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# these are the libraries you have to isntall for this projet
#!pip install ultralytics datasets wandb gradio opencv-python Pillow captum torchvision --upgrade
import os
import cv2
import torch
import numpy as np
from ultralytics import YOLO
import wandb
import matplotlib.pyplot as plt
from datetime import datetime
from google.colab import userdata
# below is the api key's name I had set in my google colab's secret key name to ensure privacy for my api key . If you want
# you can create an accoint at WANDB and then download name the api key WANDB and then use it for your personal use
wandb.login(key=userdata.get('WANDB'))
def setup_wandb():
wandb.init(project="Object-detection",
name=f"run_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
config={
"model": "yolov8n",
"dataset": "coco128",
"img_size": 640,
"batch_size": 8
})
def load_model():
model = YOLO("yolov8n.pt")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
return model
def train_model(model):
results = model.train(
data="coco128.yaml",
epochs=20,
imgsz=640,
batch=8,
device='0' if torch.cuda.is_available() else 'cpu',
patience=3,
save=True
)
return model
def validate_model(model):
metrics = model.val()
wandb.log({
"val/mAP50": metrics.box.map50,
"val/mAP50-95": metrics.box.map,
"val/precision": metrics.box.mp,
"val/recall": metrics.box.mr
})
return metrics
def visualize_results(results, img_path):
img = cv2.imread(img_path)
if img is None:
raise ValueError(f"Failed to load image: {img_path}")
pred_img = results[0].plot()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax1.axis('off')
ax2.imshow(cv2.cvtColor(pred_img, cv2.COLOR_BGR2RGB))
ax2.axis('off')
plt.savefig("detection_results.jpg")
plt.close()
return "detection_results.jpg"
def test_image(model, img_path="test_image.jpg"):
if not os.path.exists(img_path):
raise FileNotFoundError(f"Image not found: {img_path}")
results = model(img_path)
output_path = visualize_results(results, img_path)
wandb.log({
"test_results": wandb.Image(output_path),
"detections": results[0].boxes.cls.tolist(),
"confidences": results[0].boxes.conf.tolist()
})
return results
def webcam_demo(model):
try:
from google.colab.patches import cv2_imshow
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Webcam not available - skipping demo")
return
print("Press 'q' to quit webcam demo")
while True:
ret, frame = cap.read()
if not ret:
break
results = model(frame)
annotated = results[0].plot()
cv2_imshow(annotated)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except Exception as e:
print(f"Webcam error: {e}")
finally:
cap.release()
cv2.destroyAllWindows()
def export_model():
trained_weights = "runs/detect/train/weights/best.pt"
model = YOLO(trained_weights)
model.export(format="torchscript")
wandb.save("best.torchscript")
def main():
setup_wandb()
model = load_model()
model = train_model(model)
validate_model(model)
test_image(model)
export_model()
wandb.finish()
if __name__ == "__main__":
main()
# main objective of this colab filw was to generate wegihts after training and thne later downlaod and upload to hugging face for inference
#as you cannot directly run of hugging due to absence of gpu
from google.colab import files
files.download("runs/detect/train/weights/best.torchscript")
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