From 5ecb7ab4dc40f8a39d56dc50248a9e810c5c1d73 Mon Sep 17 00:00:00 2001
From: Ivan Murabito <ivan.murabito@focoos.ai>
Date: Wed, 10 Dec 2025 11:41:30 +0000
Subject: [PATCH 1/8] feat: enhance image size handling across the codebase

- Updated `im_size` attribute in `ModelInfo` to support both int and tuple formats for image dimensions.
- Introduced `parse_im_size` function in CLI to handle various input formats for image size, including string representations.
- Modified training, validation, and export commands to accept and process image sizes as either int (square) or tuple (height, width).
- Ensured backward compatibility by maintaining int representation for existing functionality.
- Adjusted relevant processors and augmentations to accommodate the new image size format.
---
 docs/cli.md                              | 30 ++++++++++--
 docs/concepts.md                         |  4 +-
 docs/inference.md                        |  8 +++
 docs/training.md                         |  4 ++
 focoos/cli/cli.py                        | 62 +++++++++++++++++++++---
 focoos/cli/commands/export.py            |  7 +--
 focoos/cli/commands/train.py             |  9 ++--
 focoos/cli/commands/val.py               |  9 ++--
 focoos/data/default_aug.py               | 28 ++++++++---
 focoos/infer/infer_model.py              | 18 +++++--
 focoos/infer/runtimes/onnx.py            | 29 ++++++++---
 focoos/infer/runtimes/torchscript.py     | 28 ++++++++---
 focoos/models/base_model.py              | 17 +++++--
 focoos/models/bisenetformer/processor.py |  4 +-
 focoos/models/fai_cls/processor.py       | 11 ++++-
 focoos/models/fai_detr/processor.py      | 12 +++--
 focoos/models/fai_mf/processor.py        |  4 +-
 focoos/models/focoos_model.py            | 19 +++-----
 focoos/models/rtmo/processor.py          | 22 +++++++--
 focoos/ports.py                          | 14 +++++-
 focoos/processor/base_processor.py       |  6 ++-
 focoos/processor/processor_manager.py    |  7 ++-
 pyproject.toml                           |  2 +-
 uv.lock                                  | 10 ++--
 24 files changed, 275 insertions(+), 89 deletions(-)

diff --git a/docs/cli.md b/docs/cli.md
index a052d95b..f3254273 100644
--- a/docs/cli.md
+++ b/docs/cli.md
@@ -85,9 +85,12 @@ Where:
 ### 🏋️ Training
 
 ```bash
-# Basic training
+# Basic training (square image)
 focoos train --model fai-detr-m-coco --dataset mydataset.zip --im-size 640
 
+# Training with non-square resolution
+focoos train --model fai-detr-m-coco --dataset mydataset.zip --im-size 640,480
+
 # Advanced training with custom hyperparameters
 focoos train \
   --model fai-detr-m-coco \
@@ -157,9 +160,12 @@ focoos predict \
 ### 📤 Model Export
 
 ```bash
-# Export to ONNX (default)
+# Export to ONNX (default, square image)
 focoos export --model fai-detr-m-coco --im-size 640
 
+# Export to ONNX with non-square resolution
+focoos export --model fai-detr-m-coco --im-size 640,480
+
 # Export to TorchScript
 focoos export \
   --model fai-detr-m-coco \
@@ -249,7 +255,7 @@ The interface will automatically open in your default web browser, typically at
 | `--model`      | Model name or path          | **Required**   | `fai-detr-m-coco`, `path/to/model` |
 | `--dataset`    | Dataset name or path        | **Required**   | `mydataset.zip`, `path/to/data/`   |
 | `--source`     | Input source (predict only) | **Required**   | `image.jpg`                        |
-| `--im-size`    | Input image size            | 640            | Any positive integer               |
+| `--im-size`    | Input image size            | 640            | Integer (square) or "height,width" or "heightxwidth" (non-square) |
 | `--batch-size` | Batch size                  | 16             | Powers of 2 recommended            |
 | `--device`     | Compute device              | `cuda`         | `cuda`, `cpu`               |
 | `--workers`    | Data loading workers        | 4              | 0-16 recommended                   |
@@ -319,7 +325,7 @@ Use CLI commands programmatically in Python:
 ```python
 from focoos.cli.commands import train_command, predict_command, export_command
 
-# Train a model
+# Train a model (square image)
 train_command(
     model_name="fai-detr-m-coco",
     dataset_name="mydataset.zip",
@@ -330,6 +336,17 @@ train_command(
     batch_size=16
 )
 
+# Train a model with non-square resolution
+train_command(
+    model_name="fai-detr-m-coco",
+    dataset_name="mydataset.zip",
+    dataset_layout="roboflow_coco",
+    im_size=(640, 480),  # (height, width)
+    run_name="my_training",
+    max_iters=5000,
+    batch_size=16
+)
+
 # Run inference
 results = predict_command(
     model_name="fai-detr-m-coco",
@@ -364,8 +381,11 @@ focoos version
 # Reduce batch size
 focoos train --model fai-detr-m-coco --dataset data.zip --batch-size 8
 
-# Use smaller image size
+# Use smaller image size (square)
 focoos train --model fai-detr-m-coco --dataset data.zip --im-size 480
+
+# Use non-square image size for memory efficiency
+focoos train --model fai-detr-m-coco --dataset data.zip --im-size 480,360
 ```
 
 **Dataset not found:**
diff --git a/docs/concepts.md b/docs/concepts.md
index 81c2dca1..1c812f7f 100644
--- a/docs/concepts.md
+++ b/docs/concepts.md
@@ -120,7 +120,7 @@ model_info = ModelInfo(
     name="custom_detector",
     model_family=ModelFamily.DETR,
     classes=["person", "car", "bicycle"],
-    im_size=640,
+    im_size=640,  # Square image (640x640), or use (640, 480) for non-square (height, width)
     task=Task.DETECTION,
     config={
         "num_classes": 3,
@@ -220,7 +220,7 @@ model.train(train_args, train_dataset, val_dataset, hub=hub)
 Exports the model to different runtime formats for optimized inference. The main function arguments are:
  - `runtime_type`: specify the target runtime and must be one of the supported (see [RuntimeType](/focoos/api/ports/#focoos.ports.RuntimeType))
  - `out_dir`: the destination folder for the exported model
- - `image_size`: the target image size, as an optional integer
+ - `image_size`: the target image size, as an optional integer (square) or tuple (height, width) for non-square images
 
 The function returns an [`InferModel`](#infer-model) instance for the exported model.
 
diff --git a/docs/inference.md b/docs/inference.md
index 72c46af1..022fc1c5 100644
--- a/docs/inference.md
+++ b/docs/inference.md
@@ -151,7 +151,11 @@ runtime = RuntimeType.TORCHSCRIPT_32
 It's time to export the model. We can use the export method of the models.
 
 ```python
+# Export with square resolution (512x512)
 optimized_model = model.export(runtime_type=runtime, image_size=512)
+
+# Export with non-square resolution (640x480, height x width)
+optimized_model = model.export(runtime_type=runtime, image_size=(640, 480))
 ```
 
 Let's visualize the output. As you will see, there are not differences from the model in pure torch.
@@ -183,5 +187,9 @@ optimized_model = model.export(runtime_type=runtime)
 detections = optimized_model(image)
 display(annotate_image(image, detections, task=model.model_info.task, classes=model.model_info.classes))
 
+# Benchmark with square resolution
 optimized_model.benchmark(iterations=10, size=640)
+
+# Benchmark with non-square resolution
+optimized_model.benchmark(iterations=10, size=(640, 480))
 ```
diff --git a/docs/training.md b/docs/training.md
index b6342ccc..096f6460 100644
--- a/docs/training.md
+++ b/docs/training.md
@@ -65,8 +65,12 @@ task = dataset.task  # see ports.Task for more information
 layout = dataset.layout  # see ports.DatasetLayout for more information
 auto_dataset = AutoDataset(dataset_name=dataset_path, task=task, layout=layout)
 
+# Square resolution (512x512)
 augs = DatasetAugmentations(resolution=512).get_augmentations()
 
+# Non-square resolution (640x480, height x width)
+augs = DatasetAugmentations(resolution=(640, 480)).get_augmentations()
+
 train_dataset = auto_dataset.get_split(augs=augs, split=DatasetSplitType.TRAIN)
 valid_dataset = auto_dataset.get_split(augs=augs, split=DatasetSplitType.VAL)
 ```
diff --git a/focoos/cli/cli.py b/focoos/cli/cli.py
index 0b75139e..447adc39 100644
--- a/focoos/cli/cli.py
+++ b/focoos/cli/cli.py
@@ -73,7 +73,7 @@
 """
 
 import uuid
-from typing import Optional, cast, get_args
+from typing import Optional, Tuple, Union, cast, get_args
 
 import typer
 from typing_extensions import Annotated
@@ -100,6 +100,40 @@
 
 logger = get_logger("CLI")
 
+
+def parse_im_size(value: Union[str, int]) -> Union[int, Tuple[int, int]]:
+    """Parse image size from string or int.
+
+    Supports formats:
+    - int: 640 (square image)
+    - str: "640" (square image)
+    - str: "640,480" or "640x480" (non-square image as height,width)
+
+    Args:
+        value: Image size as int or string
+
+    Returns:
+        int for square images, tuple (height, width) for non-square
+    """
+    if isinstance(value, int):
+        return value
+    if isinstance(value, str):
+        # Try comma or x separator
+        if "," in value:
+            parts = value.split(",")
+        elif "x" in value or "X" in value:
+            parts = value.replace("X", "x").split("x")
+        else:
+            # Single number - square image
+            return int(value)
+
+        if len(parts) == 2:
+            return (int(parts[0].strip()), int(parts[1].strip()))
+        else:
+            raise ValueError(f"Invalid image size format: {value}. Use '640', '640,480', or '640x480'")
+    return value
+
+
 app = typer.Typer(
     name="focoos",
     help=__doc__,
@@ -251,7 +285,9 @@ def train(
         Optional[str], typer.Option(help="Datasets directory (default: ~/FocoosAI/datasets/)")
     ] = None,
     dataset_layout: Annotated[DatasetLayout, typer.Option(help="Dataset layout")] = DatasetLayout.ROBOFLOW_COCO,
-    im_size: Annotated[int, typer.Option(help="Image size")] = 640,
+    im_size: Annotated[
+        str, typer.Option(help="Image size (int for square, or 'height,width' or 'heightxwidth' for non-square)")
+    ] = "640",
     output_dir: Annotated[Optional[str], typer.Option(help="Output directory")] = None,
     ckpt_dir: Annotated[Optional[str], typer.Option(help="Checkpoint directory")] = None,
     init_checkpoint: Annotated[Optional[str], typer.Option(help="Initial checkpoint path")] = None,
@@ -316,7 +352,9 @@ def train(
             generates a unique name using model name and UUID.
         datasets_dir (Optional[str]): Custom directory for datasets.
         dataset_layout (DatasetLayout): Layout format of the dataset. Defaults to ROBOFLOW_COCO.
-        im_size (int): Input image size for training. Defaults to 640.
+        im_size (str): Input image size for training. Can be int (e.g., "640") for square images,
+            or "height,width" or "heightxwidth" (e.g., "640,480" or "640x480") for non-square images.
+            Defaults to "640".
         output_dir (Optional[str]): Directory to save training outputs and logs.
         ckpt_dir (Optional[str]): Directory to save model checkpoints.
         init_checkpoint (Optional[str]): Path to initial checkpoint for transfer learning.
@@ -435,11 +473,12 @@ def train(
         validated_optimizer = cast(OptimizerType, optimizer.upper())
         assert optimizer in get_args(OptimizerType)
 
