add documentaiton config init

.github/workflows/docs.yml

@@ -0,0 +1,61 @@
+name: docs
+on: [push, pull_request]
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+
+jobs:
+  deploy:
+    environment:
+        name: documenation
+        url: ${{steps.deployement.outputs.page_url}}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Configure Git Credentials
+        run: |
+          git config user.name github-actions[bot]
+          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
+      - uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
+      - uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ env.cache_id }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+
+      - name: Install the latest version of uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+          enable-cache: true
+          cache-dependency-glob: "**/pyproject.toml"
+
+      - name: Check if cache used
+        if: steps.setup-uv.outputs.cache-hit == 'true'
+        run: echo "Cache was restored"
+
+      - run: uv sync --only-group docs
+
+      - run: uv run mkdocs build --config-file config/mkdocs.yml
+
+    #   - run: uv run mkdocs gh-deploy --force --config-file config/mkdocs.yml
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+            # Upload entire repository
+            path: 'site/'
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

.github/workflows/release_drafter.yml

@@ -0,0 +1,42 @@
+name: Release Drafter
+
+on:
+  push:
+    # branches to consider in the event; optional, defaults to all
+    branches:
+      - main
+      - dev
+  # pull_request event is required only for autolabeler
+  pull_request:
+    # Only following types are handled by the action, but one can default to all as well
+    types: [opened, reopened, synchronize]
+  # pull_request_target event is required for autolabeler to support PRs from forks
+  # pull_request_target:
+  #   types: [opened, reopened, synchronize]
+
+permissions:
+  contents: read
+
+jobs:
+  update_release_draft:
+    permissions:
+      # write permission is required to create a github release
+      contents: write
+      # write permission is required for autolabeler
+      # otherwise, read permission is required at least
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      # (Optional) GitHub Enterprise requires GHE_HOST variable set
+      #- name: Set GHE_HOST
+      #  run: |
+      #    echo "GHE_HOST=${GITHUB_SERVER_URL##https:\/\/}" >> $GITHUB_ENV
+
+      # Drafts your next Release notes as Pull Requests are merged into "master"
+      - uses: release-drafter/release-drafter@v6
+        # (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml
+        # with:
+        config-name: release_draft.yml
+        #   disable-autolabeler: true
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -141,7 +141,6 @@ __MACOSX
 # Extra directories/files
 /**data*/
 
-!tests/*/*
 agml/_helios
 agml/helios_config.sh
 agml/train

CODE_OF_CONDUCT.md

@@ -0,0 +1,74 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, gender identity and expression, level of experience,
+nationality, personal appearance, race, religion, or sexual identity and
+orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at [http://contributor-covenant.org/version/1/4][version]
+
+[homepage]: http://contributor-covenant.org
+[version]: http://contributor-covenant.org/version/1/4/

CONTRIBUTING.md

@@ -189,7 +189,12 @@ We will then review the changes and review next steps for adding the dataset int
 
 
 ### Installing uv
+<<<<<<< HEAD
+
+Install uv follow the guidelines in  https://docs.astral.sh/uv/getting-started/installation/, it is recommended to use the standalone installation.
+=======
 Dependencies and admin actions are done using `uv`. To Install uv follow the guidelines in  https://docs.astral.sh/uv/getting-started/installation/, it is recommended to use the standalone installation.
+>>>>>>> dev
 
 
 ### Building Project

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-<img src="/figures/agml-logo.png" alt="agml framework" width="400" height="400">
+<img src="assets/agml-logo.png" alt="agml framework" width="400" height="400">
 </p>
 
 ----
@@ -12,10 +12,10 @@ We're looking to hire a postdoc with both Python library development and ML expe
 
 ## Overview
 AgML is a comprehensive library for agricultural machine learning. Currently, AgML provides
-access to a wealth of public agricultural datasets for common agricultural deep learning tasks. In the future, AgML will provide ag-specific ML functionality related to data, training, and evaluation. Here's a conceptual diagram of the overall framework. 
+access to a wealth of public agricultural datasets for common agricultural deep learning tasks. In the future, AgML will provide ag-specific ML functionality related to data, training, and evaluation. Here's a conceptual diagram of the overall framework.
 
