Merge pull request #10397 from cvat-ai/release-2.61.0

Release v2.61.0

Author: cvat-bot[bot]

CHANGELOG.md

@@ -16,6 +16,29 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 <!-- scriv-insert-here -->
 
+<a id='changelog-2.61.0'></a>
+## \[2.61.0\] - 2026-03-20
+
+### Added
+
+- "Snap to point" feature for polygon and polyline editing. While enabled,
+  points being moved or drawn will automatically snap to nearby points from other shapes
+  (<https://github.com/cvat-ai/cvat/pull/10353>)
+
+- Join tool now supports polygon shapes, allowing to merge multiple
+  polygons into a single unified polygon.
+  (<https://github.com/cvat-ai/cvat/pull/10362>)
+
+### Changed
+
+- Minor visual improvements on the controls sidebar
+  (<https://github.com/cvat-ai/cvat/pull/10374>)
+
+### Fixed
+
+- Helm modifiable permissionFix paths and command
+  (<https://github.com/cvat-ai/cvat/pull/7932>)
+
 <a id='changelog-2.60.0'></a>
 ## \[2.60.0\] - 2026-03-17
 

ai-models/agents_deployment/sam2/.env

@@ -1,12 +1,14 @@
 # Docker compose
 CVAT_BASE_URL=https://app.cvat.ai # Your CVAT instance URL. Default is https://app.cvat.ai
+FUNCTION_NAME=your_function_name # Name of the function to create in CVAT.
 CVAT_ACCESS_TOKEN=your_cvat_token # Required to authenticate to CVAT API. You can get it in CVAT UI: https://app.cvat.ai/profile#security
 IMAGE_URL= your_repo:your_image_tag # Please specify your image url with tag. For example: myrepo/sam2:latest
 AGENTS_COUNT=1  # SAM2 does not support multiple agents (yet?), so this value should be set to 1
 ORG_SLUG="" # Please specify your org slug if you want agent to deployed for organization
 USE_CUDA=false # Set to true if you want to use GPU. Please ensure that you use right docker image.
 
 # Model config params for SAM2
-# Supported params are: model_id
-# For more model_ids refer to https://huggingface.co/models?search=facebook%2Fsam2
+# Supported params are: model_id=str:model_id
+# model_id - model_id from https://huggingface.co/models?search=facebook%2Fsam2
+# MODEL_CONFIG_PARAMS="-p model_id=str:facebook/sam2.1-hiera-tiny"
 MODEL_CONFIG_PARAMS="-p model_id=str:facebook/sam2.1-hiera-tiny"

ai-models/agents_deployment/sam2/check_env.sh

@@ -51,6 +51,15 @@ resolve_cuda() {
     fi
 }
 
+resolve_function_name() {
+    if [ -z "$FUNCTION_NAME" ]; then
+        echo "Warning: FUNCTION_NAME environment variable not found. Default is SAM2"
+        FUNCTION_NAME="SAM2"
+    else
+        echo "Using FUNCTION_NAME: $FUNCTION_NAME"
+    fi
+}
+
 common_env() {
     validate_access_token
     resolve_base_url

ai-models/agents_deployment/sam2/docker-compose.yaml

@@ -10,6 +10,7 @@ services:
       CVAT_ACCESS_TOKEN:
       MODEL_CONFIG_PARAMS:
       ORG_SLUG:
+      FUNCTION_NAME:
 
   cvat-agent:
     image: ${IMAGE_URL}

ai-models/agents_deployment/sam2/function_registration.sh

@@ -6,10 +6,8 @@ source "$(dirname "$0")/check_env.sh"
 
 common_env
 resolve_model_params
+resolve_function_name
 
-# Hardcoded for SAM2
-
-FUNCTION_NAME="SAM2"
 FUNCTION_FILE_PATH="func.py"
 
 

ai-models/agents_deployment/templates/docker-compose/.env

@@ -0,0 +1,8 @@
+# Docker compose template file.
+CVAT_BASE_URL=your_cvat_url # Your CVAT instance URL. Default is https://app.cvat.ai
+CVAT_ACCESS_TOKEN=your_cvat_access_token_here # Required to authenticate to CVAT API. You can get it in CVAT UI: https://app.cvat.ai/profile#security
+MODEL_ID=your_model_id  # Model ID/name.
+IMAGE_URL= your_cvat_image_here # Image to use for the agent.
+AGENTS_COUNT=1  # Number of agents to deploy.
+ORG_SLUG=your_organization_slug_here # If you want to create agent for your organization.
+USE_CUDA=false # Set to true if you want to use GPU. Please ensure that you use right docker image.