+        parsed_im_size = parse_im_size(im_size)
         train_command(
             model_name=model,
             dataset_name=dataset,
             dataset_layout=dataset_layout,
-            im_size=im_size,
+            im_size=parsed_im_size,
             run_name=run_name or f"{model}-{uuid.uuid4()}",
             output_dir=output_dir,
             ckpt_dir=ckpt_dir,
@@ -494,7 +533,9 @@ def val(
     ] = None,
     run_name: Annotated[Optional[str], typer.Option(help="Run name")] = None,
     dataset_layout: Annotated[DatasetLayout, typer.Option(help="Dataset layout")] = DatasetLayout.ROBOFLOW_COCO,
-    im_size: Annotated[int, typer.Option(help="Image size")] = 640,
+    im_size: Annotated[
+        str, typer.Option(help="Image size (int for square, or 'height,width' or 'heightxwidth' for non-square)")
+    ] = "640",
     output_dir: Annotated[Optional[str], typer.Option(help="Output directory")] = None,
     ckpt_dir: Annotated[Optional[str], typer.Option(help="Checkpoint directory")] = None,
     init_checkpoint: Annotated[Optional[str], typer.Option(help="Initial checkpoint")] = None,
@@ -677,11 +718,12 @@ def val(
         validated_optimizer = cast(OptimizerType, optimizer.upper())
         assert optimizer in get_args(OptimizerType)
 
+        parsed_im_size = parse_im_size(im_size)
         val_command(
             model_name=model,
             dataset_name=dataset,
             dataset_layout=dataset_layout,
-            im_size=im_size,
+            im_size=parsed_im_size,
             run_name=run_name or f"{model}-{uuid.uuid4()}",
             output_dir=output_dir,
             ckpt_dir=ckpt_dir,
@@ -881,7 +923,10 @@ def export(
     output_dir: Annotated[Optional[str], typer.Option(help="Output directory")] = None,
     device: Annotated[Optional[str], typer.Option(help="Device (cuda or cpu)")] = "cuda",
     onnx_opset: Annotated[Optional[int], typer.Option(help="ONNX opset version")] = 17,
-    im_size: Annotated[Optional[int], typer.Option(help="Image size for export")] = 640,
+    im_size: Annotated[
+        Optional[str],
+        typer.Option(help="Image size for export (int for square, or 'height,width' or 'heightxwidth' for non-square)"),
+    ] = "640",
     overwrite: Annotated[Optional[bool], typer.Option(help="Overwrite existing files")] = False,
 ):
     """Export a trained model to various deployment formats.
@@ -985,13 +1030,14 @@ def export(
     try:
         validated_device = cast(DeviceType, device)
         assert device in get_args(DeviceType)
+        parsed_im_size = parse_im_size(im_size) if im_size is not None else None
         export_command(
             model_name=model,
             format=format,
             output_dir=output_dir,
             device=validated_device,
             onnx_opset=onnx_opset,
-            im_size=im_size,
+            im_size=parsed_im_size,
             overwrite=overwrite,
         )
     except Exception as e:
diff --git a/focoos/cli/commands/export.py b/focoos/cli/commands/export.py
index f9d79b14..d31a0abb 100644
--- a/focoos/cli/commands/export.py
+++ b/focoos/cli/commands/export.py
@@ -55,7 +55,7 @@
     - [`focoos.ports.RuntimeType`][focoos.ports.RuntimeType]: Runtime type configurations
 """
 
-from typing import Literal, Optional
+from typing import Literal, Optional, Tuple, Union
 
 from focoos.model_manager import ModelManager
 from focoos.ports import ExportFormat, RuntimeType
@@ -70,7 +70,7 @@ def export_command(
     output_dir: Optional[str] = None,
     device: Optional[Literal["cuda", "cpu"]] = None,
     onnx_opset: Optional[int] = None,
-    im_size: Optional[int] = None,
+    im_size: Optional[Union[int, Tuple[int, int]]] = None,
     overwrite: Optional[bool] = None,
 ):
     """Export a model to different deployment formats.
@@ -114,7 +114,8 @@ def export_command(
         onnx_opset (Optional[int], optional): ONNX opset version for ONNX exports.
             Higher versions support more operations but may have compatibility issues.
             Common versions: 11, 13, 16, 17. Defaults to 17 if None.
-        im_size (Optional[int], optional): Input image size for the exported model.
+        im_size (Optional[Union[int, Tuple[int, int]]], optional): Input image size for the exported model.
+            If int, treated as square (size, size). If tuple, treated as (height, width).
             Used to define fixed input shapes for optimization.
             If None, uses the model's default input size.
             Defaults to None.
diff --git a/focoos/cli/commands/train.py b/focoos/cli/commands/train.py
index 033c18e8..3a2377c0 100644
--- a/focoos/cli/commands/train.py
+++ b/focoos/cli/commands/train.py
@@ -74,7 +74,7 @@
     - [`focoos.ports.TrainerArgs`][focoos.ports.TrainerArgs]: Training configuration
 """
 
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 from focoos.data.auto_dataset import AutoDataset
 from focoos.data.default_aug import get_default_by_task
@@ -101,7 +101,7 @@ def train_command(
     ## Dataset args
     dataset_name: str,
     dataset_layout: DatasetLayout,
-    im_size: int,
+    im_size: Union[int, Tuple[int, int]],
     ##################
     ## Training args
     run_name: str,
@@ -183,8 +183,9 @@ def train_command(
             and dataset identifiers.
         dataset_layout (DatasetLayout): Layout format of the dataset.
             Supported formats: ROBOFLOW_COCO, YOLO, COCO, etc.
-        im_size (int): Input image size for training. Images are resized
-            to this size while maintaining aspect ratio.
+        im_size (Union[int, Tuple[int, int]]): Input image size for training.
+            If int, treated as square (size, size). If tuple, treated as (height, width).
+            Images are resized to this size while maintaining aspect ratio.
         run_name (str): Unique name for this training run. Used for
             experiment tracking, logging, and output organization.
         output_dir (Optional[str], optional): Directory to save training outputs
diff --git a/focoos/cli/commands/val.py b/focoos/cli/commands/val.py
index 53a0b273..08538d7a 100644
--- a/focoos/cli/commands/val.py
+++ b/focoos/cli/commands/val.py
@@ -56,7 +56,7 @@
     - [`focoos.ports.TrainerArgs`][focoos.ports.TrainerArgs]: Configuration parameters
 """
 
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 from focoos.data.auto_dataset import AutoDataset
 from focoos.data.default_aug import get_default_by_task
@@ -82,7 +82,7 @@ def val_command(
     ## Dataset args
     dataset_name: str,
     dataset_layout: DatasetLayout,
-    im_size: int,
+    im_size: Union[int, Tuple[int, int]],
     ##################
     ## Training args
     run_name: str,
@@ -163,8 +163,9 @@ def val_command(
             and dataset identifiers.
         dataset_layout (DatasetLayout): Layout format of the dataset.
             Supported formats: ROBOFLOW_COCO, YOLO, COCO, etc.
-        im_size (int): Input image size for validation. Images are resized
-            to this size while maintaining aspect ratio.
+        im_size (Union[int, Tuple[int, int]]): Input image size for validation.
+            If int, treated as square (size, size). If tuple, treated as (height, width).
+            Images are resized to this size while maintaining aspect ratio.
         run_name (str): Unique name for this validation run. Used for
             result organization, logging, and report generation.
         output_dir (Optional[str], optional): Directory to save validation outputs
diff --git a/focoos/data/default_aug.py b/focoos/data/default_aug.py
index af2668c2..4b506be7 100644
--- a/focoos/data/default_aug.py
+++ b/focoos/data/default_aug.py
@@ -1,7 +1,7 @@
 import copy
 import sys
 from dataclasses import dataclass
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, Union
 
 from focoos.data.transforms import augmentation as A
 from focoos.data.transforms import transform as T
@@ -21,8 +21,9 @@ class DatasetAugmentations:
     color and geometric augmentations.
 
     Attributes:
-        resolution (int): Target image size for resizing operations.
-            Range [256, 1024]. Default: 640.
+        resolution (Union[int, Tuple[int, int]]): Target image size for resizing operations.
+            If int, treated as square (size, size). If tuple, treated as (height, width).
+            Range [256, 1024] for int. Default: 640.
         ==
         color_augmentation (float): Strenght of color augmentations.
             Range [0,1]. Default: 0.0.
@@ -56,7 +57,7 @@ class DatasetAugmentations:
     """
 
     # Resolution for resizing
-    resolution: int = 640
+    resolution: Union[int, Tuple[int, int]] = 640
 
     # Color augmentation parameters
     color_augmentation: float = 0.0
@@ -102,6 +103,14 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
         augs = []
         self.max_size = self.max_size if self.max_size else sys.maxsize
 
+        # Normalize resolution to tuple format for easier handling
+        if isinstance(self.resolution, int):
+            resolution_tuple = (self.resolution, self.resolution)
+            resolution_value = self.resolution  # For scalar operations
+        else:
+            resolution_tuple = self.resolution
+            resolution_value = min(self.resolution)  # Use min for scalar operations (shortest edge)
+
         ### Add color augmentation if configured
         if self.color_augmentation > 0:
             brightness_delta = int(self.color_base_brightness * self.color_augmentation)
@@ -139,7 +148,7 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
 
         ### Add AspectRatio augmentations based on configuration
         if self.square > 0.0:
-            augs.append(A.RandomApply(A.Resize(shape=(self.resolution, self.resolution)), prob=self.square))
+            augs.append(A.RandomApply(A.Resize(shape=resolution_tuple), prob=self.square))
         elif self.aspect_ratio > 0.0:
             augs.append(A.RandomAspectRatio(aspect_ratio=self.aspect_ratio))
 
