Add MNv4 medium recipe

CHANGELOG.md

@@ -2,6 +2,13 @@
 
 All notable changes to this project will be documented in this file.
 
+## \[2.7.0\]
+
+### New features
+
+- Add MobileNetV4 medium recipe
+  (https://github.com/open-edge-platform/training_extensions/pull/4341)
+
 ## \[2.6.0\]
 
 ### New features

README.md

@@ -14,8 +14,8 @@
 <!-- markdownlint-disable MD042 -->
 
 [![python](https://img.shields.io/badge/python-3.11%2B-green)]()
-[![pytorch](https://img.shields.io/badge/pytorch-2.7%2B-orange)]()
-[![openvino](https://img.shields.io/badge/openvino-2025.2-purple)]()
+[![pytorch](https://img.shields.io/badge/pytorch-2.8%2B-orange)]()
+[![openvino](https://img.shields.io/badge/openvino-2025.3-purple)]()
 
 <!-- markdownlint-enable  MD042 -->
 

library/pyproject.toml

@@ -41,7 +41,7 @@ dependencies = [
     "lightning==2.4.0",
     "torchmetrics==1.6.0",
     "pytorchcv==0.0.67",
-    "timm==1.0.3",
+    "timm==1.0.22",
     "openvino==2025.3",
     "openvino-model-api==0.3.0.4",
     "onnx==1.19.1",

library/src/otx/recipe/classification/h_label_cls/mobilenet_v4_medium.yaml

@@ -0,0 +1,51 @@
+task: H_LABEL_CLS
+model:
+  class_path: otx.backend.native.models.classification.hlabel_models.timm_model.TimmModelHLabelCls
+  init_args:
+    model_name: mobilenetv4_conv_medium.e500_r224_in1k
+
+    optimizer:
+      class_path: torch.optim.AdamW
+      init_args:
+        lr: 0.0005
+        weight_decay: 0.015
+
+    scheduler:
+      class_path: otx.backend.native.schedulers.LinearWarmupSchedulerCallable
+      init_args:
+        num_warmup_steps: 10
+        main_scheduler_callable:
+          class_path: lightning.pytorch.cli.ReduceLROnPlateau
+          init_args:
+            mode: max
+            factor: 0.5
+            patience: 3
+            monitor: val/accuracy
+
+engine:
+  device: auto
+
+callback_monitor: val/accuracy
+
+data: ../../_base_/data/classification.yaml
+
+callbacks:
+  - class_path: otx.backend.native.callbacks.adaptive_early_stopping.EarlyStoppingWithWarmup
+    init_args:
+      patience: 5
+  - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+    init_args:
+      dirpath: "" # use engine.work_dir
+      monitor: val/accuracy
+      mode: max
+      save_top_k: 1
+      save_last: true
+      auto_insert_metric_name: false
+      filename: "checkpoints/epoch_{epoch:03d}"
+
+overrides:
+  max_epochs: 90
+
+  data:
+    task: H_LABEL_CLS
+    data_format: datumaro

library/src/otx/recipe/classification/multi_class_cls/mobilenet_v4.yaml → library/src/otx/recipe/classification/multi_class_cls/mobilenet_v4_medium.yaml

@@ -2,14 +2,16 @@ task: MULTI_CLASS_CLS
 model:
   class_path: otx.backend.native.models.classification.multiclass_models.timm_model.TimmModelMulticlassCls
   init_args:
-    model_name: mobilenetv4_conv_small
+    model_name: mobilenetv4_conv_medium.e500_r224_in1k
     label_info: 1000
+    freeze_backbone: False
+
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: 0.0058
-        momentum: 0.9
-        weight_decay: 0.0001
+        lr: 0.0005
+        weight_decay: 0.015
+
     scheduler:
       class_path: otx.backend.native.schedulers.LinearWarmupSchedulerCallable
       init_args:
@@ -21,13 +23,27 @@ model:
             factor: 0.5
             patience: 3
             monitor: val/accuracy
+
 engine:
   device: auto
+
 callback_monitor: val/accuracy
+
 data: ../../_base_/data/classification.yaml
+
+callbacks:
+  - class_path: otx.backend.native.callbacks.adaptive_early_stopping.EarlyStoppingWithWarmup
+    init_args:
+      patience: 5
+  - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+    init_args:
+      dirpath: "" # use engine.work_dir
+      monitor: val/accuracy
+      mode: max
+      save_top_k: 1
+      save_last: true
+      auto_insert_metric_name: false
+      filename: "checkpoints/epoch_{epoch:03d}"
+
 overrides:
   max_epochs: 90
-  callbacks:
-    - class_path: otx.backend.native.callbacks.adaptive_early_stopping.EarlyStoppingWithWarmup
-      init_args:
-        patience: 5

library/src/otx/recipe/classification/multi_label_cls/mobilenet_v4_medium.yaml

@@ -0,0 +1,50 @@
+task: MULTI_LABEL_CLS
+model:
+  class_path: otx.backend.native.models.classification.multilabel_models.timm_model.TimmModelMultilabelCls
+  init_args:
+    model_name: mobilenetv4_conv_medium.e500_r224_in1k
+    label_info: 1000
+
+    optimizer:
+      class_path: torch.optim.AdamW
+      init_args:
+        lr: 0.0005
+        weight_decay: 0.015
+
+    scheduler:
+      class_path: otx.backend.native.schedulers.LinearWarmupSchedulerCallable
+      init_args:
+        num_warmup_steps: 10
+        main_scheduler_callable:
+          class_path: lightning.pytorch.cli.ReduceLROnPlateau
+          init_args:
+            mode: max
+            factor: 0.5
+            patience: 3
+            monitor: val/accuracy
+
+engine:
+  device: auto
+
+callback_monitor: val/accuracy
+
+data: ../../_base_/data/classification.yaml
+
+callbacks:
+  - class_path: otx.backend.native.callbacks.adaptive_early_stopping.EarlyStoppingWithWarmup
+    init_args:
+      patience: 5
+  - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+    init_args:
+      dirpath: "" # use engine.work_dir
+      monitor: val/accuracy
+      mode: max
+      save_top_k: 1
+      save_last: true
+      auto_insert_metric_name: false
+      filename: "checkpoints/epoch_{epoch:03d}"
+
+overrides:
+  data:
+    task: MULTI_LABEL_CLS
+    data_format: datumaro