Yolo_predict_tutorial_deepsea

topics/imaging/tutorials/yolo_prediction/tutorial.md

@@ -27,6 +27,7 @@ priority: 5
 contributions:
   authorship:
     - mjoudy
+    - yvanlebras
 
 follow_up_training:
   - type: "internal"
@@ -38,7 +39,7 @@ follow_up_training:
 
 # Introduction
 
-YOLO (You Only Look Once) is a fast, deep learning-based algorithm for real-time object detection. It predicts object classes and bounding boxes in a single pass over the image. YOLOv8 is one of its version, offering improved accuracy and speed.
+YOLO (You Only Look Once) is a fast, deep learning-based algorithm for real-time object detection. It predicts object classes and bounding boxes in a single pass over the image. YOLOv8 is a specific version, offering improved accuracy and speed.
 
 In this tutorial, you will use Galaxy to run two types of YOLOv8 models:
 
@@ -59,62 +60,88 @@ We will use selected images from the SEANOE dataset:
 
 The SEANOE #101899 collection features real underwater images captured by deep‑sea observatories as part of a citizen science initiative called Deep Sea Spy. These non‑destructive imaging stations continuously monitor marine ecosystems and provide snapshots of various fauna. In this dataset, multiple annotators—including trained scientists and enthusiastic citizen scientists—have manually labeled images with polygons, lines, or points highlighting marine organisms. These annotations are then cleaned and converted into bounding boxes to create a training-ready dataset for object detection with YOLOv8. Though the exact species vary, images often include deep-sea fish, species, making this dataset well-suited for practicing detection tasks.
 
+## Get data
+
+> <hands-on-title> Data Upload </hands-on-title>
+>
+> 1. Create a new history for this tutorial and give it a name (example: “Ecoregionalization workflow”) for you to find it again later if needed.
+>
+>    {% snippet faqs/galaxy/histories_create_new.md %}
+>
+>    {% snippet faqs/galaxy/histories_rename.md %}
+>
+> 2. Import images data files and models from [SEANOE marine ,datawarehouse](https://www.seanoe.org/data/00907/101899/)
+>
+>    DeepSeaSpy images data files and models as a zip file
+>    ```
+>    https://www.seanoe.org/data/00907/101899/data/115473.zip
+>    ```
+>
+>    {% snippet faqs/galaxy/datasets_import_via_link.md %}
+
+>
+> 3. Use  {% tool [Unzip a file](toolshed.g2.bx.psu.edu/repos/imgteam/unzip/unzip/6.0+galaxy0) %} to create a data collection in your history where all archive files will be unzipped
+>
+> 5. Unhide the models data files
+>
+>    History search `name:detection deleted:false visible:any` then unhidde the 2 model files "dataset_seanoe_101899_YOLOv8-weights-for-Bythograeidae-detection" and "dataset_seanoe_101899_YOLOv8-weights-for-Buccinidae-detection".
+>
+>
+>    {% snippet faqs/galaxy/datasets_unhidden.md %}
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="tabular" %}
+>
+> 6. Unhide images files and create a dedicated data collection
+>
+>    History search `extension:jpg deleted:false visible:any` then click on "select all" and "build dataset list", select 100 files and give a name of the data collection, "DeepSeaSpy 100 images sample" for example. Tips: To select only last 100 files, you can use the history search function and specify `extension:jpg deleted:false hid>XXXX visible:any` in the serach bar where XXXX is the id of the last image dataset minus 100 (for example `extension:jpg deleted:false hid>47659 visible:any` if you have images until the history dataset ID 47759.
+>
+>
+{: .hands_on}
+
 ## 📦 Model
 
 This dataset provides two pretrained YOLOv8 detection models tailored for the marine species found in SEANOE #101899. One model detects Buccinidae (a family of sea snails), and the other targets Bythograeidae (a family of deep-sea crabs). These models were trained on cleaned annotation sets that contain thousands of examples—for instance, the Buccinidae set includes over 14,900 annotations in total. For this tutorial, you’ll find two model files—`*.pt` files—each accompanied by the appropriate class_names.txt file. You can upload either or both to Galaxy to run detection experiments on your underwater images.
 