@@ -147,7 +156,7 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
         min_scale, max_scale = 2 ** (-self.scale_ratio), 2**self.scale_ratio
         augs.append(
             A.ResizeShortestEdge(
-                short_edge_length=[int(x * self.resolution) for x in [min_scale, max_scale]],
+                short_edge_length=[int(x * resolution_value) for x in [min_scale, max_scale]],
                 sample_style="range",
                 max_size=self.max_size,
             )
@@ -160,7 +169,10 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
 
         # Add cropping if configured
         if self.crop:
-            crop_range = (self.crop_size or self.resolution, self.crop_size or self.resolution)
+            if self.crop_size:
+                crop_range = (self.crop_size, self.crop_size)
+            else:
+                crop_range = resolution_tuple
             augs.append(A.RandomCrop(crop_type="absolute_range", crop_size=crop_range))
 
         return augs
@@ -251,7 +263,7 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
 
 
 def get_default_by_task(
-    task: Task, resolution: int = 640, advanced: bool = False
+    task: Task, resolution: Union[int, Tuple[int, int]] = 640, advanced: bool = False
 ) -> Tuple[DatasetAugmentations, DatasetAugmentations]:
     if task == Task.DETECTION:
         train, val = (
diff --git a/focoos/infer/infer_model.py b/focoos/infer/infer_model.py
index 7def953e..a6afb0d4 100644
--- a/focoos/infer/infer_model.py
+++ b/focoos/infer/infer_model.py
@@ -278,9 +278,18 @@ def end2end_benchmark(
         else:
             engine = "torchscript"
             device = get_device_name()
-        logger.info(f"⏱️ Benchmarking End-to-End latency on {device}, size: {size}x{size}..")
 
-        np_input = (255 * np.random.random((size[0], size[1], 3))).astype(np.uint8)
+        # Normalize size to tuple format
+        if isinstance(size, int):
+            size_tuple = (size, size)
+            size_str = f"{size}x{size}"
+        else:
+            size_tuple = size
+            size_str = f"{size[0]}x{size[1]}"
+
+        logger.info(f"⏱️ Benchmarking End-to-End latency on {device}, size: {size_str}..")
+
+        np_input = (255 * np.random.random((size_tuple[0], size_tuple[1], 3))).astype(np.uint8)
 
         durations = []
         for step in range(iterations + 5):
@@ -293,6 +302,9 @@ def end2end_benchmark(
 
         durations = np.array(durations)
 
+        # For LatencyMetrics.im_size (int), use height (first dimension) as representative value
+        # This maintains backward compatibility while supporting non-square images
+        im_size_repr = size_tuple[0] if isinstance(size, tuple) and size_tuple[0] != size_tuple[1] else size_tuple[0]
         metrics = LatencyMetrics(
             fps=int(1000 / durations.mean()),
             engine=engine,
@@ -300,7 +312,7 @@ def end2end_benchmark(
             max=round(durations.max().astype(float), 3),
             min=round(durations.min().astype(float), 3),
             std=round(durations.std().astype(float), 3),
-            im_size=size[0],  # FIXME: this is a hack to get the im_size as int, assuming it's a square
+            im_size=im_size_repr,
             device=device,
         )
         logger.info(f"🔥 FPS: {metrics.fps} Mean latency: {metrics.mean} ms ")
diff --git a/focoos/infer/runtimes/onnx.py b/focoos/infer/runtimes/onnx.py
index 21ba09d8..dd07e247 100644
--- a/focoos/infer/runtimes/onnx.py
+++ b/focoos/infer/runtimes/onnx.py
@@ -127,8 +127,16 @@ def _setup_providers(self, model_dir: Path):
 
     def _warmup(self):
         size = self.model_info.im_size
-        logger.info(f"⏱️ Warming up model {self.name} on {self.active_provider}, size: {size}x{size}..")
-        np_image = np.random.rand(1, 3, size, size).astype(self.dtype)
+        # Normalize size to tuple format
+        if isinstance(size, int):
+            size_tuple = (size, size)
+            size_str = f"{size}x{size}"
+        else:
+            size_tuple = size
+            size_str = f"{size[0]}x{size[1]}"
+
+        logger.info(f"⏱️ Warming up model {self.name} on {self.active_provider}, size: {size_str}..")
+        np_image = np.random.rand(1, 3, size_tuple[0], size_tuple[1]).astype(self.dtype)
         input_name = self.ort_sess.get_inputs()[0].name
         out_name = [output.name for output in self.ort_sess.get_outputs()]
 
@@ -168,12 +176,18 @@ def benchmark(self, iterations: int = 50, size: Union[int, Tuple[int, int]] = 64
         device_name = get_device_name()
         if self.active_provider == "CPUExecutionProvider":
             device_name = get_cpu_name()
+
+        # Normalize size to tuple format
         if isinstance(size, int):
-            size = (size, size)
+            size_tuple = (size, size)
+            size_str = f"{size}x{size}"
+        else:
+            size_tuple = size
+            size_str = f"{size[0]}x{size[1]}"
 
-        logger.info(f"⏱️ Benchmarking latency on {device_name}, size: {size}..")
+        logger.info(f"⏱️ Benchmarking latency on {device_name}, size: {size_str}..")
 
-        np_input = (255 * np.random.random((1, 3, size[0], size[1]))).astype(self.dtype)
+        np_input = (255 * np.random.random((1, 3, size_tuple[0], size_tuple[1]))).astype(self.dtype)
         input_name = self.ort_sess.get_inputs()[0].name
         out_name = [output.name for output in self.ort_sess.get_outputs()]
 
@@ -188,6 +202,9 @@ def benchmark(self, iterations: int = 50, size: Union[int, Tuple[int, int]] = 64
 
         durations = np.array(durations)
 
+        # For LatencyMetrics.im_size (int), use height (first dimension) as representative value
+        # This maintains backward compatibility while supporting non-square images
+        im_size_repr = size_tuple[0] if isinstance(size, tuple) and size_tuple[0] != size_tuple[1] else size_tuple[0]
         metrics = LatencyMetrics(
             fps=int(1000 / durations.mean()),
             engine=engine,
@@ -195,7 +212,7 @@ def benchmark(self, iterations: int = 50, size: Union[int, Tuple[int, int]] = 64
             max=round(durations.max().astype(float), 3),
             min=round(durations.min().astype(float), 3),
             std=round(durations.std().astype(float), 3),
-            im_size=size[0],  # FIXME: this is a hack to get the im_size as int, assuming it's a square
+            im_size=im_size_repr,
             device=device_name,
         )
         logger.info(f"🔥 FPS: {metrics.fps} Mean latency: {metrics.mean} ms ")
diff --git a/focoos/infer/runtimes/torchscript.py b/focoos/infer/runtimes/torchscript.py
index 619e0da5..ff1f565a 100644
--- a/focoos/infer/runtimes/torchscript.py
+++ b/focoos/infer/runtimes/torchscript.py
@@ -53,9 +53,17 @@ def __init__(
                 if self.model_info.task in [Task.DETECTION, Task.CLASSIFICATION] and self.model_info.im_size
                 else 640
             )
-            logger.info(f"⏱️ Warming up model {self.model_info.name} on {self.device}, size: {size}x{size}..")
+            # Normalize size to tuple format
+            if isinstance(size, int):
+                size_tuple = (size, size)
+                size_str = f"{size}x{size}"
+            else:
+                size_tuple = size
+                size_str = f"{size[0]}x{size[1]}"
+
+            logger.info(f"⏱️ Warming up model {self.model_info.name} on {self.device}, size: {size_str}..")
             with torch.no_grad():
-                np_image = torch.rand(1, 3, size, size).to(self.device)
+                np_image = torch.rand(1, 3, size_tuple[0], size_tuple[1]).to(self.device)
                 for _ in range(self.opts.warmup_iter):
                     self.model(np_image)
 
@@ -91,12 +99,18 @@ def benchmark(self, iterations: int = 20, size: Union[int, Tuple[int, int]] = 64
             device_name = get_cpu_name()
         else:
             device_name = get_device_name()
-        logger.info(f"⏱️ Benchmarking latency on {device_name}, size: {size}x{size}..")
 
+        # Normalize size to tuple format
         if isinstance(size, int):
-            size = (size, size)
+            size_tuple = (size, size)
+            size_str = f"{size}x{size}"
+        else:
+            size_tuple = size
+            size_str = f"{size[0]}x{size[1]}"
+
+        logger.info(f"⏱️ Benchmarking latency on {device_name}, size: {size_str}..")
 
-        torch_input = torch.rand(1, 3, size[0], size[1], device=self.device)
+        torch_input = torch.rand(1, 3, size_tuple[0], size_tuple[1], device=self.device)
         durations = []
 
         with torch.no_grad():
@@ -110,6 +124,8 @@ def benchmark(self, iterations: int = 20, size: Union[int, Tuple[int, int]] = 64
 
         durations = np.array(durations)
 
+        # For LatencyMetrics.im_size (int), use height (first dimension) as representative value
+        im_size_repr = size_tuple[0] if isinstance(size, tuple) and size_tuple[0] != size_tuple[1] else size_tuple[0]
         metrics = LatencyMetrics(
             fps=int(1000 / durations.mean().astype(float)),
             engine=engine,
@@ -117,7 +133,7 @@ def benchmark(self, iterations: int = 20, size: Union[int, Tuple[int, int]] = 64
             max=round(durations.max().astype(float), 3),
             min=round(durations.min().astype(float), 3),
             std=round(durations.std().astype(float), 3),
-            im_size=size[0],
+            im_size=im_size_repr,
             device=device_name,
         )
         logger.info(f"🔥 FPS: {metrics.fps} Mean latency: {metrics.mean} ms ")
diff --git a/focoos/models/base_model.py b/focoos/models/base_model.py
index 789e1f0e..f98e32d8 100644
--- a/focoos/models/base_model.py
+++ b/focoos/models/base_model.py
@@ -174,9 +174,17 @@ def benchmark(self, iterations: int = 50, size: Tuple[int, int] = (640, 640)) ->
         else:
             device_name = get_device_name()
 
