diff --git a/tests/scripts/run_performance.sh b/tests/scripts/run_performance.sh
index bd19f0a398f..835163d6124 100755
--- a/tests/scripts/run_performance.sh
+++ b/tests/scripts/run_performance.sh
@@ -23,6 +23,10 @@ run_perf_models_other() {
         env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/yolov4/tests/test_perf_yolo.py -m $test_marker
 
         env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/wormhole/distilbert/tests/test_perf_distilbert.py -m $test_marker
+
+        #yolov11
+        env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/experimental/yolov11/tests/test_yolov11.py -m $test_marker
+
     fi
 
     env pytest -n auto tests/ttnn/integration_tests/bert/test_performance.py -m $test_marker
@@ -133,6 +137,8 @@ run_device_perf_models() {
         env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/yolov4/tests/ -m $test_marker
 
         env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/wormhole/distilbert/tests -m $test_marker
+
+        env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/experimental/yolov11/tests/ -m $test_marker
     fi
 
     ## Merge all the generated reports
diff --git a/tests/scripts/run_python_model_tests.sh b/tests/scripts/run_python_model_tests.sh
index 576ef139fc7..0506dfbb2a8 100755
--- a/tests/scripts/run_python_model_tests.sh
+++ b/tests/scripts/run_python_model_tests.sh
@@ -43,6 +43,9 @@ run_python_model_tests_wormhole_b0() {
     # Mamba
     WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest -svv models/demos/wormhole/mamba/tests/test_residual_block.py -k "pretrained_weight_false"
 
+    # Yolov11
+    WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest tests/ttnn/integration_tests/yolov11/test_ttnn_yolo_v11.py
+
     # Llama3.1-8B
     llama8b=/mnt/MLPerf/tt_dnn-models/llama/Meta-Llama-3.1-8B-Instruct/
     # Llama3.2-1B
diff --git a/tests/scripts/single_card/run_single_card_demo_tests.sh b/tests/scripts/single_card/run_single_card_demo_tests.sh
index e7a8e492122..499e432e71f 100755
--- a/tests/scripts/single_card/run_single_card_demo_tests.sh
+++ b/tests/scripts/single_card/run_single_card_demo_tests.sh
@@ -61,6 +61,9 @@ run_common_func_tests() {
   #RoBERTa
   pytest --disable-warnings models/demos/roberta/demo/demo.py --timeout 600; fail+=$?
 
+  # Yolov11
+  WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest --disable-warnings models/experimental/yolov11/demo/demo.py --timeout 600; fail+=$?
+
   return $fail
 }
 
diff --git a/tests/ttnn/integration_tests/yolov11/test_ttnn_yolo_v11.py b/tests/ttnn/integration_tests/yolov11/test_ttnn_yolo_v11.py
new file mode 100644
index 00000000000..f150f746391
--- /dev/null
+++ b/tests/ttnn/integration_tests/yolov11/test_ttnn_yolo_v11.py
@@ -0,0 +1,776 @@
+# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from pathlib import Path
+import os
+import pytest
+import sys
+import ttnn
+import torch.nn as nn
+import torch
+from tests.ttnn.utils_for_testing import assert_with_pcc
+from models.experimental.yolov11.tt.model_preprocessing import (
+    create_yolov11_input_tensors,
+    create_yolov11_model_parameters,
+    create_yolov11_model_parameters_detect,
+)
+from models.experimental.yolov11.reference.yolov11 import (
+    Attention as torch_attention,
+    Bottleneck as torch_bottleneck,
+    C2PSA as torch_c2psa_block,
+    C3k as torch_c3k,
+    C3k2 as torch_c3k2,
+    PSABlock as torch_psa_block,
+    SPPF as torch_sppf,
+    Detect as torch_detect,
+)
+from models.experimental.yolov11.tt.ttnn_yolov11 import (
+    Attention as ttnn_attention,
+    Bottleneck as ttnn_bottleneck,
+    C2PSA as ttnn_c2psa_block,
+    C3K as ttnn_c3k,
+    C3k2 as ttnn_c3k2,
+    PSABlock as ttnn_psa_block,
+    SPPF as ttnn_sppf,
+    Detect as ttnn_detect,
+)
+from models.utility_functions import skip_for_grayskull
+
+from models.experimental.yolov11.reference import yolov11
+from models.experimental.yolov11.tt import ttnn_yolov11
+
+
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        ([128, 128, 128], [256, 128, 128], [1, 1, 3], [1, 1, 1], [0, 0, 1], [1, 1, 1], [1, 1, 128], [1, 128, 7, 7]),
+    ],
+)
+@skip_for_grayskull()
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_attention(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_attention(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_attention(device=device, parameter=parameters.conv_args, conv_pt=parameters)