ai-models/agents_deployment/templates/docker-compose/Dockerfile

@@ -0,0 +1,48 @@
+# We are aiming at using some thin image so that we can install only the necessary dependencies for our agent. Python 3.14-slim is a good choice for that.
+FROM python:3.14-slim
+
+# Install necessary tools, feel free to add more if needed.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    git \
+    jq \
+    && rm -rf /var/lib/apt/lists/*
+
+# These args are used to be able to build both GPU and CPU only images. If USE_GPU is set to true, we will install the GPU version of PyTorch, otherwise we will install the CPU only version.
+# Note that GPU support is only available for amd64 architecture, so we will check the TARGETARCH arg as well.
+ARG USE_GPU=false
+ARG TARGETARCH
+
+# We would like to be able to deploy our image to k8s as well with Helm so we need kubectl.
+ADD --chmod=+x https://dl.k8s.io/release/v1.34.0/bin/linux/${TARGETARCH}/kubectl /usr/local/bin/
+
+# Running as non root is good for security.
+RUN groupadd --gid 1000 agent && \
+    useradd --uid 1000 --gid 1000 --create-home agent
+
+# Required to share data in compose.
+RUN mkdir /shared && chown agent:agent /shared
+
+WORKDIR /app
+
+# Replace this with your relative path to the requirements.txt file. This file should contain all the dependencies for your agent.
+COPY path_to_your_model/requirements.txt .
+
+# Assuming that your model uses pytorch this condition is responsible for building CPU only or GPU images.
+RUN if [ "$USE_GPU" = "true" ] && [ "$TARGETARCH" = "amd64" ]; then \
+        echo "Building GPU image" && pip install --no-cache-dir cvat-cli -r requirements.txt; \
+    else \
+        echo "Building CPU only image" && pip install --no-cache-dir cvat-cli -r requirements.txt --extra-index-url=https://download.pytorch.org/whl/cpu; \
+    fi
+
+# Replace this with your relative path to the model files.
+# Assuming that you are using same names for the scripts.
+COPY path_to_your_model/ .
+COPY --chmod=755 path_to_your_model/function_registration.sh .
+COPY --chmod=755 path_to_your_model/function_deregistration.sh .
+COPY --chmod=755 path_to_your_model/check_env.sh .
+COPY --chmod=755 path_to_your_model/entrypoint.sh .
+
+USER agent
+
+ENTRYPOINT ["./entrypoint.sh"]

ai-models/agents_deployment/templates/docker-compose/README.md

@@ -0,0 +1,43 @@
+# This is an example Dockerfile template and docker compose for deploying your agent in CVAT.
+
+
+## The general Dockerfile approach is:
+
+1. Select base image to use with your agent.
+2. Install necessary packages with a package manager.
+For example YOLO needs libxcb1, libgl1, libglib2.0-0
+3. Install cvat-cli, and if you need any Python dependencies, you may put them to requirements.txt
+4. You should have `func.py` file that acts as adapter between your agent and CVAT.
+You can refer to existing adapters for YOLO, SAM2 or transformers for inspiration. (`/ai-models/detector/yolo/func.py`,
+`/ai-models/tracker/sam2/func.py`, `/ai-models/transformers/func.py`)
+5. Consider supporting some build args like `$USE_GPU` to allow users to choose between CPU and GPU
+   versions of the image.
+For the models that we've implemented as agents we rely on `pytorch` CPU or GPU wheels.
+6. Your image should implement the following functionality
+   - Register function that will be powered by your agent in CVAT.
+   - Run agent that will be polling CVAT for new tasks (Entrypoint).
+   - Deregister function in CVAT when it is not needed anymore.
+
+All three capabilities are powered by cvat-cli that authenticates in CVAT with provided API key and allows to
+register/deregister functions and run agents.
+Please refer to scripts [`entrypoint.sh`](./entrypoint.sh), [`function_registration.sh`](./function_registration.sh)
+and [`function_deregistration.sh`](./function_deregistration.sh) for examples of how to use cvat-cli to implement
+these capabilities.
+
+### General recommendations
+- Run your container with a non-root user.
+- Use lightweight base images to reduce the size of your image and speed up deployment.
+- Keep the frequently changing layers of your Docker closer to the end of your Dockerfile to take advantage of caching.
+- Always pin the versions of your dependencies.
+
+## Docker compose
+Once your image is built, use Docker Compose to manage the agent lifecycle.
+All services use the same image.
+
+1. It runs function registration script in the container (`cvat-function-register` service).
+2. `cvat-function-register` service creates a file on shared volume.
+3. `cvat-agent` service runs agents that connect to CVAT function that was created in step 1.
+4. When agent is not needed user should run `cvat-function-deregister` service manually to delete function from CVAT.
+   (docker compose run --rm cvat-function-deregister)
+
+It's easy to configure docker compose using [`.env`](./.env) file that should be located in the same folder as `docker-compose.yaml`