-        logger.info(f"⏱️ Benchmarking latency on {device_name} ({self.device}), size: {size}x{size}..")
+        # Normalize size to tuple format
+        if isinstance(size, int):
+            size_tuple = (size, size)
+            size_str = f"{size}x{size}"
+        else:
+            size_tuple = size
+            size_str = f"{size[0]}x{size[1]}"
+
+        logger.info(f"⏱️ Benchmarking latency on {device_name} ({self.device}), size: {size_str}..")
         # warmup
-        data = 128 * torch.randn(1, 3, size[0], size[1]).to(self.device)
+        data = 128 * torch.randn(1, 3, size_tuple[0], size_tuple[1]).to(self.device)
         durations = []
         for _ in range(iterations):
             start = torch.cuda.Event(enable_timing=True)
@@ -188,6 +196,9 @@ def benchmark(self, iterations: int = 50, size: Tuple[int, int] = (640, 640)) ->
             durations.append(start.elapsed_time(end))
 
         durations = np.array(durations)
+        # For LatencyMetrics.im_size (int), use height (first dimension) as representative value
+        # This maintains backward compatibility while supporting non-square images
+        im_size_repr = size_tuple[0] if isinstance(size, tuple) and size_tuple[0] != size_tuple[1] else size_tuple[0]
         metrics = LatencyMetrics(
             fps=int(1000 / durations.mean()),
             engine=f"torch.{self.device}",
@@ -195,7 +206,7 @@ def benchmark(self, iterations: int = 50, size: Tuple[int, int] = (640, 640)) ->
             max=round(durations.max().astype(float), 3),
             min=round(durations.min().astype(float), 3),
             std=round(durations.std().astype(float), 3),
-            im_size=size[0],  # FIXME: this is a hack to get the im_size as int, assuming it's a square
+            im_size=im_size_repr,
             device=device_name,
         )
         logger.info(f"🔥 FPS: {metrics.fps} Mean latency: {metrics.mean} ms ")
diff --git a/focoos/models/bisenetformer/processor.py b/focoos/models/bisenetformer/processor.py
index c956b5b1..1ce9ae90 100644
--- a/focoos/models/bisenetformer/processor.py
+++ b/focoos/models/bisenetformer/processor.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -23,7 +23,7 @@ def interpolate_image(image, size):
 
 
 class BisenetFormerProcessor(Processor):
-    def __init__(self, config: BisenetFormerConfig, image_size: Optional[int] = None):
+    def __init__(self, config: BisenetFormerConfig, image_size: Optional[Union[int, Tuple[int, int]]] = None):
         super().__init__(config, image_size)
         self.config = config
         processing_functions = {
diff --git a/focoos/models/fai_cls/processor.py b/focoos/models/fai_cls/processor.py
index 7a5cb318..a8dda757 100644
--- a/focoos/models/fai_cls/processor.py
+++ b/focoos/models/fai_cls/processor.py
@@ -16,11 +16,12 @@
 class ClassificationProcessor(Processor):
     """Processor for image classification model inputs and outputs."""
 
-    def __init__(self, config: ClassificationConfig, image_size: Optional[int] = None):
+    def __init__(self, config: ClassificationConfig, image_size: Optional[Union[int, Tuple[int, int]]] = None):
         """Initialize the processor with model configuration.
 
         Args:
             config: Model configuration
+            image_size: Image size. If int, treated as square (size, size). If tuple, treated as (height, width).
         """
         super().__init__(config, image_size)
         self.config = config
@@ -75,7 +76,13 @@ def preprocess(
 
         if self.training:
             raise ValueError("During training, inputs should be a list of DetectionDatasetDict")
-        target_size = (self.image_size, self.image_size) if self.image_size is not None else None
+        if self.image_size is not None:
+            if isinstance(self.image_size, int):
+                target_size = (self.image_size, self.image_size)
+            else:
+                target_size = self.image_size
+        else:
+            target_size = None
         # if target_size is not None:
         # print("Resizing to", target_size)
         images_torch = self.get_torch_batch(
diff --git a/focoos/models/fai_detr/processor.py b/focoos/models/fai_detr/processor.py
index 4ce17f2e..2a7f11a8 100644
--- a/focoos/models/fai_detr/processor.py
+++ b/focoos/models/fai_detr/processor.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -58,7 +58,7 @@ def detector_postprocess(
 
 
 class DETRProcessor(Processor):
-    def __init__(self, config: DETRConfig, image_size: Optional[int] = None):
+    def __init__(self, config: DETRConfig, image_size: Optional[Union[int, Tuple[int, int]]] = None):
         super().__init__(config, image_size)
         self.top_k = config.top_k
         self.threshold = config.threshold
@@ -104,7 +104,13 @@ def preprocess(
                 raise ValueError("During training, inputs should be a list of DetectionDatasetDict")
 
             # Use optimized get_tensors with optional resizing for better memory efficiency
-            target_size = (self.image_size, self.image_size) if self.image_size is not None else None
+            if self.image_size is not None:
+                if isinstance(self.image_size, int):
+                    target_size = (self.image_size, self.image_size)
+                else:
+                    target_size = self.image_size
+            else:
+                target_size = None
             images_torch = self.get_torch_batch(
                 inputs,  # type: ignore
                 target_size=target_size,
diff --git a/focoos/models/fai_mf/processor.py b/focoos/models/fai_mf/processor.py
index 3afe34fc..d293ac22 100644
--- a/focoos/models/fai_mf/processor.py
+++ b/focoos/models/fai_mf/processor.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -26,7 +26,7 @@ class MaskFormerProcessor(Processor):
     def __init__(
         self,
         config: MaskFormerConfig,
-        image_size: Optional[int] = None,
+        image_size: Optional[Union[int, Tuple[int, int]]] = None,
     ):
         super().__init__(config, image_size)
         processing_functions = {
diff --git a/focoos/models/focoos_model.py b/focoos/models/focoos_model.py
index 09099cdb..8458e015 100644
--- a/focoos/models/focoos_model.py
+++ b/focoos/models/focoos_model.py
@@ -455,18 +455,15 @@ def export(
             input_size=export_image_size,
         )
         os.makedirs(out_dir, exist_ok=True)
-        if image_size is None:
-            data = 128 * torch.randn(1, 3, self.model_info.im_size, self.model_info.im_size).to(device)
+
+        if isinstance(export_image_size, int):
+            height, width = export_image_size, export_image_size
         else:
-            if isinstance(image_size, int):
-                # Square image
-                data = 128 * torch.randn(1, 3, image_size, image_size).to(device)
-                self.model_info.im_size = image_size
-            else:
-                # Tuple (height, width)
-                height, width = image_size
-                data = 128 * torch.randn(1, 3, height, width).to(device)
-                self.model_info.im_size = max(height, width)  # Use max dimension for compatibility
+            height, width = export_image_size
+
+        self.model_info.im_size = export_image_size
+
+        data = 128 * torch.randn(1, 3, height, width).to(device)
 
         export_model_name = ArtifactName.ONNX if format == ExportFormat.ONNX else ArtifactName.PT
         _out_file = os.path.join(out_dir, export_model_name)
diff --git a/focoos/models/rtmo/processor.py b/focoos/models/rtmo/processor.py
index f33692d8..e4bbfabb 100644
--- a/focoos/models/rtmo/processor.py
+++ b/focoos/models/rtmo/processor.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -15,7 +15,7 @@
 
 
 class RTMOProcessor(Processor):
-    def __init__(self, config: RTMOConfig, image_size: Optional[int] = None):
+    def __init__(self, config: RTMOConfig, image_size: Optional[Union[int, Tuple[int, int]]] = None):
         super().__init__(config, image_size)
 
         self.score_thr = config.score_thr
@@ -84,7 +84,13 @@ def preprocess(
         else:
             if self.training:
                 raise ValueError("During training, inputs should be a list of DatasetEntry")
-            target_size = (self.image_size, self.image_size) if self.image_size is not None else None
+            if self.image_size is not None:
+                if isinstance(self.image_size, int):
+                    target_size = (self.image_size, self.image_size)
+                else:
+                    target_size = self.image_size
+            else:
+                target_size = None
             images_torch = self.get_torch_batch(
                 inputs,  # type: ignore
                 target_size=target_size,
@@ -132,8 +138,14 @@ def postprocess(
             h, w = int(size[0]), int(size[1])
             scale = False
             if self.image_size is not None:
-                scale_x = w / self.image_size
-                scale_y = h / self.image_size
+                if isinstance(self.image_size, int):
+                    # Square image - use same scale for both dimensions
+                    scale_x = w / self.image_size
+                    scale_y = h / self.image_size
+                else:
+                    # Non-square image - use different scales
+                    scale_x = w / self.image_size[1]  # width
+                    scale_y = h / self.image_size[0]  # height
                 scale = True
 
             # logger.debug(f"outputs.outputs.scores[i].shape: {outputs.outputs.scores[i].shape}")
diff --git a/focoos/ports.py b/focoos/ports.py
index c319d9aa..df96a479 100644
--- a/focoos/ports.py
+++ b/focoos/ports.py
@@ -1219,7 +1219,7 @@ class ModelInfo(DictClass):
     name: str
     model_family: ModelFamily
     classes: list[str]
-    im_size: int
+    im_size: Union[int, Tuple[int, int]]
     task: Task
     config: dict
     ref: Optional[str] = None
@@ -1260,12 +1260,22 @@ def from_json(cls, data: Union[str, dict]):
                 **{k: v for k, v in model_info_json["training_info"].items() if k in TrainingInfo.__dataclass_fields__}
             )
 
+        # Handle im_size: can be int (square) or list/tuple (height, width)
+        im_size_value = model_info_json["im_size"]
+        if isinstance(im_size_value, (list, tuple)):
+            im_size = tuple(im_size_value)
+        elif isinstance(im_size_value, int):
+            im_size = im_size_value
+        else:
+            # Try to convert to int for backward compatibility
+            im_size = int(im_size_value)
+
         model_info = cls(
             name=model_info_json["name"],
             ref=model_info_json.get("ref", None),
             model_family=ModelFamily(model_info_json["model_family"]),
             classes=model_info_json["classes"],
-            im_size=int(model_info_json["im_size"]),
+            im_size=im_size,
             status=ModelStatus(model_info_json.get("status")) if model_info_json.get("status") else None,
             task=Task(model_info_json["task"]),
             focoos_model=model_info_json.get("focoos_model", None),
diff --git a/focoos/processor/base_processor.py b/focoos/processor/base_processor.py
index b58ab57d..5183f351 100644
--- a/focoos/processor/base_processor.py
+++ b/focoos/processor/base_processor.py
@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Literal, Optional, Union
+from typing import Any, Literal, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -19,11 +19,13 @@ class Processor(ABC):
         training (bool): Flag indicating whether the processor is in training mode.
     """
 
-    def __init__(self, config: ModelConfig, image_size: Optional[int] = None):
+    def __init__(self, config: ModelConfig, image_size: Optional[Union[int, Tuple[int, int]]] = None):
         """Initialize the processor with the given configuration.
 