 ## ⚙️ Run YOLOv8 in detect mode
 
 
-  To perform object detection in Galaxy, use the tool "**Perform YOLO image labeling** with ultralytics (Galaxy Version 8.3.0+galaxy2)". Here’s how to set it up:
-
-- `Input images:` Select the .jpg underwater images you downloaded from the SEANOE dataset.
-
-- `Class names file:` This is a plain text file (.txt) that lists the names of the classes the model can detect.
-For example, for detecting Bythograeidae species, the file should look like this:
-
-```
-Bythograeid crab
-Buccinid snail
-
-```
-and for Buccinide spicies, class fil names could be like:
-
-```
-Autre poisson
-Couverture de moules
-Couverture microbienne
-Couverture vers tubicole
-Crabe araignée
-Crabe bythograeidé
-Crevette alvinocarididae
-Escargot buccinidé
-Ophiure
-Poisson Cataetyx
-Poisson chimère
-Poisson zoarcidé
-Pycnogonide
-Ver polynoidé
-Vers polynoidés
-```
-
-Each class name must be on its own line, in the same order used during model training. So the class ID 0 corresponds to Buccinidae, and 1 to Bythograeidae.
-
-- `Model`: Upload and choose from the dataset either `YOLOv8-weights-for-Buccinidae-detection.pt` or `YOLOv8-weights-for-Bythograeidae-detection.pt`, or test both on different runs.
-
-- `Prediction mode`: Select detect. This tells YOLO to output bounding boxes around detected objects.
-
-- `Image size`: Use 1000 (or a smaller number like 640 if processing speed is important). This controls how much the image is resized before prediction. Smaller values = faster but possibly less accurate.
-
-- `Confidence threshold`: Set to 0.25 (25%). This controls how confident the model must be to report a detection. If you increase this value (e.g., 0.5), you’ll get fewer detections, but they’ll be more confident. If you lower it (e.g., 0.1), you may get more results, but possibly more false positives.
-
-- `IoU threshold`: Set to 0.45. This is used for Non-Maximum Suppression (NMS), which removes overlapping detections. A higher IoU value (e.g., 0.7) keeps more overlapping boxes. A lower IoU (e.g., 0.3) removes more overlaps, which may help clean up crowded images.
-
-- `Max detections`: Set a reasonable cap like 300. This limits the number of objects detected per image.
-
-💡 Tip: Try changing the confidence and IoU thresholds to see how detection results vary. It helps you find a good balance between sensitivity and accuracy.
-
-> 💡 **Note**: These models are trained only for detection, not segmentation.
+> <hands-on-title> Detect Buccinid snails on images </hands-on-title>
+>
+> 1. {% tool [Perform YOLO image labeling](toolshed.g2.bx.psu.edu/repos/bgruening/yolo_predict/yolo_predict/8.3.0+galaxy2) %} with the following parameters:
+>    - {% icon param-file %} *"Input images"*: `DeepSeaSpy 100 images sample` (Input images dataset collection)
+>    - *"Class names file"*: `Buccinide` (Input plain text file (.txt) that lists the names of the classes the model can detect)
+>    - *"Model"*: `dataset_seanoe_101899_YOLOv8-weights-for-Buccinidae-detection` (Input pt file)
+>    - *"Prediction mode"*: `Detect`
+>    - *"Image size"*: `1000`
+>    - *"Confidence"*: `0.25`
+>    - *"IoU"*: `0.45`
+>    - *"Max detections"*: `300`
+>
+>    > <warning-title> Models type </warning-title>
+>    >
+>    > The model is trained only for detection, not segmentation
+>    >
+>    {: .warning}
+> 
+> > <tip-title>IoU threshold parameter</tip-title>
+> >
+> >   Try changing the confidence and IoU thresholds to see how detection results vary. It helps you find a good balance between sensitivity and accuracy.
+> >
+> >
+> {: .tip}
+>
+> > <comment-title>Additional information on class names file and parameters</comment-title>
+> >
+> >    Concerning the class names file: Each class name must be on its own line, in the same order used during model training. So the class ID 0 corresponds to Buccinidae, and 1 to Bythograeidae.
+> >    Concerning tool parameters:
+> >    - `Image size`: Use 1000 (or a smaller number like 640 if processing speed is important). This controls how much the image is resized before prediction. Smaller values = faster but possibly less accurate.
+> >    - `Confidence threshold`: Set to 0.25 (25%). This controls how confident the model must be to report a detection. If you increase this value (e.g., 0.5), you’ll get fewer detections, but they’ll be more confident. If you lower it (e.g., 0.1), you may get more results, but possibly more false positives.
+> >    - `IoU threshold`: Set to 0.45. This is used for Non-Maximum Suppression (NMS), which removes overlapping detections. A higher IoU value (e.g., 0.7) keeps more overlapping boxes. A lower IoU (e.g., 0.3) removes more overlaps, which may help clean up crowded images.
+> >    - `Max detections`: Set a reasonable cap like 300. This limits the number of objects detected per image.
+> > 
+> {: .comment}
+> 
+{: .hands_on}
 
 ## 🧾 Explore the Outputs
 
@@ -141,7 +168,7 @@ which means:
 
 These are your original images with colored boxes drawn around detected species. Each box also includes:
 The class name and the confidence score. For example, you might see a box labeled:
-Crabe bythograeid 0.54
+`Crabe bythograeid 0.54`
 
 ![buccinid](../../images/yolo/buccinid.jpeg)
 ![bythog](../../images/yolo/bythog2.jpeg)
@@ -150,8 +177,7 @@ Crabe bythograeid 0.54
 
 These images are useful for visually checking whether detections are correct or if something was missed.
 
-⚠️ No masks or segmentation files
-Since we used detect mode, this tool will not generate segmentation masks (like .tiff or polygon files). Those are only available in segment mode, which we'll cover next.
+⚠️ **No masks or segmentation files:** Since we used detect mode, this tool will not generate segmentation masks (like .tiff or polygon files). Those are only available in segment mode, which we'll cover next.
 
 
 
@@ -210,7 +236,7 @@ This output contains class IDs, bounding box coordinates, confidence scores, and
 |------------------|--------------------------|----------------------------------|
 | Mode             | `detect`                 | `segment`                        |
 | Output overlays  | Bounding boxes only      | Boxes + masks                    |
-| `.tiff` masks    | ❌                        | ✅                               |
+| .tiff masks      | ❌                        | ✅                               |
 | Use case         | Objects presence/count   | Object shape, size, morphology   |
 | Performance      | Faster                   | Slightly slower, more detailed   |