ai-models/agents_deployment/templates/docker-compose/check_env.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+
+# This file contains functions that are used to validate existence of important variables
+# and setting defaults in some cases.
+
+validate_access_token() {
+    if [ -z "$CVAT_ACCESS_TOKEN" ]; then
+        echo "Error: CVAT_ACCESS_TOKEN environment variable must be set."
+        exit 1
+    fi
+}
+
+resolve_base_url() {
+    if [ -z "$CVAT_BASE_URL" ]; then
+        echo "Warning: CVAT_BASE_URL environment variable is missing, using https://app.cvat.ai as default."
+        CVAT_BASE_URL="https://app.cvat.ai"
+    fi
+}
+
+resolve_org_slug() {
+    ORG_SLUG_ARGS=()
+    if [ -z "$ORG_SLUG" ]; then
+        echo "Warning: ORG_SLUG environment variable not found. Function will be registered only for your user."
+        echo "ORG_SLUG must be the short name of the organization; it is the name displayed under your username when you switch to the organization in the CVAT UI."
+        ORG_CURL_ARGS=(--data-urlencode "org=")
+    else
+        echo "Using organization: $ORG_SLUG"
+        ORG_SLUG_ARGS=(--organization "$ORG_SLUG")
+        ORG_CURL_ARGS=(--data-urlencode "org=$ORG_SLUG")
+    fi
+}
+
+# This example is for SAM2 function.
+# For your function it will be different.
+resolve_model_id() {
+    if [ -z "$MODEL_ID" ]; then
+        echo "Warning: MODEL_ID environment variable not found. Default is facebook/sam2.1-hiera-tiny"
+        MODEL_ID="facebook/sam2.1-hiera-tiny"
+    else
+        echo "Using MODEL_ID: $MODEL_ID"
+    fi
+}
+
+resolve_cuda() {
+    if [ "$USE_CUDA" = "true" ]; then
+        echo "Using CUDA! Please ensure that you are using proper image with CUDA support"
+        USE_CUDA_ARGS=(-p device=str:cuda)
+    else
+        echo "Info: USE_CUDA environment variable not found. Model will run on CPU."
+    fi
+}
+
+common_env() {
+    validate_access_token
+    resolve_base_url
+    resolve_org_slug
+}

ai-models/agents_deployment/templates/docker-compose/docker-compose.yaml

@@ -0,0 +1,45 @@
+services:
+  cvat-function-register:
+    image: ${IMAGE_URL}
+    entrypoint: ["/app/function_registration.sh"]
+    volumes:
+      - shared-data:/shared
+    restart: "on-failure"
+    environment:
+      CVAT_BASE_URL:
+      CVAT_ACCESS_TOKEN:
+      MODEL_ID:
+      ORG_SLUG:
+
+  cvat-agent:
+    image: ${IMAGE_URL}
+    volumes:
+      - shared-data:/shared:ro
+    depends_on:
+      cvat-function-register:
+        condition: service_completed_successfully
+    deploy:
+      replicas: ${AGENTS_COUNT:-1}
+    environment:
+      CVAT_BASE_URL:
+      CVAT_ACCESS_TOKEN:
+      MODEL_ID:
+      ORG_SLUG:
+
+  #NB! MANUAL RUN!
+  # docker compose run --rm cvat-function-deregister
+  # This command will run this service once and remove container after execution.
+  cvat-function-deregister:
+    image: ${IMAGE_URL}
+    entrypoint: ["/app/function_deregistration.sh"]
+    volumes:
+      - shared-data:/shared:ro
+    profiles:
+      - manual
+    environment:
+      CVAT_BASE_URL:
+      CVAT_ACCESS_TOKEN:
+      ORG_SLUG:
+
+volumes:
+  shared-data: