diff --git a/Tenacious/architecture_diagram.png b/Tenacious/architecture_diagram.png
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diff --git a/Tenacious/car.jpg b/Tenacious/car.jpg
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diff --git a/Tenacious/main.py b/Tenacious/main.py
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--- /dev/null
+++ b/Tenacious/main.py
@@ -0,0 +1,52 @@
+import numpy as np
+
+from PIL import Image
+import cv2
+
+import torch
+
+import ultralytics
+from ultralytics import YOLO
+from diffusers import StableDiffusionInpaintPipeline
+
+model = YOLO('yolov8m-seg.pt')
+
+img= cv2.imread('car.jpg')
+image = cv2.resize(img,(640,384))
+
+results = model.predict(source=img.copy(), save=True, save_txt=False, stream=True)
+
+for result in results:
+ # get array results
+ masks = result.masks.data
+ boxes = result.boxes.data
+ # extract classes
+ clss = boxes[:, 5]
+ # get indices of results where class is 0 (people in COCO)
+ car_indices = torch.where(clss == 2)
+ # use these indices to extract the relevant masks
+ car_masks = masks[car_indices]
+ # scale for visualizing results
+ car_mask = torch.any(car_masks, dim=0).int() * 255
+
+ mask_image = car_mask.cpu().numpy()
+
+image = Image.fromarray(image.astype('uint8'), 'RGB')
+mask_image = Image.fromarray(cv2.bitwise_not(mask_image).astype('uint8'))
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+ "runwayml/stable-diffusion-inpainting",
+ revision="fp16",
+ torch_dtype=torch.float32,
+)
+
+prompt = str(input("Enter the prompt: ")) #"high resolution, car on beach"
+
+out_image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
+
+output_image = np.array(out_image)
+output_image = cv2.resize(output_image,image.size)
+
+cv2.imshow("Product Image",ouput_image)
+cv2.waitkey(0)
+
diff --git a/Tenacious/readme.md b/Tenacious/readme.md
new file mode 100644
index 00000000..08ed12d5
--- /dev/null
+++ b/Tenacious/readme.md
@@ -0,0 +1,38 @@
+**Team Name** - Tenacious
+**Problem Statement** - Revolutionary AI-Infused Retail Platform
+**Team Leader Email** - dhiman.anushka@gmail.com
+# A Brief of the Prototype
+GenAI Product Shots, will generate professional studio equivalent images of products.
+GenAI Product Shots will cut the photography time, cost and resource.
+We will mask the object from the image, then original image and masked image will be given as an input.
+Stable diffusion will produce desirable results based on the input and prompt.
+Multiple variant of these images with different colours and backgrounds can be used for product catalogue.
+
+
+
+
+# Tech Stack
+- Intel Extension for PyTorch
+- PyTorch-GPU
+- OpenCV
+- Numpy
+- Pillow
+- YOLO
+- Stable Diffusion
+
+# Step-by-Step Code Execution Instructions
+
+``git clone https://github.com/akkmr1996/oneAPI-GenAI-Hackathon-2023.git``
+
+``cd Tenacious``
+
+``pip install -r requirements.txt``
+
+``python main.py``
+
+
+# Future Scope
+- We will develop a user friendly web application using Flask.
+- In this we have only used a car as a product, but in future we will use different product like food product, beauty and fashion products.
+- Then we need to train a segmentation model for these products and then deployed it on Intel Cloud and hence use the Intel OneAPI Toolkit.
+
diff --git a/Tenacious/requirements.txt b/Tenacious/requirements.txt
new file mode 100644
index 00000000..eec0e445
--- /dev/null
+++ b/Tenacious/requirements.txt
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