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        ([16, 8], [8, 16], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 16, 56, 56]),  # 1
+        ([32, 16], [16, 32], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 32, 38, 28]),  # 2
+        ([32, 32], [32, 32], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 32, 14, 14]),  # 3
+        ([64, 64], [64, 64], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 64, 7, 7]),
+        ([64, 32], [32, 64], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 64, 14, 14]),
+        ([32, 16], [16, 32], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 32, 28, 28]),
+        ([64, 32], [32, 64], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 64, 14, 14]),
+        ([64, 64], [64, 64], [3, 3], [1, 1], [1, 1], [1, 1], [1, 1], [1, 64, 7, 7]),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_bottleneck(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_bottleneck(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_bottleneck(device=device, parameter=parameters.conv_args, conv_pt=parameters)
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        (
+            [256, 256, 128, 128, 128, 128, 256],
+            [256, 256, 256, 128, 128, 256, 128],
+            [1, 1, 1, 1, 3, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 1, 0, 0],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 128, 1, 1],
+            [1, 256, 7, 7],
+        ),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_c2psa_block(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_c2psa_block(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_c2psa_block(device=device, parameter=parameters.conv_args, conv_pt=parameters)
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        (
+            [64, 64, 64, 32, 32, 32, 32],
+            [32, 32, 64, 32, 32, 32, 32],
+            [1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 64, 14, 14],
+        ),
+        (
+            [128, 128, 128, 64, 64, 64, 64],
+            [64, 64, 128, 64, 64, 64, 64],
+            [1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 128, 7, 7],
+        ),
+        (
+            [64, 64, 64, 32, 32, 32, 32],
+            [32, 32, 64, 32, 32, 32, 32],
+            [1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 64, 40, 40],
+        ),
+        (
+            [128, 128, 128, 64, 64, 64, 64],
+            [64, 64, 128, 64, 64, 64, 64],
+            [1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1],
+            [1, 128, 20, 20],
+        ),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_c3k(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_c3k(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_c3k(device=device, parameter=parameters.conv_args, conv_pt=parameters)
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,is_bk_enabled,fwd_input_shape",
+    [
+        # 224
+        (
+            [32, 48, 16, 8],
+            [32, 64, 8, 16],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 32, 56, 56],
+        ),
+        (
+            [64, 96, 32, 16],
+            [64, 128, 16, 32],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 64, 28, 28],
+        ),
+        (
+            [128, 192, 64, 64, 64, 32, 32, 32, 32],
+            [128, 128, 32, 32, 64, 32, 32, 32, 32],
+            [1, 1, 1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            False,
+            [1, 128, 14, 14],
+        ),
+        (
+            [256, 384, 128, 128, 128, 64, 64, 64, 64],
+            [256, 256, 64, 64, 128, 64, 64, 64, 64],
+            [1, 1, 1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            False,
+            [1, 256, 7, 7],
+        ),
+        (
+            [384, 192, 64, 32],
+            [128, 128, 32, 64],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 384, 14, 14],
+        ),
+        (
+            [256, 96, 32, 16],
+            [64, 64, 16, 32],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 256, 28, 28],
+        ),
+        (
+            [192, 192, 64, 32],
+            [128, 128, 32, 64],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 192, 14, 14],
+        ),
+        (
+            [384, 384, 128, 128, 128, 64, 64, 64, 64],
+            [256, 256, 64, 64, 128, 64, 64, 64, 64],