 <p align="center">
-<img src="/figures/agml-framework.png" alt="agml framework" width="350" height="291">
+<img src="assets/agml-framework.png" alt="agml framework" width="350" height="291">
 </p>
 
 AgML supports both the [TensorFlow](https://www.tensorflow.org/) and [PyTorch](https://pytorch.org/) machine learning frameworks.
@@ -45,7 +45,7 @@ loader = agml.data.AgMLDataLoader('apple_flower_segmentation')
 ```
 
 You can then use the in-built processing methods to get the loader ready for your training and evaluation pipelines. This includes, but
-is not limited to, batching data, shuffling data, splitting data into training, validation, and test sets, and applying transforms. 
+is not limited to, batching data, shuffling data, splitting data into training, validation, and test sets, and applying transforms.
 
 ```python
 import albumentations as A
@@ -125,35 +125,35 @@ model.run_training(loader)
 
 | Dataset | Task | Number of Images |
 | :--- | ---: |-----------------:|
-[bean_disease_uganda](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/bean_disease_uganda.md) | Image Classification |             1295 | 
-[carrot_weeds_germany](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/carrot_weeds_germany.md) | Semantic Segmentation |               60 | 
-[plant_seedlings_aarhus](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/plant_seedlings_aarhus.md) | Image Classification |             5539 | 
-[soybean_weed_uav_brazil](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/soybean_weed_uav_brazil.md) | Image Classification |            15336 | 
-[sugarcane_damage_usa](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/sugarcane_damage_usa.md) | Image Classification |              153 | 
-[crop_weeds_greece](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/crop_weeds_greece.md) | Image Classification |              508 | 
-[sugarbeet_weed_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/sugarbeet_weed_segmentation.md) | Semantic Segmentation |             1931 | 
-[rangeland_weeds_australia](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/rangeland_weeds_australia.md) | Image Classification |            17509 | 
-[fruit_detection_worldwide](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/fruit_detection_worldwide.md) | Object Detection |              565 | 
-[leaf_counting_denmark](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/leaf_counting_denmark.md) | Image Classification |             9372 | 
-[apple_detection_usa](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_detection_usa.md) | Object Detection |             2290 | 
-[mango_detection_australia](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/mango_detection_australia.md) | Object Detection |             1730 | 
-[apple_flower_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_flower_segmentation.md) | Semantic Segmentation |              148 | 
-[apple_segmentation_minnesota](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_segmentation_minnesota.md) | Semantic Segmentation |              670 | 
-[rice_seedling_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/rice_seedling_segmentation.md) | Semantic Segmentation |              224 | 
-[plant_village_classification](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/plant_village_classification.md) | Image Classification |            55448 | 
-[autonomous_greenhouse_regression](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/autonomous_greenhouse_regression.md) | Image Regression |              389 | 
-[grape_detection_syntheticday](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/grape_detection_syntheticday.md) | Object Detection |              448 | 
-[grape_detection_californiaday](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/grape_detection_californiaday.md) | Object Detection |              126 | 
-[grape_detection_californianight](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/grape_detection_californianight.md) | Object Detection |              150 | 
-[guava_disease_pakistan](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/guava_disease_pakistan.md) | Image Classification |              306 | 
-[apple_detection_spain](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_detection_spain.md) | Object Detection |              967 | 
-[apple_detection_drone_brazil](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_detection_drone_brazil.md) | Object Detection |              689 | 
-[plant_doc_classification](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/plant_doc_classification.md) | Image Classification |             2598 | 
-[plant_doc_detection](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/plant_doc_detection.md) | Object Detection |             2346 | 
-[wheat_head_counting](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/wheat_head_counting.md) | Object Detection |             6512 | 
-[peachpear_flower_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/peachpear_flower_segmentation.md) | Semantic Segmentation |               42 | 
-[red_grapes_and_leaves_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/red_grapes_and_leaves_segmentation.md) | Semantic Segmentation |              258 | 
-[white_grapes_and_leaves_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/white_grapes_and_leaves_segmentation.md) | Semantic Segmentation |              273 | 
+[bean_disease_uganda](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/bean_disease_uganda.md) | Image Classification |             1295 |
+[carrot_weeds_germany](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/carrot_weeds_germany.md) | Semantic Segmentation |               60 |
+[plant_seedlings_aarhus](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/plant_seedlings_aarhus.md) | Image Classification |             5539 |
