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70epoch.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f8a7d4c3ee25c2236425f2bb5f4257ef5d16d71834676d273984870a11bcdcbd
+size 22519288

README.md

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----
-title: Ugan Yolo
-emoji: 📈
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.32.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+underwater image enhancement and trash detection model

checkpoint_epoch_350.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40f6a8934bda8230cd4c97bbc251a0561a57ff35ebdce9e507e13ab9e4999273
+size 655177894

detection_infer.py

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+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+import streamlit as st
+import seaborn as sns
+st.set_option('deprecation.showPyplotGlobalUse', False)
+import torch
+import cv2
+
+labels = ['Mask', 'can', 'cellphone', 'electronics', 'gbottle', 'glove', 'metal', 'misc', 'net', 'pbag', 'pbottle',
+        'plastic', 'rod', 'sunglasses', 'tire']
+
+garbage = []
+def detect(image):
+    model = YOLO("70epoch.pt")
+    results = model(image)
+    class_list = []
+    for result in results:
+        boxes = result.boxes  # Boxes object for bbox outputs
+        class_list = boxes.cls.tolist()
+    int_list = [int(num) for num in class_list]
+    class_names = [labels[i] for i in int_list]
+    garbage.extend(class_names)
+    res_plotted = results[0].plot()
+
+    return res_plotted, class_names

ugan.py

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+import torch
+import torch.nn as nn 
+from torch.nn import Conv2d,LeakyReLU,BatchNorm2d, ConvTranspose2d,ReLU
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+import numpy as np
+import cv2
+import os
+from accelerate import Accelerator
+import datetime
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from tensorboardX import SummaryWriter
+from accelerate import notebook_launcher
+
+
+accelerator = Accelerator()
+device = accelerator.device
+
+def get_transforms():
+    transform = transforms.Compose([
+        transforms.ToTensor(),# H,W,C -> C,H,W && [0,255] -> [0,1]
+        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)) #[0,1] -> [-1,1]
+    ])
+    return transform
+
+
+class MYDataSet(Dataset):
+    def __init__(self,src_data_path,dst_data_path):
+        self.train_A_imglist = self.get_imglist(src_data_path)
+        self.train_B_imglist = self.get_imglist(dst_data_path)
+        self.transform = get_transforms()
+    
+    def get_imglist(self, img_dir):
+        img_name_list = sorted(os.listdir(img_dir))
+        img_list = []
+        half_len = len(img_name_list) // 3
+        for img_name in img_name_list[:half_len]:
+            img_path = os.path.join(img_dir, img_name)
+            img_list.append(img_path)
+        return img_list
+    
+    def __len__(self):
+        return len(self.train_A_imglist)
+    def __getitem__(self,index):
+        train_A_img_path = self.train_A_imglist[index]
+        train_B_img_path = self.train_B_imglist[index]
+
+        train_A_img = cv2.imread(train_A_img_path)
+        train_B_img = cv2.imread(train_B_img_path)
+
+        train_A_tensor = self.transform(train_A_img)
+        train_B_tensor = self.transform(train_B_img)
+    
+
+        return [train_A_tensor,train_B_tensor]
+    
+
+def encoder_layer(in_channels,out_channels,kernel_size=4,stride = 2,padding = 1): # NOTE: Padding here is different from the 'vaild' in tensorflow version of original github
+    layer = nn.Sequential(
+        Conv2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding),
+        BatchNorm2d(out_channels),
+        LeakyReLU(0.2)
+    )
+    return layer
+
+
+def decoder_layer(in_channels,out_channels,last_layer=False,kernel_size=4,stride = 2,padding = 1):
+    if not last_layer:
+        layer = nn.Sequential(
+            ConvTranspose2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding),
+            torch.nn.ReLU()
+        )
+    else:
+        layer = nn.Sequential(
+            ConvTranspose2d(in_channels,out_channels,kernel_size=4,stride=2,padding=1),
+            torch.nn.Tanh()
+        )
+    return layer
+
+def discrimiter_layer(in_channels,out_channels,kernel_size=4,stride = 2,padding = 1,wgan=False):
+    if wgan:
+        layer = nn.Sequential(
+            Conv2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding),
+            BatchNorm2d(out_channels),
+            LeakyReLU(0.2)
+        )
+    else:
+        layer = nn.Sequential(
+            Conv2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding),
