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Releases: roboflow/inference

v0.62.2

26 Nov 11:37
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💪 Added

🔧 Fixed

🏡 Maintenance

Full Changelog: v0.62.0...v0.62.2

v0.62.0

19 Nov 22:48
12882e3

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💪 Added

♾️ Segment Anything 3 🤝 inference

sam-3-build-an-app.mp4

We are happy to share that newest Meta AI model - Segment Anything 3, just got added to inference 🔥

Staring from now, you can use the model directly in inference or explore various Roboflow Platform features powered by SAM3 - plenty of options are on the table :

  • use the model to power your Workflow
  • support your labelling efforts (or even offload the task to the model completely)
  • build your model within few minutes with Roboflow Rapid

Our Blog 📖 provides further reading and is an excellent source of inspiration. You can also visit our docs 📝 providing instructions on how to use the model in the code.

PRs contributing to the release: #1729, #1731, #1732, #1704, #1734, #1736, #1737, #1738, #1739, #1740, #1741

Full Changelog: v0.61.0...v0.62.0

v0.61.0

17 Nov 18:41
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💪 Added

  • Api key passthrough/email - with this change @yeldarby enabled our users to send emails from workflows using Roboflow's email relay - change by @yeldarby in #1685
  • WebRTC data decoupling - video channel and data channel are now created independently, making it possible to process video without streaming preview back to preserve bandwidth - change by @lrosemberg in #1700
  • Dinov3 classifier by @mkaic in #1705

🔧 Fixed

🏡 Maintenance

New Contributors

Full Changelog: v0.60.0...v0.61.0

v0.60.0

07 Nov 19:28
ed0f5a6

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What's Changed

Full Changelog: v0.59.1...v0.60.0

v0.59.1

31 Oct 23:32
2928dd1

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💪 Added

📺 hosted webrtc

Thanks to @grzegorz-roboflow we have a great news 🔥

With this release of inference it is now possible to easily process video streams over webrtc with inference!

We prepared example script to showcase how to process streams from your python scripts. Start inference server, and then stream web-cam in order to start processing the stream and to see live preview produced by inference:

python examples/webrtc/webrtc_worker.py \
    --workspace-id="<your workspace>" \
    --workflow-id="<your workflow id>" \
    --api-key="<your API key>" \
    --inference-server-url="http://127.0.0.1:9001" \
    --realtime

Locally stored files can also be streamed and processed:

python examples/webrtc/webrtc_worker.py \
    --workspace-id="<your workspace>" \
    --workflow-id="<your workflow id>" \
    --api-key="<your API key>" \
    --inference-server-url="http://127.0.0.1:9001" \
    --source "/path/to/file.mp4" \
    --realtime

The processing can be performed on Modal simply export WEBRTC_MODAL_TOKEN_ID and WEBRTC_MODAL_TOKEN_SECRET with your credentials, and inference takes care of creating Modal app and processing stream on Modal function. All aspects of Modal execution are controlled through environmental variables. On-demand stream processing on CPU or GPU is extremely easy with this feature!

The feature is also available through our app https://app.roboflow.com:

image

Check out details #1612

Other new features

🔧 Fixed

🏡 Maintenance

🥼 inference-experimental

Full Changelog: v0.59.0...v0.59.1

v0.59.0

24 Oct 20:56
a92e202

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💪 Added

🍾 Workflows Execution Engine v1.7.0

This release refines how Workflow step errors are reported when interacting with the Roboflow platform. Client-side issues such as invalid API keys or model IDs now raise ClientCausedStepExecutionError with accurate HTTP status codes (400–404) instead of a generic StepExecutionError with HTTP 500.

Motivation

The change ensures that client misconfigurations are clearly distinguished from server-side errors, improving observability and making debugging easier.

Compatibility

Most users should not be affected. However, if an integration relies on the legacy error type or status code, it can be temporarily restored by setting:

DEFAULT_WORKFLOWS_STEP_ERROR_HANDLER=legacy

This update brings more accurate error reporting with minimal impact on existing workflows. See details of the change in the Workflows change log and in PR #1640

🥼 Changes inference-experimental

Full Changelog: v0.58.3...v0.59.0

v0.58.3

21 Oct 19:31
6ad4910

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What's Changed

New Contributors

Full Changelog: v0.58.2...v0.58.3

v0.58.2

13 Oct 14:06
cd700d0

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🔧 Fixed

  • Honor mask decode args for rf-detr seg model by @probicheaux in #1613
  • Fix image preprocessing block to accept dynamic parameter references by @rf-elliot in #1616

🏡 Maintenance

🏅 New Contributors

Full Changelog: v0.58.1...v0.58.2

v0.58.1

03 Oct 09:23
16961e2

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🔧 Fixed

Warning

Potential OOM errors in inference server as a result of bug introduced in v0.55.0

We've detected a bug that was in inference since 0.55.0 release - setting confidence to 0.0 was historically prevented globally in inference server. Since we wanted to remove that constraint, in 0.55.0 release, we make it possible to set such confidence threshold, but that turned out to lead to OOM issues with some models - namely instance segmentation ones, which in some cases may yield large amount of segmentation masks using wast amount of RAM. To provide quick-fix, we've added CONFIDENCE_LOWER_BOUND_OOM_PREVENTION env variable flag to control the minimal allowed confidence, set by default to 0.01.

Review the change here: #1611

🏡 Maintenance

Full Changelog: v0.58.0...v0.58.1

v0.58.0

02 Oct 10:57
e9a9402

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💪 Added

🤯 RFDetr Seg — Fast, Accurate Segmentation in Roboflow 💥

image
We’re excited to announce the addition of RF-DETR Seg (Preview), bringing real-time instance segmentation to the Roboflow platform. Thanks to the outstanding work of the RF research team, users can now:
  • Train and deploy RF-DETR Seg models directly in Roboflow
  • Enjoy state-of-the-art accuracy with latency
  • Run models seamlessly through Roboflow inference or the rf-detr packages

This release pushes the boundary of fast, accurate segmentation — and it’s now available to explore in Roboflow. Check out our 📖 blog for more details and try out your RFDetr model in inference:

import os
import supervision as sv
from inference import get_model
from PIL import Image
from io import BytesIO
import requests

url = "https://media.roboflow.com/dog.jpeg"
image = Image.open(BytesIO(requests.get(url).content))

model = get_model("<your-rf-model-id>")
predictions = model.infer(image)[0]
detections = sv.Detections.from_inference(predictions)

🔌 Improved

  • improve speed and memory usage of masks2poly and masks2multipoly by @hansent in #1586

Full Changelog: v0.57.4...v0.58.0