         Args:
             config (ModelConfig): Model configuration containing settings and parameters.
+            image_size (Optional[Union[int, Tuple[int, int]]]): Image size. If int, treated as square (size, size).
+                If tuple, treated as (height, width).
         """
         self.config = config
         self.training = False
diff --git a/focoos/processor/processor_manager.py b/focoos/processor/processor_manager.py
index 42f04940..d5286758 100644
--- a/focoos/processor/processor_manager.py
+++ b/focoos/processor/processor_manager.py
@@ -1,5 +1,5 @@
 import importlib
-from typing import Callable, Dict, Optional, Type
+from typing import Callable, Dict, Optional, Tuple, Type, Union
 
 from focoos.ports import ModelConfig, ModelFamily
 from focoos.processor.base_processor import Processor
@@ -30,7 +30,10 @@ def _ensure_family_registered(cls, model_family: ModelFamily):
 
     @classmethod
     def get_processor(
-        cls, model_family: ModelFamily, model_config: ModelConfig, image_size: Optional[int] = None
+        cls,
+        model_family: ModelFamily,
+        model_config: ModelConfig,
+        image_size: Optional[Union[int, Tuple[int, int]]] = None,
     ) -> Processor:
         """
         Get a processor instance for the given model family.
diff --git a/pyproject.toml b/pyproject.toml
index 514c80f5..ecdf45e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ requires-python = ">=3.10,<3.13"
 dependencies = [
     "requests",
     "Pillow~=10.2.0",
-    "supervision~=0.26.0",
+    "supervision~=0.27.0",
     "opencv-python~=4.11.0.86",
     "pydantic~=2.11.4",
     "pydantic-settings~=2.8.1",
diff --git a/uv.lock b/uv.lock
index a0581cb9..e47d0a4c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -677,7 +677,7 @@ wheels = [
 
 [[package]]
 name = "focoos"
-version = "0.22.0"
+version = "0.24.0"
 source = { editable = "." }
 dependencies = [
     { name = "colorama" },
@@ -783,7 +783,7 @@ requires-dist = [
     { name = "scipy", specifier = "~=1.14.1" },
     { name = "shapely", specifier = "~=2.1.0" },
     { name = "sniffio", marker = "extra == 'dev'", specifier = "~=1.3.1" },
-    { name = "supervision", specifier = "~=0.26.0" },
+    { name = "supervision", specifier = "~=0.27.0" },
     { name = "tensorboard", specifier = "~=2.19.0" },
     { name = "tensorrt", marker = "extra == 'tensorrt'", specifier = "==10.5.0" },
     { name = "torch", specifier = "~=2.7.1" },
@@ -3136,7 +3136,7 @@ wheels = [
 
 [[package]]
 name = "supervision"
-version = "0.26.0"
+version = "0.27.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "defusedxml" },
@@ -3149,9 +3149,9 @@ dependencies = [
     { name = "scipy" },
     { name = "tqdm" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/e9/502cb036a3aba19a4278f68a007958687ddb628b97f1646d4689b831525a/supervision-0.26.0.tar.gz", hash = "sha256:03801068ab55f75be10142772c3b1f68826b3d7af98ae341dfe6a474af299fcc", size = 175968, upload-time = "2025-07-16T00:40:52.826Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/df/685a25485c54b5ed8f7aeb35dd669a176391d5dc59204742f423d5f55406/supervision-0.27.0.tar.gz", hash = "sha256:b3fc9a0721bbeb03b23595bd8066dd263f3e27484a9fa727c2887c366c471c02", size = 181754, upload-time = "2025-11-16T18:07:25.7Z" }
 wheels = [
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 [[package]]

From 9a719daa3f6c68ed555c11830926912abd94c340 Mon Sep 17 00:00:00 2001
From: Ivan Murabito <ivan.murabito@focoos.ai>
Date: Thu, 11 Dec 2025 13:21:54 +0000
Subject: [PATCH 2/8] chore: update dependencies in pyproject.toml and uv.lock

- Updated `orjson` version from `3.10.18` to `3.11.5`.
- Added additional `tensorrt` dependencies: `tensorrt-cu12`, `tensorrt-cu12-bindings`, and `tensorrt-cu12-libs`.
- Updated `onnxruntime-gpu` and `onnxruntime` versions from `1.22.0` to `1.23.2`.
- Updated `optimum[onnxruntime]` version from `1.27.0` to `2.0.0`.
---
 pyproject.toml |  17 +-
 uv.lock        | 741 ++++++++-----------------------------------------
 2 files changed, 124 insertions(+), 634 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ecdf45e0..b65581f6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,12 +46,11 @@ dependencies = [
     "matplotlib~=3.10.1",
     "colorama~=0.4.6",
     "ipython",
-    "shapely~=2.1.0",
     "fvcore~=0.1.4",
     "pycocotools~=2.0.8",
     "faster_coco_eval~=1.6.6",
     "tensorboard~=2.19.0",
-    "orjson~=3.10.18",
+    "orjson~=3.11.5",
     "gradio~=5.31.0",
     "torch~=2.7.1",
     "torchvision~=0.22.0",
@@ -67,19 +66,23 @@ keywords = [
 ]
 
 [project.optional-dependencies]
-tensorrt = ["tensorrt==10.5.0"]
+tensorrt = [
+    "tensorrt==10.5.0",
+    "tensorrt-cu12==10.5.0",
+    "tensorrt-cu12-bindings==10.5.0",
+    "tensorrt-cu12-libs==10.5.0"]
 onnx = [
-    "onnxruntime-gpu==1.22.0",
+    "onnxruntime-gpu==1.23.2",
     "onnx>=1.18.0",
     "onnxscript~=0.5.1",
-    "optimum[onnxruntime]~=1.27.0",
+    "optimum[onnxruntime]~=2.0.0",
     "huggingface-hub>=0.34.0",
 ]
 onnx-cpu = [
-    "onnxruntime==1.22.0",
+    "onnxruntime==1.23.2",
     "onnx>=1.18.0",
     "onnxscript~=0.5.1",
-    "optimum[onnxruntime]~=1.27.0",
+    "optimum[onnxruntime]~=2.0.0",
     "huggingface-hub>=0.34.0",
 ]
 dev = [
diff --git a/uv.lock b/uv.lock
index e47d0a4c..5788989d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -41,97 +41,6 @@ wheels = [
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 ]
 
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From ea5fb0565ea99e97e0cfd52d188366de3ae42c72 Mon Sep 17 00:00:00 2001
From: Ivan Murabito <ivan.murabito@focoos.ai>
Date: Thu, 11 Dec 2025 13:44:17 +0000
Subject: [PATCH 3/8] feat: add resolution parameter to dataset mappers and
 augmentations

- Introduced an optional `resolution` parameter across various dataset mappers including `AutoDataset`, `ClassificationDatasetMapper`, `DetectionDatasetMapper`, `KeypointDatasetMapper`, and `SemanticDatasetMapper`.
- Updated the `MapDataset` class to expose the `resolution` property.
- Enhanced the handling of image resolution in the `FocoosModel` to utilize the new `resolution` parameter from augmentations if available.
---
 focoos/data/auto_dataset.py                      | 16 ++++++++++++----
 focoos/data/datasets/map_dataset.py              |  5 +++++
 .../mappers/classification_dataset_mapper.py     |  5 ++++-
 focoos/data/mappers/detection_dataset_mapper.py  |  5 ++++-
 focoos/data/mappers/keypoint.py                  |  5 ++++-
 focoos/data/mappers/mapper.py                    |  4 +++-
 focoos/data/mappers/semantic_dataset_mapper.py   |  5 ++++-
 focoos/models/focoos_model.py                    |  7 +++++++
 8 files changed, 43 insertions(+), 9 deletions(-)

diff --git a/focoos/data/auto_dataset.py b/focoos/data/auto_dataset.py
index 880a8ccf..45baa3bd 100644
--- a/focoos/data/auto_dataset.py
+++ b/focoos/data/auto_dataset.py
@@ -1,5 +1,5 @@
 import os
-from typing import List
+from typing import List, Optional, Tuple, Union
 
 from focoos.data.datasets.dict_dataset import DictDataset
 from focoos.data.datasets.map_dataset import MapDataset
@@ -85,6 +85,7 @@ def _load_mapper(
         self,
         augs: List[T.Transform],
         is_validation_split: bool,
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ) -> DatasetMapper:
         if self.task == Task.SEMSEG:
             return SemanticDatasetMapper(
@@ -92,12 +93,14 @@ def _load_mapper(
                 ignore_label=255,
                 augmentations=augs,
                 is_train=not is_validation_split,
+                resolution=resolution,
             )
         elif self.task == Task.DETECTION:
             return DetectionDatasetMapper(
                 image_format="RGB",
                 is_train=not is_validation_split,
                 augmentations=augs,
+                resolution=resolution,
             )
         elif self.task == Task.INSTANCE_SEGMENTATION:
             return DetectionDatasetMapper(
@@ -105,18 +108,21 @@ def _load_mapper(
                 is_train=not is_validation_split,
                 augmentations=augs,
                 use_instance_mask=True,
+                resolution=resolution,
             )
         elif self.task == Task.CLASSIFICATION:
             return ClassificationDatasetMapper(
                 image_format="RGB",
                 is_train=not is_validation_split,
                 augmentations=augs,
+                resolution=resolution,
             )
         elif self.task == Task.KEYPOINT:
             return KeypointDatasetMapper(
                 image_format="RGB",
                 augmentations=augs,
                 is_train=not is_validation_split,
+                resolution=resolution,
                 # keypoint_hflip_indices=np.array(keypoint_hflip_indices),
             )
         else:
@@ -144,14 +150,15 @@ def get_split(
         self,
         augs: List[T.Transform],
         split: DatasetSplitType = DatasetSplitType.TRAIN,
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ) -> MapDataset:
         """
         Generate a dataset for a given dataset name with optional augmentations.
 
         Parameters:
-            short_edge_length (int): The length of the shorter edge of the images.
-            max_size (int): The maximum size of the images.
-            extra_augs (List[Transform]): Extra augmentations to apply.
+            augs (List[Transform]): Augmentations to apply.
+            split (DatasetSplitType): Dataset split type (TRAIN or VAL).
+            resolution (Optional[Union[int, Tuple[int, int]]]): Image resolution used for augmentations.
 
