINO and the Segment Anything Model (SAM), as our base model with Autodistill. Without further ado, let’s get started! Step 1: Install Autodistill and CVevals In this guide, we are going to show how to use the open source CV evals framework to evaluate prompts for use with Autodistill. You don’t want to label a thousand images with Autodistill and find your prompt didn’t label your data accurately. ![]() To tell Autodistill how to label images in your project, you need to specify a prompt that will instruct the base model on what to annotate.īut what prompt will work best for your use case? How do you know if you have chosen the right prompt? These are key questions, especially if you plan to label hundreds or thousands of images for use with your model. These smaller models are more suitable for edge deployment, offering greater performance in terms of inference time and compute constraints.Īutodistill takes in a folder of images relevant to your project, automatically labels them using a large, general model (called a “base model”), and uses those images to train a target model. Segment Anything (SAM)) into smaller models (i.e. Autodistill is an open-source ecosystem of tools for distilling the knowledge from large, general computer vision models (i.e. ![]()
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