+[soybean_weed_uav_brazil](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/soybean_weed_uav_brazil.md) | Image Classification |            15336 |
+[sugarcane_damage_usa](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/sugarcane_damage_usa.md) | Image Classification |              153 |
+[crop_weeds_greece](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/crop_weeds_greece.md) | Image Classification |              508 |
+[sugarbeet_weed_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/sugarbeet_weed_segmentation.md) | Semantic Segmentation |             1931 |
+[rangeland_weeds_australia](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/rangeland_weeds_australia.md) | Image Classification |            17509 |
+[fruit_detection_worldwide](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/fruit_detection_worldwide.md) | Object Detection |              565 |
+[leaf_counting_denmark](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/leaf_counting_denmark.md) | Image Classification |             9372 |
+[apple_detection_usa](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_detection_usa.md) | Object Detection |             2290 |
+[mango_detection_australia](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/mango_detection_australia.md) | Object Detection |             1730 |
+[apple_flower_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_flower_segmentation.md) | Semantic Segmentation |              148 |
+[apple_segmentation_minnesota](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_segmentation_minnesota.md) | Semantic Segmentation |              670 |
+[rice_seedling_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/rice_seedling_segmentation.md) | Semantic Segmentation |              224 |
+[plant_village_classification](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/plant_village_classification.md) | Image Classification |            55448 |
+[autonomous_greenhouse_regression](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/autonomous_greenhouse_regression.md) | Image Regression |              389 |
+[grape_detection_syntheticday](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/grape_detection_syntheticday.md) | Object Detection |              448 |
+[grape_detection_californiaday](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/grape_detection_californiaday.md) | Object Detection |              126 |
+[grape_detection_californianight](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/grape_detection_californianight.md) | Object Detection |              150 |
+[guava_disease_pakistan](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/guava_disease_pakistan.md) | Image Classification |              306 |
+[apple_detection_spain](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_detection_spain.md) | Object Detection |              967 |
+[apple_detection_drone_brazil](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/apple_detection_drone_brazil.md) | Object Detection |              689 |
+[plant_doc_classification](https://github.com/Project-AgML/AgML/blob/dev/docs/datasets/plant_doc_classification.md) | Image Classification |             2598 |
+[plant_doc_detection](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/plant_doc_detection.md) | Object Detection |             2346 |
+[wheat_head_counting](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/wheat_head_counting.md) | Object Detection |             6512 |
+[peachpear_flower_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/peachpear_flower_segmentation.md) | Semantic Segmentation |               42 |
+[red_grapes_and_leaves_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/red_grapes_and_leaves_segmentation.md) | Semantic Segmentation |              258 |
+[white_grapes_and_leaves_segmentation](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/white_grapes_and_leaves_segmentation.md) | Semantic Segmentation |              273 |
 [ghai_romaine_detection](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/ghai_romaine_detection.md) | Object Detection |              500 |
 [ghai_green_cabbage_detection](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/ghai_green_cabbage_detection.md) | Object Detection |              500 |
 [ghai_iceberg_lettuce_detection](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/ghai_iceberg_lettuce_detection.md) | Object Detection |              500 |
@@ -170,13 +170,13 @@ model.run_training(loader)
 
 ### Using Public Agricultural Data
 
-AgML aims to provide easy access to a range of existing public agricultural datasets The core of AgML's public data pipeline is 
-[`AgMLDataLoader`](/agml/data/loader.py). You can use the `AgMLDataLoader` or `agml.data.download_public_dataset()` to download 
-the dataset locally from which point it will be automatically loaded from the disk on future runs. 
+AgML aims to provide easy access to a range of existing public agricultural datasets The core of AgML's public data pipeline is
+[`AgMLDataLoader`](/agml/data/loader.py). You can use the `AgMLDataLoader` or `agml.data.download_public_dataset()` to download
+the dataset locally from which point it will be automatically loaded from the disk on future runs.
 From this point, the data within the loader can be split into train/val/test sets, batched, have augmentations and transforms
 applied, and be converted into a training-ready dataset (including batching, tensor conversion, and image formatting).
 
-To see the various ways in which you can use AgML datasets in your training pipelines, check out 
+To see the various ways in which you can use AgML datasets in your training pipelines, check out
 the [example notebook](/examples/AgML-Data.ipynb).
 
 ## Annotation Formats