#0: Yolov11 Demo and Evaluation Code Added

models/experimental/yolov11/README.md

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+## YOLOv11n - Model
+
+### Introduction
+
+**YOLOv11** is the latest iteration in the YOLO series, offering improvements in accuracy, speed, and efficiency for real-time object detection. It features enhanced architecture and optimized training methods, suitable for various computer vision tasks.
+
+### Model Details
+
+* **Entry Point:** `models/experimental/yolov11/tt/ttnn_yolov11.py`
+* **Weights:** `models/experimental/yolov11/reference/yolov11n.pt`
+
+### Batch Size
+
+* Default: 1
+* Recommended: 1 for optimal performance
+
+### Running YOLOv11 Demo
+
+* **Single Image (640x640x3 or 224x224x3):** `pytest models/experimental/yolov11/demo/demo.py`
+* **Dataset Evaluation:** `pytest models/experimental/yolov11/demo/evaluate.py`
+  * Validation accuracy: 0.5616 on 250 images (coco-2017)
+
+### Input and Output Data
+
+* **Input Directory:** `models/experimental/yolov11/demo/images`
+* **Output Directory:** `models/experimental/yolov11/demo/runs`
+  * Torch model output: `torch_model`
+  * TTNN model output: `tt_model`
+
+### Pending Issues
+
+* [#17385](https://github.com/tenstorrent/tt-metal/issues/17835) - Tracing fails in Yolov11n model