+            [1, 1, 1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            False,
+            [1, 384, 7, 7],
+        ),
+        # 640
+        (
+            [32, 48, 16, 8],
+            [32, 64, 8, 16],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 32, 160, 160],
+        ),
+        (
+            [64, 96, 32, 16],
+            [64, 128, 16, 32],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 64, 80, 80],
+        ),
+        (
+            [128, 192, 64, 64, 64, 32, 32, 32, 32],
+            [128, 128, 32, 32, 64, 32, 32, 32, 32],
+            [1, 1, 1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            False,
+            [1, 128, 40, 40],
+        ),
+        (
+            [256, 384, 128, 128, 128, 64, 64, 64, 64],
+            [256, 256, 64, 64, 128, 64, 64, 64, 64],
+            [1, 1, 1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            False,
+            [1, 256, 20, 20],
+        ),
+        (
+            [384, 192, 64, 32],
+            [128, 128, 32, 64],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 384, 40, 40],
+        ),
+        (
+            [256, 96, 32, 16],
+            [64, 64, 16, 32],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 256, 80, 80],
+        ),
+        (
+            [192, 192, 64, 32],
+            [128, 128, 32, 64],
+            [1, 1, 3, 3],
+            [1, 1, 1, 1],
+            [0, 0, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            True,
+            [1, 192, 40, 40],
+        ),
+        (
+            [384, 384, 128, 128, 128, 64, 64, 64, 64],
+            [256, 256, 64, 64, 128, 64, 64, 64, 64],
+            [1, 1, 1, 1, 1, 3, 3, 3, 3],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 0, 0, 0, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            False,
+            [1, 384, 20, 20],
+        ),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_c3k2(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    is_bk_enabled,
+    fwd_input_shape,
+):
+    torch_module = torch_c3k2(in_channel, out_channel, kernel, stride, padding, dilation, groups, is_bk_enabled)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device, memory_config=ttnn.L1_MEMORY_CONFIG)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_c3k2(
+        device=device, parameter=parameters.conv_args, conv_pt=parameters, is_bk_enabled=is_bk_enabled
+    )
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        (
+            [128, 128, 128, 128, 256],
+            [256, 128, 128, 256, 128],
+            [1, 1, 3, 1, 1],
+            [1, 1, 1, 1, 1],
+            [0, 0, 1, 0, 0],
+            [1, 1, 1, 1, 1],
+            [1, 1, 128, 1, 1],
+            [1, 128, 7, 7],
+        ),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_psa_block(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_psa_block(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_psa_block(device=device, parameter=parameters.conv_args, conv_pt=parameters)
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        ([256, 512], [128, 256], [1, 1], [1, 1], [0, 0], [1, 1], [1, 1], [1, 256, 20, 20]),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_sppf(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_sppf(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0],
+        input_channels=fwd_input_shape[1],
+        input_height=fwd_input_shape[2],
+        input_width=fwd_input_shape[3],
+    )
+    ttnn_input = ttnn.to_device(ttnn_input, device=device)
+    ttnn_input = ttnn.to_layout(ttnn_input, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input)
+    parameters = create_yolov11_model_parameters(torch_module, torch_input, device=device)
+    ttnn_module = ttnn_sppf(device=device, parameter=parameters.conv_args, conv_pt=parameters)
+    ttnn_output = ttnn_module(x=ttnn_input, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.permute(0, 3, 1, 2)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+try:
+    sys.modules["ultralytics"] = yolov11
+    sys.modules["ultralytics.nn.tasks"] = yolov11
+    sys.modules["ultralytics.nn.modules.conv"] = yolov11
+    sys.modules["ultralytics.nn.modules.block"] = yolov11
+    sys.modules["ultralytics.nn.modules.head"] = yolov11
+
+except KeyError:
+    print("models.experimental.yolov11.reference.yolov11 not found.")