         Returns:
             MapDataset: A DictDataset with DatasetMapper for training.
@@ -164,5 +171,6 @@ def get_split(
             mapper=self._load_mapper(
                 augs=augs,
                 is_validation_split=(split == DatasetSplitType.VAL),
+                resolution=resolution,
             ),
         )  # type: ignore
diff --git a/focoos/data/datasets/map_dataset.py b/focoos/data/datasets/map_dataset.py
index f8d8ad27..c50cc064 100644
--- a/focoos/data/datasets/map_dataset.py
+++ b/focoos/data/datasets/map_dataset.py
@@ -78,6 +78,11 @@ def name(self):
     def task(self):
         return self.dataset.metadata.task
 
+    @property
+    def resolution(self):
+        """Get the resolution from the mapper if available."""
+        return getattr(self.mapper, "resolution", None)
+
     def preview(self, index=None, use_augmentations=True):
         if not use_augmentations:
             current_augmentations = self.mapper.augmentations
diff --git a/focoos/data/mappers/classification_dataset_mapper.py b/focoos/data/mappers/classification_dataset_mapper.py
index 1f76ed70..01941477 100644
--- a/focoos/data/mappers/classification_dataset_mapper.py
+++ b/focoos/data/mappers/classification_dataset_mapper.py
@@ -1,6 +1,6 @@
 import copy
 from dataclasses import dataclass
-from typing import Optional, Sequence, Union
+from typing import Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -40,17 +40,20 @@ def __init__(
         *,
         augmentations: Sequence[Union[A.Augmentation, T.Transform]],
         image_format: str = "RGB",
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ):
         """
         Args:
             is_train: Whether it's used in training or inference
             augmentations: A list of augmentations or transforms to apply
             image_format: An image format supported by PIL and OpenCV
+            resolution: Image resolution used for augmentations.
         """
         super().__init__(
             is_train=is_train,
             augmentations=augmentations,  # type: ignore
             image_format=image_format,
+            resolution=resolution,
         )
         self.logger = get_logger("ClassificationDsMapper")
         mode = "train" if is_train else "val"
diff --git a/focoos/data/mappers/detection_dataset_mapper.py b/focoos/data/mappers/detection_dataset_mapper.py
index d6ca1043..73acc7a0 100644
--- a/focoos/data/mappers/detection_dataset_mapper.py
+++ b/focoos/data/mappers/detection_dataset_mapper.py
@@ -1,7 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 # Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
 import copy
-from typing import List, Optional, Sequence, Union
+from typing import List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -43,6 +43,7 @@ def __init__(
         use_keypoint: bool = False,
         keypoint_hflip_indices: Optional[np.ndarray] = None,
         recompute_boxes: bool = False,
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ):
         """
         Args:
@@ -56,6 +57,7 @@ def __init__(
                 proposals from dataset_dict and keep the top k proposals for each image.
             recompute_boxes: whether to overwrite bounding box annotations
                 by computing tight bounding boxes from instance mask annotations.
+            resolution: Image resolution used for augmentations.
         """
         if recompute_boxes:
             assert use_instance_mask, "recompute_boxes requires instance masks"
@@ -67,6 +69,7 @@ def __init__(
         self.use_keypoint           = use_keypoint
         self.keypoint_hflip_indices = keypoint_hflip_indices
         self.recompute_boxes        = recompute_boxes
+        self.resolution             = resolution
         # fmt: on
         logger = get_logger("DetectionMapper")
         mode = "train" if is_train else "val"
diff --git a/focoos/data/mappers/keypoint.py b/focoos/data/mappers/keypoint.py
index f5e9ca5d..331119f1 100644
--- a/focoos/data/mappers/keypoint.py
+++ b/focoos/data/mappers/keypoint.py
@@ -1,7 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 # Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
 import copy
-from typing import Optional, Sequence, Union
+from typing import Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -42,6 +42,7 @@ def __init__(
         augmentations: Sequence[Union[A.Augmentation, T.Transform]],
         image_format: str,
         keypoint_hflip_indices: Optional[np.ndarray] = None,
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ):
         """
         Args:
@@ -55,6 +56,7 @@ def __init__(
                 proposals from dataset_dict and keep the top k proposals for each image.
             recompute_boxes: whether to overwrite bounding box annotations
                 by computing tight bounding boxes from instance mask annotations.
+            resolution: Image resolution used for augmentations.
         """
         # fmt: off
         self.is_train               = is_train
@@ -63,6 +65,7 @@ def __init__(
         self.use_keypoint           = True
         self.keypoint_hflip_indices = keypoint_hflip_indices
         self.recompute_boxes        = False
+        self.resolution             = resolution
         # fmt: on
         logger = get_logger("KeypointMapper")
         mode = "train" if is_train else "val"
diff --git a/focoos/data/mappers/mapper.py b/focoos/data/mappers/mapper.py
index a0ccea11..f952978a 100644
--- a/focoos/data/mappers/mapper.py
+++ b/focoos/data/mappers/mapper.py
@@ -1,5 +1,5 @@
 import logging
-from typing import List, Union
+from typing import List, Optional, Tuple, Union
 
 from focoos.data.transforms import augmentation as A
 from focoos.data.transforms import transform as T
@@ -14,10 +14,12 @@ def __init__(
         *,
         augmentations: List[Union[A.Augmentation, T.Transform]],
         image_format: str,
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ):
         self.is_train = is_train
         self.augmentations = A.AugmentationList(augmentations)
         self.image_format = image_format
+        self.resolution = resolution
 
     def check_image_size(self, dataset_dict, image):
         expected_wh = None
diff --git a/focoos/data/mappers/semantic_dataset_mapper.py b/focoos/data/mappers/semantic_dataset_mapper.py
index 7f05fd47..7998ed04 100644
--- a/focoos/data/mappers/semantic_dataset_mapper.py
+++ b/focoos/data/mappers/semantic_dataset_mapper.py
@@ -1,7 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
 import copy
 from dataclasses import dataclass
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -44,6 +44,7 @@ def __init__(
         augmentations,
         image_format="RGB",
         ignore_label=255,
+        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ):
         """
         Args:
@@ -51,11 +52,13 @@ def __init__(
             augmentations: a list of augmentations or deterministic transforms to apply
             image_format: an image format supported by :func:`detection_utils.read_image`.
             ignore_label: the label that is ignored to evaluation
+            resolution: Image resolution used for augmentations.
         """
         self.is_train = is_train
         self.augmentations = augmentations
         self.img_format = image_format
         self.ignore_label = ignore_label
+        self.resolution = resolution
 
         logger = get_logger("SemSegMapper")
         mode = "train" if is_train else "val"
diff --git a/focoos/models/focoos_model.py b/focoos/models/focoos_model.py
index 8458e015..0d5d74a3 100644
--- a/focoos/models/focoos_model.py
+++ b/focoos/models/focoos_model.py
@@ -171,6 +171,7 @@ def _setup_model_for_training(self, train_args: TrainerArgs, data_train: MapData
             device = "mps"
         else:
             device = "cpu"
+
         self.model_info.ref = None
 
         self.model_info.train_args = train_args  # type: ignore
@@ -201,6 +202,12 @@ def _setup_model_for_training(self, train_args: TrainerArgs, data_train: MapData
             self.model_info.config["num_keypoints"] = len(data_train.dataset.metadata.keypoints)
         if data_train.dataset.metadata.keypoints_skeleton is not None:
             self.model_info.config["skeleton"] = data_train.dataset.metadata.keypoints_skeleton
+
+        # Set im_size from resolution in augmentations if available
+        resolution = getattr(data_train, "resolution", None) or getattr(data_val, "resolution", None)
+        if resolution is not None:
+            self.model_info.im_size = resolution
+
         self._reload_model()
         self.model_info.name = train_args.run_name.strip()
         self.processor = ProcessorManager.get_processor(self.model_info.model_family, self.model_info.config)  # type: ignore

From 0910071374f09d772fdbcaa89d261a45f7922294 Mon Sep 17 00:00:00 2001
From: Ivan Murabito <ivan.murabito@focoos.ai>
Date: Thu, 11 Dec 2025 14:46:35 +0000
Subject: [PATCH 4/8] refactor: update dataset augmentation handling to use new
 structure

- Modified the dataset split methods across various files to directly use the `DatasetAugmentations` object instead of calling `get_augmentations()`.
- Updated relevant documentation and examples to reflect the new usage pattern for augmentations.
- Ensured consistency in how augmentations are applied in training and validation processes.
---
 README.md                             |  4 ++--
 docs/training.md                      |  4 ++--
 focoos/cli/commands/train.py          |  4 ++--
 focoos/cli/commands/val.py            |  2 +-
 focoos/data/auto_dataset.py           | 17 ++++++++++------
 focoos/data/default_aug.py            | 26 ++++++++++++++++--------
 focoos/models/fai_detr/config.py      |  4 ++--
 focoos/models/fai_detr/processor.py   |  1 -
 focoos/models/focoos_model.py         | 29 ++++++++++++++-------------
 ops/test_training.py                  |  4 ++--
 ops/test_validation.py                |  2 +-
 scripts/training.py                   |  4 ++--
 scripts/validation.py                 |  2 +-
 tutorials/convert_dataset_ninja.ipynb |  4 ++--
 tutorials/hub.ipynb                   |  4 ++--
 tutorials/training.ipynb              |  4 ++--
 16 files changed, 65 insertions(+), 50 deletions(-)

diff --git a/README.md b/README.md
index 474b43bc..0edf2fc5 100644
--- a/README.md
+++ b/README.md
@@ -69,8 +69,8 @@ layout = DatasetLayout.ROBOFLOW_COCO
 auto_dataset = AutoDataset(dataset_name=ds_name, task=task, layout=layout)
 
 train_augs, val_augs = get_default_by_task(task, 640, advanced=False)
-train_dataset = auto_dataset.get_split(augs=train_augs.get_augmentations(), split=DatasetSplitType.TRAIN)
-valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)
+valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
 
 model = ModelManager.get("fai-detr-l-obj365")
diff --git a/docs/training.md b/docs/training.md
index 096f6460..bbbea0c0 100644
--- a/docs/training.md
+++ b/docs/training.md
@@ -66,10 +66,10 @@ layout = dataset.layout  # see ports.DatasetLayout for more information
 auto_dataset = AutoDataset(dataset_name=dataset_path, task=task, layout=layout)
 
 # Square resolution (512x512)
-augs = DatasetAugmentations(resolution=512).get_augmentations()
+augs = DatasetAugmentations(resolution=512)
 