models/experimental/yolov11/demo/demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from pathlib import Path
+import os
+import cv2
+import sys
+import ttnn
+import torch
+import pytest
+import torch.nn as nn
+from loguru import logger
+from datetime import datetime
+from models.utility_functions import disable_persistent_kernel_cache
+from models.experimental.yolov11.reference import yolov11
+from models.experimental.yolov11.reference.yolov11 import attempt_load
+from models.experimental.yolov11.tt import ttnn_yolov11
+from models.experimental.yolov11.tt.model_preprocessing import (
+    create_yolov11_input_tensors,
+    create_yolov11_model_parameters,
+)
+from models.experimental.yolov11.demo.demo_utils import LoadImages, preprocess, postprocess
+
+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.")
+
+
+def save_yolo_predictions_by_model(result, save_dir, image_path, model_name):
+    model_save_dir = os.path.join(save_dir, model_name)
+    os.makedirs(model_save_dir, exist_ok=True)
+
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    if model_name == "torch_model":
+        bounding_box_color, label_color = (0, 255, 0), (0, 255, 0)
+    else:
+        bounding_box_color, label_color = (255, 0, 0), (255, 0, 0)
+
+    boxes = result["boxes"]["xyxy"]
+    scores = result["boxes"]["conf"]
+    classes = result["boxes"]["cls"]
+    names = result["names"]
+
+    for box, score, cls in zip(boxes, scores, classes):
+        x1, y1, x2, y2 = map(int, box)
+        label = f"{names[int(cls)]} {score.item():.2f}"
+        cv2.rectangle(image, (x1, y1), (x2, y2), bounding_box_color, 3)
+        cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, label_color, 2)
+
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_name = f"prediction_{timestamp}.jpg"
+    output_path = os.path.join(model_save_dir, output_name)
+    cv2.imwrite(output_path, image)
+    print(f"Predictions saved to {output_path}")
+
+
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 32768}], indirect=True)
+@pytest.mark.parametrize(
+    "source, model_type,resolution",
+    [
+        # 224*224
+        # ("models/experimental/yolov11/demo/images/cycle_girl.jpg", "torch_model", [3, 224, 224]),
+        # ("models/experimental/yolov11/demo/images/cycle_girl.jpg", "tt_model", [3, 224, 224]),
+        # ("models/experimental/yolov11/demo/images/dog.jpg", "torch_model", [3, 224, 224]),
+        # ("models/experimental/yolov11/demo/images/dog.jpg", "tt_model", [3, 224, 224]),
+        # 640*640
+        # ("models/experimental/yolov11/demo/images/cycle_girl.jpg", "torch_model", [3, 640, 640]),
+        ("models/experimental/yolov11/demo/images/cycle_girl.jpg", "tt_model", [3, 640, 640]),
+        # ("models/experimental/yolov11/demo/images/dog.jpg", "torch_model", [3, 640, 640]),
+        # ("models/experimental/yolov11/demo/images/dog.jpg", "tt_model", [3, 640, 640]),
+    ],
+)
+def test_demo(device, source, model_type, resolution):
+    disable_persistent_kernel_cache()
+    state_dict = attempt_load("yolov11n.pt", map_location="cpu").state_dict()
+    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(model.state_dict().items(), ds_state_dict.items()):
+        if isinstance(parameter2, torch.FloatTensor):
+            new_state_dict[name1] = parameter2
+    model.load_state_dict(new_state_dict)
+    if model_type == "torch_model":
+        model.eval()
+        logger.info("Inferencing using Torch Model")
+    else:
+        torch_input, ttnn_input = create_yolov11_input_tensors(
+            device, input_channels=resolution[0], input_height=resolution[1], input_width=resolution[2]
+        )
+        parameters = create_yolov11_model_parameters(model, torch_input, device=device)
+        model = ttnn_yolov11.YoloV11(device, parameters)
+        logger.info("Inferencing using ttnn Model")
+
+    save_dir = "models/experimental/yolov11/demo/runs"
+    dataset = LoadImages(path=source)
+    model_save_dir = os.path.join(save_dir, model_type)
+    os.makedirs(model_save_dir, exist_ok=True)
+
+    names = {
+        0: "person",
+        1: "bicycle",
+        2: "car",
+        3: "motorcycle",
+        4: "airplane",
+        5: "bus",
+        6: "train",
+        7: "truck",
+        8: "boat",
+        9: "traffic light",
+        10: "fire hydrant",
+        11: "stop sign",
+        12: "parking meter",
+        13: "bench",
+        14: "bird",
+        15: "cat",
+        16: "dog",
+        17: "horse",
+        18: "sheep",
+        19: "cow",
+        20: "elephant",
+        21: "bear",
+        22: "zebra",
+        23: "giraffe",
+        24: "backpack",
+        25: "umbrella",
+        26: "handbag",
+        27: "tie",
+        28: "suitcase",
+        29: "frisbee",
+        30: "skis",
+        31: "snowboard",
+        32: "sports ball",
+        33: "kite",
+        34: "baseball bat",
+        35: "baseball glove",
+        36: "skateboard",
+        37: "surfboard",
+        38: "tennis racket",
+        39: "bottle",
+        40: "wine glass",
+        41: "cup",
+        42: "fork",
+        43: "knife",
+        44: "spoon",
+        45: "bowl",
+        46: "banana",
+        47: "apple",
+        48: "sandwich",
+        49: "orange",
+        50: "broccoli",
+        51: "carrot",
+        52: "hot dog",
+        53: "pizza",
+        54: "donut",
+        55: "cake",
+        56: "chair",
+        57: "couch",
+        58: "potted plant",
+        59: "bed",
+        60: "dining table",
+        61: "toilet",
+        62: "TV",
+        63: "laptop",
+        64: "mouse",
+        65: "remote",
+        66: "keyboard",
+        67: "cell phone",
+        68: "microwave",
+        69: "oven",
+        70: "toaster",
+        71: "sink",
+        72: "refrigerator",
+        73: "book",
+        74: "clock",
+        75: "vase",
+        76: "scissors",
+        77: "teddy bear",
+        78: "hair drier",
+        79: "toothbrush",
+    }
+
+    for batch in dataset:
+        paths, im0s, s = batch
+        im = preprocess(im0s, resolution)
+        if model_type == "torch_model":
+            preds = model(im)
+        else:
+            img = torch.permute(im, (0, 2, 3, 1))
+            img = img.reshape(
+                1,
+                1,
+                img.shape[0] * img.shape[1] * img.shape[2],
+                img.shape[3],
+            )
+            ttnn_im = ttnn.from_torch(img, layout=ttnn.TILE_LAYOUT, dtype=ttnn.bfloat8_b)
+            preds = model(x=ttnn_im)
+            preds = ttnn.to_torch(preds, dtype=torch.float32)
+        results = postprocess(preds, im, im0s, batch, names)[0]
+        save_yolo_predictions_by_model(results, save_dir, source, model_type)
+    print("Inference done")