+
+
+class Ensemble(nn.ModuleList):
+    def __init__(self):
+        super(Ensemble, self).__init__()
+
+    def forward(self, x, augment=False):
+        y = []
+        for module in self:
+            y.append(module(x, augment)[0])
+        y = torch.cat(y, 1)
+        return y, None
+
+
+def attempt_download(file, repo="ultralytics/assets"):
+    tests = Path(__file__).parent.parent / "yolov11"
+    file_path = tests / Path(str(file).strip().replace("'", "").lower())
+
+    if not file_path.exists():
+        name = "yolov11n.pt"  # file_path.name
+        msg = f"{file_path} missing, try downloading from https://github.com/{repo}/releases/"
+
+        try:
+            url = f"https://github.com/{repo}/releases/download/v8.3.0/{name}"
+
+            print(f"Downloading {url} to {file_path}...")
+            torch.hub.download_url_to_file(url, file_path)
+
+            # Validate the file
+            assert file_path.exists() and file_path.stat().st_size > 1e6, f"Download failed for {name}"
+
+        except Exception as e:
+            print(f"Error downloading from GitHub: {e}. Trying secondary source...")
+
+            url = f"https://storage.googleapis.com/{repo}/ckpt/{name}"
+            print(f"Downloading {url} to {file_path}...")
+            os.system(f"curl -L {url} -o {file_path}")
+
+            if not file_path.exists() or file_path.stat().st_size < 1e6:
+                file_path.unlink(missing_ok=True)
+                print(f"ERROR: Download failure for {msg}")
+            else:
+                print(f"Download succeeded from secondary source!")
+    return file_path
+
+
+# Function to load weights into the model
+def attempt_load(weights, map_location=None):
+    model = Ensemble()
+
+    # Iterate through the weights and load them
+    for w in weights if isinstance(weights, list) else [weights]:
+        weight_path = attempt_download(w)
+        ckpt = torch.load(weight_path, map_location=map_location)
+        model.append(ckpt["ema" if ckpt.get("ema") else "model"].float().eval())
+    for m in model.modules():
+        if isinstance(m, (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU)):
+            m.inplace = True
+        elif isinstance(m, nn.Upsample):
+            m.recompute_scale_factor = None
+
+    if len(model) == 1:
+        return model[-1]
+    else:
+        for k in ["names", "stride"]:
+            setattr(model, k, getattr(model[-1], k))
+        return model
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "in_channel, out_channel, kernel, stride, padding, dilation, groups,fwd_input_shape",
+    [
+        (
+            [64, 64, 64, 128, 64, 64, 256, 64, 64, 64, 64, 80, 80, 80, 128, 128, 80, 80, 80, 256, 256, 80, 80, 80, 16],
+            [64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 80, 80, 80, 80, 128, 80, 80, 80, 80, 256, 80, 80, 80, 80, 1],
+            [3, 3, 1, 3, 3, 1, 3, 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1, 64, 1, 80, 1, 1, 128, 1, 80, 1, 1, 256, 1, 80, 1, 1, 1],
+            [[1, 64, 28, 28], [1, 128, 14, 14], [1, 256, 7, 7]],
+        ),
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolo_v11_detect(
+    device,
+    use_program_cache,
+    reset_seeds,
+    in_channel,
+    out_channel,
+    kernel,
+    stride,
+    padding,
+    dilation,
+    groups,
+    fwd_input_shape,
+):
+    torch_module = torch_detect(in_channel, out_channel, kernel, stride, padding, dilation, groups)
+    torch_module.eval()