 # Non-square resolution (640x480, height x width)
-augs = DatasetAugmentations(resolution=(640, 480)).get_augmentations()
+augs = DatasetAugmentations(resolution=(640, 480))
 
 train_dataset = auto_dataset.get_split(augs=augs, split=DatasetSplitType.TRAIN)
 valid_dataset = auto_dataset.get_split(augs=augs, split=DatasetSplitType.VAL)
diff --git a/focoos/cli/commands/train.py b/focoos/cli/commands/train.py
index 3a2377c0..7a382d0e 100644
--- a/focoos/cli/commands/train.py
+++ b/focoos/cli/commands/train.py
@@ -343,8 +343,8 @@ def train_command(
         # Get augmentations
         logger.info("🎨 Setting up data augmentations")
         train_augs, val_augs = get_default_by_task(model.task, resolution=im_size or model.model_info.im_size)
-        train_dataset = auto_dataset.get_split(augs=train_augs.get_augmentations(), split=DatasetSplitType.TRAIN)
-        valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+        train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)
+        valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
         # Configure training arguments
         logger.info("⚙️ Configuring training parameters")
diff --git a/focoos/cli/commands/val.py b/focoos/cli/commands/val.py
index 08538d7a..00eb8a52 100644
--- a/focoos/cli/commands/val.py
+++ b/focoos/cli/commands/val.py
@@ -318,7 +318,7 @@ def val_command(
         # Get validation augmentations
         logger.info("🎨 Setting up validation augmentations")
         _, val_augs = get_default_by_task(task=model.model_info.task, resolution=im_size or model.model_info.im_size)
-        valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+        valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
         # Configure validation arguments
         logger.info("⚙️ Configuring validation parameters")
diff --git a/focoos/data/auto_dataset.py b/focoos/data/auto_dataset.py
index 45baa3bd..f4cf5705 100644
--- a/focoos/data/auto_dataset.py
+++ b/focoos/data/auto_dataset.py
@@ -3,11 +3,13 @@
 
 from focoos.data.datasets.dict_dataset import DictDataset
 from focoos.data.datasets.map_dataset import MapDataset
+from focoos.data.default_aug import DatasetAugmentations
 from focoos.data.mappers.classification_dataset_mapper import ClassificationDatasetMapper
 from focoos.data.mappers.detection_dataset_mapper import DetectionDatasetMapper
 from focoos.data.mappers.keypoint import KeypointDatasetMapper
 from focoos.data.mappers.mapper import DatasetMapper
 from focoos.data.mappers.semantic_dataset_mapper import SemanticDatasetMapper
+from focoos.data.transforms import augmentation as A
 from focoos.data.transforms import transform as T
 from focoos.ports import (
     DATASETS_DIR,
@@ -83,7 +85,7 @@ def _load_split(self, dataset_name: str, split: DatasetSplitType) -> DictDataset
 
     def _load_mapper(
         self,
-        augs: List[T.Transform],
+        augs: List[Union[A.Augmentation, T.Transform]],
         is_validation_split: bool,
         resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ) -> DatasetMapper:
@@ -148,17 +150,16 @@ def _get_split_path(self, dataset_root: str, split_type: DatasetSplitType) -> st
 
     def get_split(
         self,
-        augs: List[T.Transform],
+        augs: DatasetAugmentations,
         split: DatasetSplitType = DatasetSplitType.TRAIN,
-        resolution: Optional[Union[int, Tuple[int, int]]] = None,
     ) -> MapDataset:
         """
         Generate a dataset for a given dataset name with optional augmentations.
 
         Parameters:
-            augs (List[Transform]): Augmentations to apply.
+            augs (DatasetAugmentations): Augmentations configuration.
+                Resolution will be automatically extracted from this object.
             split (DatasetSplitType): Dataset split type (TRAIN or VAL).
-            resolution (Optional[Union[int, Tuple[int, int]]]): Image resolution used for augmentations.
 
         Returns:
             MapDataset: A DictDataset with DatasetMapper for training.
@@ -166,10 +167,14 @@ def get_split(
         dict_split = self._load_split(dataset_name=self.dataset_name, split=split)
         assert dict_split.metadata.num_classes > 0, "Number of dataset classes must be greater than 0"
 
+        # Extract resolution and augmentations from DatasetAugmentations
+        resolution = augs.resolution
+        augs_list = augs.get_augmentations()
+
         return MapDataset(
             dataset=dict_split,
             mapper=self._load_mapper(
-                augs=augs,
+                augs=augs_list,
                 is_validation_split=(split == DatasetSplitType.VAL),
                 resolution=resolution,
             ),
diff --git a/focoos/data/default_aug.py b/focoos/data/default_aug.py
index 4b506be7..b1025425 100644
--- a/focoos/data/default_aug.py
+++ b/focoos/data/default_aug.py
@@ -153,14 +153,24 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
             augs.append(A.RandomAspectRatio(aspect_ratio=self.aspect_ratio))
 
         ### Add Resizing augmentations based on configuration
-        min_scale, max_scale = 2 ** (-self.scale_ratio), 2**self.scale_ratio
-        augs.append(
-            A.ResizeShortestEdge(
-                short_edge_length=[int(x * resolution_value) for x in [min_scale, max_scale]],
-                sample_style="range",
-                max_size=self.max_size,
+        # For non-square resolutions, use Resize directly to get exact dimensions
+        # For square resolutions, use ResizeShortestEdge with scale augmentation
+        is_non_square = isinstance(self.resolution, tuple) and self.resolution[0] != self.resolution[1]
+
+        if is_non_square:
+            # Non-square resolution: use direct Resize to get exact (height, width)
+            # Note: scale_ratio is ignored for non-square resolutions
+            augs.append(A.Resize(shape=resolution_tuple))
+        else:
+            # Square resolution: use ResizeShortestEdge with scale augmentation
+            min_scale, max_scale = 2 ** (-self.scale_ratio), 2**self.scale_ratio
+            augs.append(
+                A.ResizeShortestEdge(
+                    short_edge_length=[int(x * resolution_value) for x in [min_scale, max_scale]],
+                    sample_style="range",
+                    max_size=self.max_size,
+                )
             )
-        )
 
         ### Add rotation augmentations if configured
         if self.rotation > 0:
@@ -173,7 +183,7 @@ def get_augmentations(self, img_format="RGB", task: Optional[Task] = None) -> Li
                 crop_range = (self.crop_size, self.crop_size)
             else:
                 crop_range = resolution_tuple
-            augs.append(A.RandomCrop(crop_type="absolute_range", crop_size=crop_range))
+            augs.append(A.RandomCrop(crop_type="absolute", crop_size=crop_range))
 
         return augs
 
diff --git a/focoos/models/fai_detr/config.py b/focoos/models/fai_detr/config.py
index d0cb9d9c..e43a573c 100644
--- a/focoos/models/fai_detr/config.py
+++ b/focoos/models/fai_detr/config.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import List
+from typing import List, Optional
 
 from focoos.nn.backbone.base import BackboneConfig
 from focoos.ports import ModelConfig
@@ -11,7 +11,7 @@ class DETRConfig(ModelConfig):
     num_classes: int
 
     num_queries: int = 300
-    resolution: int = 640
+    resolution: Optional[int] = None
 
     # Image detector configuration
     pixel_mean: List[float] = field(default_factory=lambda: [123.675, 116.28, 103.53])
diff --git a/focoos/models/fai_detr/processor.py b/focoos/models/fai_detr/processor.py
index 2a7f11a8..ed68666a 100644
--- a/focoos/models/fai_detr/processor.py
+++ b/focoos/models/fai_detr/processor.py
@@ -62,7 +62,6 @@ def __init__(self, config: DETRConfig, image_size: Optional[Union[int, Tuple[int
         super().__init__(config, image_size)
         self.top_k = config.top_k
         self.threshold = config.threshold
-        self.resolution = config.resolution
 
     def preprocess(
         self,
diff --git a/focoos/models/focoos_model.py b/focoos/models/focoos_model.py
index 0d5d74a3..dd6c95ac 100644
--- a/focoos/models/focoos_model.py
+++ b/focoos/models/focoos_model.py
@@ -251,22 +251,23 @@ def train(self, args: TrainerArgs, data_train: MapDataset, data_val: MapDataset,
                 args=(args, data_train, data_val, self.model, self.processor, self.model_info, hub),
             )
 
-            logger.info("Training done, resuming main process.")
-            # here i should restore the best model and config since in DDP it is not updated
-            final_folder = os.path.join(args.output_dir, args.run_name)
-            model_path = os.path.join(final_folder, ArtifactName.WEIGHTS)
-            metadata_path = os.path.join(final_folder, ArtifactName.INFO)
-
-            if not os.path.exists(model_path):
-                raise FileNotFoundError(f"Training did not end correctly, model file not found at {model_path}")
-            if not os.path.exists(metadata_path):
-                raise FileNotFoundError(f"Training did not end correctly, metadata file not found at {metadata_path}")
-            self.model_info = ModelInfo.from_json(metadata_path)
-
-            logger.info(f"Reloading weights from {self.model_info.weights_uri}")
-            self._reload_model()
         else:
             run_train(args, data_train, data_val, self.model, self.processor, self.model_info, hub)
+        logger.info("Training done, resuming main process.")
+        # here i should restore the best model and config since in DDP it is not updated
+        final_folder = os.path.join(args.output_dir, args.run_name)
+        model_path = os.path.join(final_folder, ArtifactName.WEIGHTS)
+        metadata_path = os.path.join(final_folder, ArtifactName.INFO)
+
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"Training did not end correctly, model file not found at {model_path}")
+        if not os.path.exists(metadata_path):
+            raise FileNotFoundError(f"Training did not end correctly, metadata file not found at {metadata_path}")
+        self.model_info = ModelInfo.from_json(metadata_path)
+
+        self._reload_model()
+        self.model.eval()
+        self.processor.eval()
 
     def eval(self, args: TrainerArgs, data_test: MapDataset, save_json: bool = True):
         """evaluate the model on the provided test dataset.
diff --git a/ops/test_training.py b/ops/test_training.py
index 1dbb97c4..44800490 100644
--- a/ops/test_training.py
+++ b/ops/test_training.py
@@ -81,8 +81,8 @@ def train(model_name: str, iter: int):
     train_augs.crop_size = resolution
     train_augs.crop = True
 
-    train_dataset = auto_dataset.get_split(augs=train_augs.get_augmentations(), split=DatasetSplitType.TRAIN)
-    valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+    train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)
+    valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
     # Get again the model with the correct number of classes
     model = ModelManager.get(model_name, num_classes=train_dataset.dataset.metadata.num_classes)
diff --git a/ops/test_validation.py b/ops/test_validation.py
index 33486959..1e8016c3 100644
--- a/ops/test_validation.py
+++ b/ops/test_validation.py
@@ -34,7 +34,7 @@ def train(model_name: str):
 
     # Get default augmentations for the specified task
     train_augs, val_augs = get_default_by_task(task, resolution)
-    valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+    valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
     _temp_dir = tempfile.mkdtemp()
     out_dir = os.path.join(_temp_dir, "output")
diff --git a/scripts/training.py b/scripts/training.py
index 53f9d9f4..57e075fc 100644
--- a/scripts/training.py
+++ b/scripts/training.py
@@ -76,8 +76,8 @@ def main():
 
