v0.9.11

🚀 Added

YOLO World in the inference

Have you heard about YOLO World model? 🤔 If not - you would probably be interested to learn something about it! Our blog post 📰 may be a good starting point❗

Great news is that YOLO World is already integrated with inference. Model is capable to perform zero-shot detections of classes specified in inference parameter. Thanks to that, you may start making videos like that just now 🚀

yellow-filling-output-1280x720.mp4

Simply install dependencies.

pip install inference-sdk inference-cli

Start the server

inference server start

And run inference against our HTTP server:

from inference_sdk import InferenceHTTPClient

client = InferenceHTTPClient(api_url="http://127.0.0.1:9001")
result = client.infer_from_yolo_world(
    inference_input=YOUR_IMAGE,
    class_names=["dog", "cat"],
)

Active Learning 🤝 workflows

Active Learning data collection made simple with workflows 🔥 Now, with just a little bit of configuration you can start data collection to improve your model over time. Just take look how easy it is:

active_learning_in_workflows.mp4

Key features:

• works for all models supported at Roboflow platform, including the ones from Roboflow Universe - making it trivial to use off-the-shelf model during project kick-off stage to collect dataset while serving meaningful predictions
• combines well with multiple workflows blocks - including DetectionsConsensus - making it possible to sample based on predictions of models ensemble 💥
• Active Learning block may use project-level config of Active Learning or define Active Learning strategy directly in the block definition (refer to Active Learning documentation 📖 for details on how to configure data collection)

See documentation 📖 of new ActiveLearningDataCollector to find detailed info.

🌱 Changed

InferencePipeline now works with all models supported at Roboflow platform 🎆

For a long time - InferencePipeline worked only with object-detection models. This is no longer the case - from now on, other type of models supported at Roboflow platform (including stubs - like my-project/0) work under InferencePipeline. No changes are required in existing code. Just put model_id of your model and the pipeline should work. Sinks suited for detection-only models were adjusted to ignore non-compliant formats of predictions and produce warnings notifying about incompatibility.

🔨 Fixed

• Bug in yolact model in #266

🏆 Contributors

@paulguerrie (Paul Guerrie), @probicheaux (Peter Robicheaux), @PawelPeczek-Roboflow (Paweł Pęczek)

Full Changelog: v0.9.10...v0.9.11