+    torch_input_1, ttnn_input_1 = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[0][0],
+        input_channels=fwd_input_shape[0][1],
+        input_height=fwd_input_shape[0][2],
+        input_width=fwd_input_shape[0][3],
+    )
+    torch_input_2, ttnn_input_2 = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[1][0],
+        input_channels=fwd_input_shape[1][1],
+        input_height=fwd_input_shape[1][2],
+        input_width=fwd_input_shape[1][3],
+    )
+    torch_input_3, ttnn_input_3 = create_yolov11_input_tensors(
+        device,
+        batch=fwd_input_shape[2][0],
+        input_channels=fwd_input_shape[2][1],
+        input_height=fwd_input_shape[2][2],
+        input_width=fwd_input_shape[2][3],
+    )
+    ttnn_input_1 = ttnn.to_device(ttnn_input_1, device=device)
+    ttnn_input_1 = ttnn.to_layout(ttnn_input_1, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    ttnn_input_2 = ttnn.to_device(ttnn_input_2, device=device)
+    ttnn_input_2 = ttnn.to_layout(ttnn_input_2, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    ttnn_input_3 = ttnn.to_device(ttnn_input_3, device=device)
+    ttnn_input_3 = ttnn.to_layout(ttnn_input_3, layout=ttnn.TILE_LAYOUT, memory_config=ttnn.L1_MEMORY_CONFIG)
+    torch_output = torch_module(torch_input_1, torch_input_2, torch_input_3)
+    parameters = create_yolov11_model_parameters_detect(
+        torch_module, torch_input_1, torch_input_2, torch_input_3, device=device
+    )
+    ttnn_module = ttnn_detect(device=device, parameter=parameters.model, conv_pt=parameters)
+
+    ttnn_output = ttnn_module(y1=ttnn_input_1, y2=ttnn_input_2, y3=ttnn_input_3, device=device)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)
+
+
+@skip_for_grayskull()
+@pytest.mark.parametrize(
+    "resolution",
+    [
+        ([1, 3, 224, 224]),
+        ([1, 3, 640, 640]),
+    ],
+)
+@pytest.mark.parametrize(
+    "use_pretrained_weight",
+    [
+        False,
+        # True      # uncomment  to run the model for real weights
+    ],
+    ids=[
+        "pretrained_weight_false",
+        # "pretrained_weight_true",    # uncomment to run the model for real weights
+    ],
+)
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True)
+def test_yolov11(device, use_program_cache, reset_seeds, resolution, use_pretrained_weight):
+    torch_input, ttnn_input = create_yolov11_input_tensors(
+        device, batch=resolution[0], input_channels=resolution[1], input_height=resolution[2], input_width=resolution[3]
+    )
+    if use_pretrained_weight:
+        torch_model = attempt_load("yolov11n.pt", map_location="cpu")
+        state_dict = torch_model.state_dict()
+        torch_model = yolov11.YoloV11()
+        ds_state_dict = {k: v for k, v in state_dict.items()}
+        new_state_dict = {}
+        for (name1, parameter1), (name2, parameter2) in zip(torch_model.state_dict().items(), ds_state_dict.items()):
+            if isinstance(parameter2, torch.FloatTensor):
+                new_state_dict[name1] = parameter2
+        torch_model.load_state_dict(new_state_dict)
+    else:
+        torch_model = yolov11.YoloV11()
+    torch_model.eval()
+
+    torch_output = torch_model(torch_input)
+    parameters = create_yolov11_model_parameters(torch_model, torch_input, device=device)
+    ttnn_model = ttnn_yolov11.YoloV11(device, parameters)
+    ttnn_output = ttnn_model(ttnn_input)
+    ttnn_output = ttnn.to_torch(ttnn_output)
+
+    ttnn_output = ttnn_output.reshape(torch_output.shape)
+    assert_with_pcc(torch_output, ttnn_output, 0.99)