     # Get default augmentations for the specified task
     train_augs, val_augs = get_default_by_task(task, resolution, advanced=args.advanced_aug)
-    train_dataset = auto_dataset.get_split(augs=train_augs.get_augmentations(), split=DatasetSplitType.TRAIN)
-    valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+    train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)
+    valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
     # Initialize model
     model = ModelManager.get(args.model_name, num_classes=train_dataset.dataset.metadata.num_classes)
diff --git a/scripts/validation.py b/scripts/validation.py
index 4fc9e726..496100a8 100644
--- a/scripts/validation.py
+++ b/scripts/validation.py
@@ -69,7 +69,7 @@ def main():
 
     # Get default augmentations for the specified task
     train_augs, val_augs = get_default_by_task(task, resolution, advanced=args.advanced_aug)
-    valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)
+    valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
 
     # Initialize model
     model = ModelManager.get(args.model_name)
diff --git a/tutorials/convert_dataset_ninja.ipynb b/tutorials/convert_dataset_ninja.ipynb
index cb80a691..31448701 100644
--- a/tutorials/convert_dataset_ninja.ipynb
+++ b/tutorials/convert_dataset_ninja.ipynb
@@ -82,8 +82,8 @@
     "\n",
     "augs = DatasetAugmentations(resolution=512)\n",
     "\n",
-    "train_dataset = auto_dataset.get_split(augs=augs.get_augmentations(), split=DatasetSplitType.TRAIN)\n",
-    "valid_dataset = auto_dataset.get_split(augs=augs.get_augmentations(), split=DatasetSplitType.VAL)"
+    "train_dataset = auto_dataset.get_split(augs=augs, split=DatasetSplitType.TRAIN)\n",
+    "valid_dataset = auto_dataset.get_split(augs=augs, split=DatasetSplitType.VAL)"
    ]
   },
   {
diff --git a/tutorials/hub.ipynb b/tutorials/hub.ipynb
index 3524cbb8..20c8b83f 100644
--- a/tutorials/hub.ipynb
+++ b/tutorials/hub.ipynb
@@ -194,8 +194,8 @@
     "    auto_dataset = AutoDataset(dataset_name=dataset_path, task=remote_dataset.task, layout=remote_dataset.layout)\n",
     "\n",
     "    train_augs, val_augs = get_default_by_task(remote_dataset.task, 640, advanced=False)\n",
-    "    train_dataset = auto_dataset.get_split(augs=train_augs.get_augmentations(), split=DatasetSplitType.TRAIN)\n",
-    "    valid_dataset = auto_dataset.get_split(augs=val_augs.get_augmentations(), split=DatasetSplitType.VAL)\n",
+    "    train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)\n",
+    "    valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)\n",
     "\n",
     "    model = ModelManager.get(\"fai-detr-l-obj365\")\n",
     "\n",
diff --git a/tutorials/training.ipynb b/tutorials/training.ipynb
index e0c3c3ce..faaeca93 100644
--- a/tutorials/training.ipynb
+++ b/tutorials/training.ipynb
@@ -227,8 +227,8 @@
     "# Optionally, you can also get the default augmentations for the task\n",
     "# train_augs, valid_augs = get_default_by_task(task, 512)\n",
     "\n",
-    "train_dataset = auto_dataset.get_split(augs=train_augs.get_augmentations(), split=DatasetSplitType.TRAIN)\n",
-    "valid_dataset = auto_dataset.get_split(augs=valid_augs.get_augmentations(), split=DatasetSplitType.VAL)"
+    "train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)\n",
+    "valid_dataset = auto_dataset.get_split(augs=valid_augs, split=DatasetSplitType.VAL)"
    ]
   },
   {

From 04fe628c143396679226ea88fb683011abe8f3d7 Mon Sep 17 00:00:00 2001
From: Ivan Murabito <ivan.murabito@focoos.ai>
Date: Thu, 11 Dec 2025 15:31:28 +0000
Subject: [PATCH 5/8] chore: add shapely dependency to pyproject.toml

- Included `shapely` version `2.1.2` in the project dependencies to support geometric operations.
---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index b65581f6..2cb88a5d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,7 @@ dependencies = [
     "torch~=2.7.1",
     "torchvision~=0.22.0",
     "typer>=0.16.0",
+    "shapely~=2.1.2",
 ]
 
 authors = [{ name = "focoos.ai", email = "info@focoos.ai" }]

From f535d24bfa6834ee40c6da17fe4fe5a0a259eabd Mon Sep 17 00:00:00 2001
From: Ivan Murabito <ivan.murabito@focoos.ai>
Date: Thu, 11 Dec 2025 15:42:13 +0000
Subject: [PATCH 6/8] refactor: update image size type in RemoteModelInfo

- Changed the `im_size` attribute in `RemoteModelInfo` to accept both `int` and `Tuple[int, int]` for more flexible image size handling.
- Updated related documentation to reflect the new type definition.
---
 focoos/hub/remote_model.py | 2 +-
 focoos/ports.py            | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/focoos/hub/remote_model.py b/focoos/hub/remote_model.py
index aaa07193..2b30bc17 100644
--- a/focoos/hub/remote_model.py
+++ b/focoos/hub/remote_model.py
@@ -44,7 +44,7 @@
 from focoos.utils.metrics import MetricsVisualizer, parse_metrics
 from focoos.utils.vision import annotate_image, image_loader
 
-logger = get_logger()
+logger = get_logger(name="RemoteModel")
 
 
 class RemoteModel:
diff --git a/focoos/ports.py b/focoos/ports.py
index df96a479..d421f30f 100644
--- a/focoos/ports.py
+++ b/focoos/ports.py
@@ -272,7 +272,7 @@ class RemoteModelInfo(PydanticBase):
         metrics (Optional[dict]): Performance metrics of the model (e.g., mAP, accuracy).
         latencies (Optional[list[dict]]): Inference latency measurements across different configurations.
         classes (Optional[list[str]]): List of class names the model can detect or segment.
-        im_size (Optional[int]): Input image size the model expects.
+        im_size (Optional[Union[int, Tuple[int, int]]]): Input image size the model expects.
         training_info (Optional[TrainingInfo]): Information about the training process.
         location (Optional[str]): Storage location of the model.
         dataset (Optional[DatasetPreview]): Information about the dataset used for training.
@@ -292,7 +292,7 @@ class RemoteModelInfo(PydanticBase):
     model_family: Optional[str] = None
     metrics: Optional[dict] = None
     classes: Optional[list[str]] = None
-    im_size: Optional[int] = None
+    im_size: Optional[Union[int, Tuple[int, int]]] = None
     training_info: Optional[TrainingInfo] = None
     dataset: Optional[DatasetPreview] = None
     hyperparameters: Optional[dict] = None

From 14367f2a1a0d2323942be23450e9640e98bf0d69 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 11 Dec 2025 16:02:14 +0000
Subject: [PATCH 7/8] Refactor: Pass im_size to ProcessorManager

Co-authored-by: ivan.murabito <ivan.murabito@focoos.ai>
---
 focoos/models/focoos_model.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/focoos/models/focoos_model.py b/focoos/models/focoos_model.py
index dd6c95ac..93389030 100644
--- a/focoos/models/focoos_model.py
+++ b/focoos/models/focoos_model.py
@@ -210,7 +210,11 @@ def _setup_model_for_training(self, train_args: TrainerArgs, data_train: MapData
 
         self._reload_model()
         self.model_info.name = train_args.run_name.strip()
-        self.processor = ProcessorManager.get_processor(self.model_info.model_family, self.model_info.config)  # type: ignore
+        self.processor = ProcessorManager.get_processor(
+            self.model_info.model_family,
+            self.model_info.config,  # type: ignore
+            self.model_info.im_size,
+        )
         self.model = self.model.train()
         assert self.model_info.task == data_train.dataset.metadata.task, "Task mismatch between model and dataset."
 

From 915e7adc335597bd6980c2ca679719d8e7e08d5e Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 11 Dec 2025 16:05:28 +0000
Subject: [PATCH 8/8] feat: Allow non-square image sizes for end-to-end
 benchmark

Co-authored-by: ivan.murabito <ivan.murabito@focoos.ai>
---
 focoos/models/focoos_model.py | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/focoos/models/focoos_model.py b/focoos/models/focoos_model.py
index 93389030..0e99ed8d 100644
--- a/focoos/models/focoos_model.py
+++ b/focoos/models/focoos_model.py
@@ -720,7 +720,9 @@ def benchmark(
         metrics = model.benchmark(size=size, iterations=iterations)
         return metrics
 
-    def end2end_benchmark(self, iterations: int = 50, size: Optional[int] = None) -> LatencyMetrics:
+    def end2end_benchmark(
+        self, iterations: int = 50, size: Optional[Union[int, Tuple[int, int]]] = None
+    ) -> LatencyMetrics:
         """Benchmark the complete end-to-end inference pipeline.
 
         This method measures the full inference latency including preprocessing,
@@ -736,6 +738,15 @@ def end2end_benchmark(self, iterations: int = 50, size: Optional[int] = None) ->
         """
         if size is None:
             size = self.model_info.im_size
+        if isinstance(size, (tuple, list)):
+            if len(size) != 2:
+                raise ValueError("Size tuples must be (height, width)")
+            height, width = (int(size[0]), int(size[1]))
+        elif isinstance(size, int):
+            height = width = int(size)
+        else:
+            raise TypeError("size must be an int or tuple/list of two ints")
+        normalized_size = (height, width)
         if self.model.device.type == "cpu":
             device_name = get_cpu_name()
         else:
@@ -744,9 +755,12 @@ def end2end_benchmark(self, iterations: int = 50, size: Optional[int] = None) ->
             model = self.model.cuda()
         except Exception:
             logger.warning("Unable to use CUDA")
-        logger.info(f"⏱️ Benchmarking End-to-End latency on {device_name} ({self.model.device}), size: {size}x{size}..")
+        logger.info(
+            f"⏱️ Benchmarking End-to-End latency on {device_name} ({self.model.device}), "
+            f"size: {height}x{width}.."
+        )
         # warmup
-        data = 128 * torch.randn(1, 3, size, size).to(model.device)
+        data = 128 * torch.randn(1, 3, height, width).to(model.device)
 
         durations = []
         for _ in range(iterations):
@@ -766,7 +780,7 @@ def end2end_benchmark(self, iterations: int = 50, size: Optional[int] = None) ->
             max=round(durations.max().astype(float), 3),
             min=round(durations.min().astype(float), 3),
             std=round(durations.std().astype(float), 3),
-            im_size=size,
+            im_size=normalized_size,
             device=str(self.model.device),
         )
         logger.info(f"🔥 FPS: {metrics.fps} Mean latency: {metrics.mean} ms ")