+            LeakyReLU(0.2)
+        )
+    return layer
+
+
+class GeneratorNet(torch.nn.Module):
+    def __init__(self):
+        super(GeneratorNet, self).__init__()
+
+        # Encoder
+        self.enc_conv1 = encoder_layer(3,64)
+        self.enc_conv2 = encoder_layer(64,128)
+        self.enc_conv3 = encoder_layer(128,256)
+        self.enc_conv4 = encoder_layer(256,512)
+        self.enc_conv5 = encoder_layer(512,512)
+        self.enc_conv6 = encoder_layer(512,512)
+        self.enc_conv7 = encoder_layer(512,512)
+        self.enc_conv8 = encoder_layer(512,512,padding=1)
+        # Decoder
+        self.dec_conv1 = decoder_layer(512,512)
+        self.dec_conv2 = decoder_layer(1024,512)
+        self.dec_conv3 = decoder_layer(1024,512)
+        self.dec_conv4 = decoder_layer(1024,512)
+        self.dec_conv5 = decoder_layer(1024,256)
+        self.dec_conv6 = decoder_layer(512,128)
+        self.dec_conv7 = decoder_layer(256,64)
+        self.dec_conv8 = decoder_layer(128,3,last_layer=True)
+        
+    def forward(self,input_x):
+        # Encoder
+        output_enc_conv1 = self.enc_conv1(input_x)
+        output_enc_conv2 = self.enc_conv2(output_enc_conv1)
+        output_enc_conv3 = self.enc_conv3(output_enc_conv2)
+        output_enc_conv4 = self.enc_conv4(output_enc_conv3)
+        output_enc_conv5 = self.enc_conv5(output_enc_conv4)
+        output_enc_conv6 = self.enc_conv6(output_enc_conv5)
+        output_enc_conv7 = self.enc_conv7(output_enc_conv6)
+        output_enc_conv8 = self.enc_conv8(output_enc_conv7)
+
+
+        #  Decoder
+        output_dec_conv1 = self.dec_conv1(output_enc_conv8)
+        output_dec_conv1 = torch.cat([output_dec_conv1,output_enc_conv7],dim = 1)
+
+        output_dec_conv2 = self.dec_conv2(output_dec_conv1)
+        output_dec_conv2 = torch.cat([output_dec_conv2,output_enc_conv6],dim = 1)
+
+        output_dec_conv3 = self.dec_conv3(output_dec_conv2)
+        output_dec_conv3 = torch.cat([output_dec_conv3,output_enc_conv5],dim = 1)
+
+        output_dec_conv4 = self.dec_conv4(output_dec_conv3)
+        output_dec_conv4 = torch.cat([output_dec_conv4,output_enc_conv4],dim = 1)
+
+        output_dec_conv5 = self.dec_conv5(output_dec_conv4)
+        output_dec_conv5 = torch.cat([output_dec_conv5,output_enc_conv3],dim = 1)
+
+        output_dec_conv6 = self.dec_conv6(output_dec_conv5)
+        output_dec_conv6 = torch.cat([output_dec_conv6,output_enc_conv2],dim = 1)
+
+        output_dec_conv7 = self.dec_conv7(output_dec_conv6)
+        output_dec_conv7 = torch.cat([output_dec_conv7,output_enc_conv1],dim = 1)
+
+        output_dec_conv8 = self.dec_conv8(output_dec_conv7)
+
+        return output_dec_conv8
+      
+
+      
+class DiscrimiterNet(torch.nn.Module):
+    def __init__(self,wgan_loss):
+        super(DiscrimiterNet, self).__init__()
+        self.wgan_loss = wgan_loss
+
+        self.conv1 = discrimiter_layer(3,64,self.wgan_loss)
+        self.conv2 = discrimiter_layer(64,128,self.wgan_loss)
+        self.conv3 = discrimiter_layer(128,256,self.wgan_loss)
+        self.conv4 = discrimiter_layer(256,512,self.wgan_loss)
+        self.conv5 = discrimiter_layer(512,1,kernel_size=1,stride=1)
+    def forward(self,x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.conv5(x)
+
+        return x
+    
+
+def tensor2img(one_tensor):# [b,c,h,w] [-1,1]
+    tensor = one_tensor.squeeze(0) #[c,h,w] [0,1]
+    tensor = (tensor*0.5 + 0.5)*255 # [c,h,w] [0,255]
+    tensor_cpu = tensor.cpu()
+    img = np.array(tensor_cpu,dtype=np.uint8)
+    img = np.transpose(img,(1,2,0))
+    return img
+def img2tensor(np_img):# [h,w,c]
+    tensor = get_transforms()(np_img).cuda() # [c,h,w] [-1,1]
+    tensor = tensor.unsqueeze(0) # [b,c,h,w] [-1,1]
+    return tensor
+
+
+def weights_init(module):
+    classname = module.__class__.__name__
+    if classname.find('Conv') !=-1:
+        nn.init.normal_(module.weight.data,0.0,0.02)
+
+def loss_gradient_difference(real_image,generated): # b x c x h x w
+    true_x_shifted_right = real_image[:,:,1:,:]# 32 x 3 x 255 x 256
+    true_x_shifted_left = real_image[:,:,:-1,:]
+    true_x_gradient = torch.abs(true_x_shifted_left - true_x_shifted_right)
+
+    generated_x_shift_right = generated[:,:,1:,:]# 32 x 3 x 255 x 256
+    generated_x_shift_left = generated[:,:,:-1,:]
+    generated_x_griednt = torch.abs(generated_x_shift_left - generated_x_shift_right)
+
+    difference_x = true_x_gradient - generated_x_griednt
+
+    loss_x_gradient = (torch.sum(difference_x**2))/2 # tf.nn.l2_loss(true_x_gradient - generated_x_gradient)
+
+    true_y_shifted_right = real_image[:,:,:,1:]
+    true_y_shifted_left = real_image[:,:,:,:-1]
+    true_y_gradient = torch.abs(true_y_shifted_left - true_y_shifted_right)
+
+    generated_y_shift_right = generated[:,:,:,1:]
+    generated_y_shift_left = generated[:,:,:,:-1]
+    generated_y_griednt = torch.abs(generated_y_shift_left - generated_y_shift_right)
+
+    difference_y = true_y_gradient - generated_y_griednt
+    loss_y_gradient = (torch.sum(difference_y**2))/2 # tf.nn.l2_loss(true_y_gradient - generated_y_gradient)
+
+    igdl = loss_x_gradient + loss_y_gradient
+    return igdl
+
+
+def calculate_x_gradient(images):
+    x_gradient_filter = torch.Tensor(
+        [
+            [[0, 0, 0], [-1, 0, 1], [0, 0, 0]],
+            [[0, 0, 0], [-1, 0, 1], [0, 0, 0]],
+            [[0, 0, 0], [-1, 0, 1], [0, 0, 0]],
+        ]
+    ).cuda()
+    x_gradient_filter = x_gradient_filter.view(3, 1, 3, 3)
+    result = torch.functional.F.conv2d(
+        images, x_gradient_filter, groups=3, padding=(1, 1)
+    )
+    return result
+
+
+
+
+def calculate_y_gradient(images):
+    y_gradient_filter = torch.Tensor(
+        [
+            [[0, 1, 0], [0, 0, 0], [0, -1, 0]],
+            [[0, 1, 0], [0, 0, 0], [0, -1, 0]],
+            [[0, 1, 0], [0, 0, 0], [0, -1, 0]],
+        ]
+    ).cuda()
+    y_gradient_filter = y_gradient_filter.view(3, 1, 3, 3)
+    result = torch.functional.F.conv2d(
+        images, y_gradient_filter, groups=3, padding=(1, 1)
+    )
+    return result
+
+def loss_igdl( correct_images, generated_images): # taken from https://github.com/Arquestro/ugan-pytorch/blob/master/ops/loss_modules.py
+    correct_images_gradient_x = calculate_x_gradient(correct_images)
+    generated_images_gradient_x = calculate_x_gradient(generated_images)
+    correct_images_gradient_y = calculate_y_gradient(correct_images)
+    generated_images_gradient_y = calculate_y_gradient(generated_images)
+    pairwise_p_distance = torch.nn.PairwiseDistance(p=1)
+    distances_x_gradient = pairwise_p_distance(
+        correct_images_gradient_x, generated_images_gradient_x
+    )
+    distances_y_gradient = pairwise_p_distance(
+        correct_images_gradient_y, generated_images_gradient_y
+    )
+    loss_x_gradient = torch.mean(distances_x_gradient)
+    loss_y_gradient = torch.mean(distances_y_gradient)
+    loss = 0.5 * (loss_x_gradient + loss_y_gradient)
+    return loss
+
+
+def ToTensor(image):
+    """Convert ndarrays in sample to Tensors."""
+    # numpy image: H x W x C
+    # torch image: C X H X W
+    image = image.transpose((2, 0, 1))
+    # Normalize image from [0, 255] to [0, 1]
+    image = 1 / 255.0 * image
+    return torch.from_numpy(image).type(dtype=torch.float)
+
+def train():
+    accelerator = Accelerator()
+    device = accelerator.device
+    print(device)
+    
+    # Define the argparse arguments as variables
+    trainA_path = '/kaggle/input/underwater-img/underwater_imagenet/trainA'
+    trainB_path = '/kaggle/input/underwater-img/underwater_imagenet/trainB'
+    use_wgan = True
+    lr = 1e-4
+    max_epoch = 350
+    bz = 32
+    lbda1 = 100
+    lbda2 = 1
+    num_workers = 4
+    checkpoints_root = 'checkpoints'
+    log_root = './log'
+
+    if __name__ == "__main__":
+        wgan = use_wgan
+        learning_rate = lr
+        max_epochs = max_epoch
+        batch_size = bz
+        lambda_1 = lbda1
+        lambda_2 = lbda2
+
+        netG = torch.nn.DataParallel(GeneratorNet()).to(device)
+        netD = torch.nn.DataParallel(DiscrimiterNet(wgan_loss=wgan)).to(device)
+
+        optimizer_g = optim.Adam(netG.parameters(), lr=learning_rate)
+        optimizer_d = optim.Adam(netD.parameters(), lr=learning_rate)
+
+        dataset = MYDataSet(src_data_path=trainA_path, dst_data_path=trainB_path)
+        datasetloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+
+        log_root = log_root
+        date = datetime.datetime.now().strftime('%F_%T').replace(':', '_')
+        log_folder = date
+        log_dir = os.path.join(log_root, log_folder)
+        if not os.path.exists(log_dir):
+            os.makedirs(log_dir)
+        writer = SummaryWriter(log_dir=log_dir)
+
+        checkpoint_root = checkpoints_root
+        checkpoint_folder = date
+        checkpoint_dir = os.path.join(checkpoint_root, checkpoint_folder)
+        if not os.path.exists(checkpoint_dir):
+            os.makedirs(checkpoint_dir)
+
+        netG, netD, optimizer_g, optimizer_d, datasetloader = accelerator.prepare(netG, netD, optimizer_g, optimizer_d, datasetloader)
+
+        for epoch in range(0, max_epochs):
+            print("epoch :",epoch+1)
+            d_loss_log_list = []
+            g_loss_log_list = []
+            for iteration, data in enumerate(tqdm(datasetloader)):
+                batchtensor_A = data[0].to(device)
+                batchtensor_B = data[1].to(device)
+                generated_batchtensor = netG(batchtensor_A)
+
+                num_critic = 1 if not wgan else 5
+                for i in range(num_critic):
+                    optimizer_d.zero_grad()
+                    d_fake = netD(generated_batchtensor.detach())
+                    d_real = netD(batchtensor_B)
+
+                    d_loss = torch.mean(d_fake) - torch.mean(d_real)
+                    if wgan:
+                        lambda_gp = 10
+                        epsilon = torch.rand(batchtensor_B.size()[0], 1, 1, 1).to(device)
+                        x_hat = batchtensor_B * epsilon + (1 - epsilon) * generated_batchtensor
+                        d_hat = netD(x_hat)
+                        gradients = torch.autograd.grad(outputs=d_hat, inputs=x_hat, grad_outputs=torch.ones_like(d_hat), create_graph=True, retain_graph=True)[0]
+                        gradients = gradients.view(gradients.size(0), -1)
+                        gradient_penalty = lambda_gp * ((gradients.norm(2, dim=1) - 1) ** 2).mean()
+                        d_loss += gradient_penalty
+
+                    accelerator.backward(d_loss,retain_graph=True)
+                    optimizer_d.step()
+                    d_loss_log = d_loss.item()
+                    d_loss_log_list.append(d_loss_log)
+
+                optimizer_g.zero_grad()
+                d_fake = netD(generated_batchtensor)
+
+                g_loss = -torch.mean(d_fake)
+                l1_loss = torch.mean(torch.abs(generated_batchtensor - batchtensor_B))
+                igdl_loss = loss_igdl(batchtensor_B, generated_batchtensor)
+                g_loss += lambda_1 * l1_loss + lambda_2 * igdl_loss
+
+                accelerator.backward(g_loss)
+                optimizer_g.step()
+
+                g_loss_log = g_loss.item()
+                g_loss_log_list.append(g_loss_log)
+
+                writer.add_scalar('G_loss', g_loss_log, (epoch * len(datasetloader) + iteration))
+                writer.add_scalar('D_loss', d_loss_log, (epoch * len(datasetloader) + iteration))
+
+            d_loss_average_log = np.array(d_loss_log_list).mean()
+            g_loss_average_log = np.array(g_loss_log_list).mean()
+
+            writer.add_scalar('D_loss_epoch', d_loss_average_log, epoch)
+            writer.add_scalar('G_loss_epoch', g_loss_average_log, epoch)
+            print('D_loss_epoch',d_loss_average_log)
+            print('g_loss_epoch',g_loss_average_log)
+            if (epoch + 1) % 50 == 0:
+                torch.save({
+                    'epoch': epoch,
+                    'netG_state_dict': netG.state_dict(),
+                    'netD_state_dict': netD.state_dict(),
+                    'optimizer_g_state_dict': optimizer_g.state_dict(),
+                    'optimizer_d_state_dict': optimizer_d.state_dict(),
+                    'g_loss': g_loss_log_list[-1],
+                    'd_loss': d_loss_log_list[-1],
+                }, f"checkpoint_epoch_{epoch + 1}.pth")
+
+        writer.close()
+
+
+if __name__ == "__main__":
+    notebook_launcher(train, num_processes=1)
+
+
+
+

web.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+from ugan import GeneratorNet
+import torch
+from ugan import img2tensor
+from ugan import tensor2img
+from PIL import Image, ImageOps
+from torchvision import transforms
+import io
+from detection_infer import detect
+
+model_path ='checkpoint_epoch_350.pth'
+checkpoint = torch.load(model_path)
+
+
+# Assuming 'model' is your model instance
+netG = GeneratorNet().cuda()
+model_dict = netG.state_dict()
+new_state_dict = {}
+for k, v in checkpoint['netG_state_dict'].items():
+    if k in model_dict:
+        new_state_dict[k] = v
+        
+        
+for k, v in checkpoint['netG_state_dict'].items():
+    name = k[7:]  # remove 'module.' prefix
+    if name in model_dict:
+        new_state_dict[name] = v        
+# Load the newly created state dict
+
+new_model = GeneratorNet().cuda()
+for name, param in new_model.named_parameters():
+    if name in model_dict:
+        model_dict[name].copy_(param)
+        
+        
+        
+new_model.load_state_dict(new_state_dict)
+
+
+
+# Main Streamlit app
+def main():
+    st.title('under water Image enhancement and trash detection App')
+    st.write('Upload an image and we will process it!')
+
+    # Upload image
+    uploaded_image = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
+
+    if uploaded_image is not None:
+        with torch.no_grad():
+            # Display the uploaded image
+            st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
+            img = Image.open(uploaded_image)
+            img = ImageOps.exif_transpose(img)  # Corrects orientation
+            img = img.resize((512, 512), resample=Image.BILINEAR)
+            img_tensor = img2tensor(img)
+            output_tensor = new_model.forward(img_tensor)
+            output_img = tensor2img(output_tensor)
+            cv2.imwrite('output.jpg', output_img)
+            processed_image = Image.open('output.jpg')
+            st.image(processed_image, caption='Processed Image', use_column_width=True)
+            st.text("Running the model for trash detection")
+            output_image, class_names = detect(processed_image)
+            # Display the output
+            st.text("Output Image:")
+            # Display "Output Image"
+            st.image(output_image)
+            if len(class_names)==0:
+                st.success("The water is clear!!!")
+            else:
+                st.error(f"Waste Detected!!!\nThe image has {class_names}")
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+
+
+
+

yolo-training.ipynb

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'/kaggle/input/underwater-trash-detection-yolov9/'\nworkingDir = '/kaggle/working/' ","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:13:02.117642Z","iopub.execute_input":"2024-03-09T11:13:02.117955Z","iopub.status.idle":"2024-03-09T11:13:02.122142Z","shell.execute_reply.started":"2024-03-09T11:13:02.117926Z","shell.execute_reply":"2024-03-09T11:13:02.121308Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"!wget  https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:13:02.123274Z","iopub.execute_input":"2024-03-09T11:13:02.123516Z","iopub.status.idle":"2024-03-09T11:13:04.227215Z","shell.execute_reply.started":"2024-03-09T11:13:02.123488Z","shell.execute_reply":"2024-03-09T11:13:04.226195Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"--2024-03-09 11:13:02--  https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt\nResolving github.com (github.com)... 20.248.137.48\nConnecting to github.com (github.com)|20.248.137.48|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://objects.githubusercontent.com/github-production-release-asset-2e65be/759338070/1380cea0-94b4-4d8b-adab-773e081eacee?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240309%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240309T111303Z&X-Amz-Expires=300&X-Amz-Signature=816cefb1f0ded7c873912fd7471d7399b103f5ec730f7208d09c41fbf0decaf5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=759338070&response-content-disposition=attachment%3B%20filename%3Dyolov9-e.pt&response-content-type=application%2Foctet-stream [following]\n--2024-03-09 11:13:03--  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-q","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:13:04.238114Z","iopub.execute_input":"2024-03-09T11:13:04.238383Z","iopub.status.idle":"2024-03-09T11:13:18.508026Z","shell.execute_reply.started":"2024-03-09T11:13:04.238359Z","shell.execute_reply":"2024-03-09T11:13:18.506755Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"import shutil\n\n# Source directory\nsrc_dir = \"/kaggle/input/underwater-trash-detection-yolov9/valid\"\n\n# Destination directory\ndest_dir = \"/kaggle/working/yolov9/valid\"\n\n# Copy all files from source to destination\nshutil.copytree(src_dir, dest_dir)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:17:48.981515Z","iopub.execute_input":"2024-03-09T11:17:48.982181Z","iopub.status.idle":"2024-03-09T11:17:59.795664Z","shell.execute_reply.started":"2024-03-09T11:17:48.982150Z","shell.execute_reply":"2024-03-09T11:17:59.794729Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/yolov9/valid'"},"metadata":{}}]},{"cell_type":"code","source":"import shutil\n\n# Source directory\nsrc_dir = \"/kaggle/input/underwater-trash-detection-yolov9/train\"\n\n# Destination directory\ndest_dir = \"/kaggle/working/yolov9/train\"\n\n# Copy all files from source to destination\nshutil.copytree(src_dir, dest_dir)","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:20:35.506459Z","iopub.execute_input":"2024-03-09T11:20:35.506920Z","iopub.status.idle":"2024-03-09T11:21:20.729740Z","shell.execute_reply.started":"2024-03-09T11:20:35.506882Z","shell.execute_reply":"2024-03-09T11:21:20.728878Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/yolov9/train'"},"metadata":{}}]},{"cell_type":"code","source":"import shutil\n\n# Source directory\nsrc_dir = \"/kaggle/input/underwater-trash-detection-yolov9/test\"\n\n# Destination directory\ndest_dir = \"/kaggle/working/yolov9/test\"\n\n# Copy all files from source to destination\nshutil.copytree(src_dir, dest_dir)","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:21:37.676948Z","iopub.execute_input":"2024-03-09T11:21:37.677658Z","iopub.status.idle":"2024-03-09T11:21:42.021755Z","shell.execute_reply.started":"2024-03-09T11:21:37.677625Z","shell.execute_reply":"2024-03-09T11:21:42.020848Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/yolov9/test'"},"metadata":{}}]},{"cell_type":"code","source":"!python train_dual.py --workers 8 --batch 4  --img 256 --epochs 100 --data /kaggle/input/underwater-trash-detection-yolov9/data.yaml --weights /kaggle/working/yolov9-e.pt --device 0 --cfg /kaggle/working/yolov9/models/detect/yolov9.yaml --hyp /kaggle/working/yolov9/data/hyps/hyp.scratch-high.yaml\n","metadata":{"execution":{"iopub.status.busy":"2024-03-09T11:21:51.866687Z","iopub.execute_input":"2024-03-09T11:21:51.867377Z","iopub.status.idle":"2024-03-09T17:59:41.560031Z","shell.execute_reply.started":"2024-03-09T11:21:51.867341Z","shell.execute_reply":"2024-03-09T17:59:41.558985Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"2024-03-09 11:21:58.805623: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-03-09 11:21:58.805689: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-03-09 11:21:58.807310: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\n\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\n\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice: (30 second timeout) \n\u001b[34m\u001b[1mwandb\u001b[0m: W&B disabled due to login timeout.\n\u001b[34m\u001b[1mtrain_dual: \u001b[0mweights=/kaggle/working/yolov9-e.pt, cfg=/kaggle/working/yolov9/models/detect/yolov9.yaml, data=/kaggle/input/underwater-trash-detection-yolov9/data.yaml, hyp=/kaggle/working/yolov9/data/hyps/hyp.scratch-high.yaml, epochs=100, batch_size=4, imgsz=256, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, flat_cos_lr=False, fixed_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, min_items=0, close_mosaic=0, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\nYOLOv5 🚀 1e33dbb Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)\n\n\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, cls_pw=1.0, dfl=1.5, obj_pw=1.0, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLO 🚀 in ClearML\n\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLO 🚀 runs in Comet\n\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\nOverriding model.yaml nc=80 with nc=15\n\n                 from  n    params  module                                  arguments                     \n  0                -1  1         0  models.common.Silence                   []                            \n  1                -1  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n  2                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n  3                -1  1    212864  models.common.RepNCSPELAN4              [128, 256, 128, 64, 1]        \n  4                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n  5                -1  1    847616  models.common.RepNCSPELAN4              [256, 512, 256, 128, 1]       \n  6                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n  7                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n  8                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n  9                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n 10                -1  1    656896  models.common.SPPELAN                   [512, 512, 256]               \n 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n 12           [-1, 7]  1         0  models.common.Concat                    [1]                           \n 13                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n 14                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n 15           [-1, 5]  1         0  models.common.Concat                    [1]                           \n 16                -1  1    912640  models.common.RepNCSPELAN4              [1024, 256, 256, 128, 1]      \n 17                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n 18          [-1, 13]  1         0  models.common.Concat                    [1]                           \n 19                -1  1   2988544  models.common.RepNCSPELAN4              [768, 512, 512, 256, 1]       \n 20                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n 21          [-1, 10]  1         0  models.common.Concat                    [1]                           \n 22                -1  1   3119616  models.common.RepNCSPELAN4              [1024, 512, 512, 256, 1]      \n 23                 5  1    131328  models.common.CBLinear                  [512, [256]]                  \n 24                 7  1    393984  models.common.CBLinear                  [512, [256, 512]]             \n 25                 9  1    656640  models.common.CBLinear                  [512, [256, 512, 512]]        \n 26                 0  1      1856  models.common.Conv                      [3, 64, 3, 2]                 \n 27                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n 28                -1  1    212864  models.common.RepNCSPELAN4              [128, 256, 128, 64, 1]        \n 29                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n 30  [23, 24, 25, -1]  1         0  models.common.CBFuse                    [[0, 0, 0]]                   \n 31                -1  1    847616  models.common.RepNCSPELAN4              [256, 512, 256, 128, 1]       \n 32                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n 33      [24, 25, -1]  1         0  models.common.CBFuse                    [[1, 1]]                      \n 34                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n 35                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n 36          [25, -1]  1         0  models.common.CBFuse                    [[2]]                         \n 37                -1  1   2857472  models.common.RepNCSPELAN4              [512, 512, 512, 256, 1]       \n 38[31, 34, 37, 16, 19, 22]  1  21575162  models.yolo.DualDDetect                 [15, [512, 512, 512, 256, 512, 512]]\nyolov9 summary: 930 layers, 60829562 parameters, 60829530 gradients, 266.3 GFLOPs\n\nTransferred 438/1412 items from /kaggle/working/yolov9-e.pt\n\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 230 weight(decay=0.0), 247 weight(decay=0.0005), 245 bias\n\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/yolov9/train/labels... 3626 images, 0 background\u001b[0m\n\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/yolov9/train/labels.cache\n\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/yolov9/valid/labels... 1000 images, 0 backgrounds,\u001b[0m\n\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /kaggle/working/yolov9/valid/labels.cache\nPlotting labels to runs/train/exp3/labels.jpg... \n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\nImage sizes 256 train, 256 val\nUsing 4 dataloader workers\nLogging results to \u001b[1mruns/train/exp3\u001b[0m\nStarting training for 100 epochs...\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       0/99      1.73G      5.401      6.228      5.238         18        256:  WARNING ⚠️ TensorBoard graph visualization failure Only tensors, lists, tuples of tensors, or dictionary of tensors can be output from traced functions\n       0/99         2G      5.267      6.123      5.138         11        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891     0.0701    0.00336    0.00133   0.000465\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       1/99         2G      4.972      5.486       4.88         30        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891    0.00105      0.179    0.00162   0.000347\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       2/99         2G      3.845      4.507      3.993          2        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.582     0.0591     0.0301     0.0113\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       3/99         2G      3.108      3.999      3.233          3        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.603     0.0789     0.0523     0.0235\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       4/99         2G      2.847      3.793      2.895          9        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.679     0.0875     0.0713     0.0317\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       5/99         2G      2.679      3.598      2.682          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.495       0.12     0.0913     0.0443\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       6/99         2G      2.604      3.455       2.58         11        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.576      0.155      0.107     0.0563\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       7/99         2G      2.531      3.357      2.506          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.525      0.128      0.115     0.0636\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       8/99         2G      2.462      3.296      2.469         19        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.526      0.142      0.119     0.0644\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n       9/99         2G      2.401      3.216      2.405          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.558      0.166      0.151     0.0749\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      10/99         2G      2.389      3.163      2.374          2        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.543      0.162      0.144       0.08\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      11/99         2G      2.332       3.04      2.325          8        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.696      0.154      0.161     0.0884\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      12/99         2G      2.306      2.974      2.288          7        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.602      0.178      0.185      0.102\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      13/99         2G      2.291      2.967      2.308         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.574      0.195      0.184      0.108\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      14/99         2G      2.258        2.9      2.256         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.598      0.194      0.199      0.118\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      15/99         2G      2.251      2.861       2.26         15        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.537      0.214      0.204      0.122\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      16/99         2G      2.228      2.818      2.239          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.623      0.235      0.217      0.125\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      17/99         2G      2.215      2.775      2.233         14        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.558      0.231      0.216      0.126\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      18/99         2G      2.191      2.727      2.204         10        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.642       0.22      0.237      0.145\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      19/99         2G      2.155      2.695      2.173          4        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891        0.7      0.233      0.256      0.145\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      20/99         2G      2.128      2.642      2.159          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.68      0.245       0.27      0.159\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      21/99         2G      2.128      2.626      2.157          6        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.707      0.246       0.27      0.159\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      22/99         2G      2.098      2.583      2.158         23        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.685      0.275      0.274      0.159\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      23/99         2G      2.118      2.545       2.13         24        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.709      0.266      0.285      0.171\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      24/99         2G       2.08      2.534      2.121          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.676      0.238      0.252      0.156\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      25/99         2G      2.067      2.495       2.11         11        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.61       0.27      0.278      0.171\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      26/99         2G      2.077      2.487      2.113          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.72      0.253      0.295       0.18\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      27/99         2G      2.039      2.435       2.08          4        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.643      0.283      0.306      0.181\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      28/99         2G      2.071       2.43      2.103         13        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.634      0.285      0.294      0.178\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      29/99         2G      2.043      2.408      2.087          6        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.537      0.292       0.31      0.187\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      30/99         2G      2.017      2.367      2.061         13        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.545      0.267      0.291      0.181\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      31/99         2G      2.029      2.351      2.066         18        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.738       0.29      0.338      0.202\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      32/99         2G      2.005      2.346      2.058         28        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.672      0.292      0.333      0.202\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      33/99         2G      2.003      2.349       2.05         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.386      0.353      0.324      0.192\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      34/99         2G      2.001      2.344      2.088         21        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.526      0.334      0.337       0.21\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      35/99         2G      1.994      2.269      2.034          2        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.723      0.282      0.312      0.199\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      36/99         2G      1.981      2.284      2.044         19        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.406      0.357      0.337      0.205\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      37/99         2G      1.951      2.254      2.023          9        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.59      0.325      0.335       0.21\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      38/99         2G       1.96      2.218      2.039          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.638      0.278      0.335       0.21\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      39/99         2G      1.952      2.186      2.022          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.576      0.335      0.369      0.234\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      40/99         2G      1.953      2.241      2.027         10        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.458       0.33      0.341       0.21\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      41/99         2G      1.951      2.181      2.015         25        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.464      0.342      0.351      0.222\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      42/99         2G      1.921       2.17      1.998         12        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.413      0.349      0.347      0.224\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      43/99         2G      1.915       2.14      1.997          9        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.494      0.311      0.343      0.214\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      44/99         2G      1.938      2.165      2.018         10        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.346      0.334      0.339      0.218\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      45/99         2G      1.888      2.102      1.987         10        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.578      0.326      0.383      0.235\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      46/99         2G      1.914      2.092      1.999         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.367      0.385      0.384      0.232\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      47/99         2G      1.935       2.13      2.003          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.416      0.392      0.382      0.241\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      48/99         2G      1.887      2.082      1.974         20        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.428      0.399      0.405      0.253\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      49/99         2G      1.881      2.072      1.979         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.417       0.34      0.353      0.227\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      50/99         2G       1.88      2.044      1.946          3        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.427      0.395      0.395      0.255\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      51/99         2G      1.883      2.021      1.971          9        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.406      0.427      0.398      0.253\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      52/99         2G      1.864      2.027      1.969          7        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.442      0.343      0.368      0.239\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      53/99         2G      1.857          2      1.956          7        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.392      0.411      0.396      0.253\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      54/99         2G      1.845      1.985      1.963         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.45      0.388      0.415      0.269\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      55/99         2G      1.837      1.971      1.948         14        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.459      0.408      0.418      0.264\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      56/99         2G      1.866      2.004      1.978         13        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.394      0.402       0.41      0.264\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      57/99         2G      1.848      1.962      1.954         14        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.442      0.415      0.419      0.263\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      58/99         2G      1.824      1.953      1.933         12        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.502      0.386      0.425       0.27\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      59/99         2G      1.835      1.972      1.948         25        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.561      0.362      0.415      0.267\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      60/99         2G       1.81      1.935      1.946         10        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.507      0.407      0.425      0.273\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      61/99         2G      1.819      1.902      1.921          9        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.484      0.404      0.427      0.272\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      62/99         2G      1.819      1.926      1.935          6        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.549      0.388      0.431       0.27\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      63/99         2G      1.793      1.865      1.898          4        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.509      0.427      0.436      0.273\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      64/99         2G      1.781      1.875      1.911         21        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.482      0.435      0.433      0.275\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      65/99         2G      1.809       1.89       1.92         14        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.508      0.406      0.445      0.286\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      66/99         2G      1.791      1.837        1.9         13        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.464      0.423      0.443      0.284\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      67/99         2G      1.767      1.837      1.896          4        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.488      0.459      0.463      0.294\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      68/99         2G       1.78      1.822       1.91          7        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.498      0.444      0.436      0.281\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      69/99         2G      1.778      1.806      1.893         14        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.551      0.391       0.44      0.286\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      70/99         2G      1.766      1.775      1.889         10        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.531      0.439       0.46      0.294\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      71/99         2G      1.743      1.775      1.879          7        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.554      0.449      0.478       0.31\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      72/99         2G      1.736      1.771      1.875         23        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.551      0.422      0.469      0.298\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      73/99         2G      1.737      1.771       1.87          2        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.511       0.43      0.455      0.293\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      74/99         2G      1.746      1.765      1.876          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.507      0.422      0.456      0.296\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      75/99         2G      1.731      1.742      1.862         15        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.559      0.419      0.469      0.298\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      76/99         2G      1.731      1.741      1.873         19        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.533       0.43      0.477      0.304\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      77/99         2G      1.737      1.749      1.876         15        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.505      0.446      0.477      0.303\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      78/99         2G      1.716      1.719      1.865          4        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.505      0.407      0.462      0.302\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      79/99         2G      1.715       1.71      1.857         16        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.516      0.432      0.475      0.307\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      80/99         2G      1.732      1.727      1.866          9        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.507      0.443      0.472      0.308\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      81/99         2G      1.735       1.72       1.88         35        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.491      0.502        0.5      0.323\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      82/99         2G       1.71      1.683      1.857         31        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.528       0.49      0.505      0.329\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      83/99         2G      1.713      1.682       1.86         13        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.597      0.435      0.491      0.317\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      84/99         2G        1.7      1.673      1.847          3        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.55      0.485      0.505      0.323\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      85/99         2G      1.666      1.649      1.838          6        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.588       0.45      0.518      0.327\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      86/99         2G      1.696      1.674      1.858         14        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.586      0.449      0.514      0.331\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      87/99         2G       1.69      1.647      1.838         17        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.563      0.485      0.522      0.331\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      88/99         2G      1.682      1.646       1.84          3        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.632      0.469      0.518      0.331\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      89/99         2G      1.672      1.626       1.83         12        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.646      0.468      0.517      0.333\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      90/99         2G      1.683      1.648      1.847         15        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.558      0.492      0.524      0.338\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      91/99         2G      1.668      1.617      1.827         12        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.632      0.492      0.524      0.342\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      92/99         2G      1.655      1.594      1.819         15        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.597      0.486      0.532       0.34\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      93/99         2G      1.669      1.605      1.819         11        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.544      0.519      0.536      0.344\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      94/99         2G      1.678      1.617      1.825          3        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.571      0.526       0.54      0.344\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      95/99         2G      1.656      1.609      1.823          5        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.582        0.5       0.54      0.346\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      96/99         2G      1.646      1.573      1.814          8        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891       0.63      0.466      0.541      0.345\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      97/99         2G      1.665      1.587      1.819          6        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.567      0.495      0.542      0.346\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      98/99         2G      1.647      1.572      1.813         13        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.557      0.501      0.545      0.348\n\n      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n      99/99         2G      1.658      1.582      1.806          1        256: 1\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.561      0.523      0.544      0.348\n\n100 epochs completed in 6.608 hours.\nOptimizer stripped from runs/train/exp3/weights/last.pt, 122.4MB\nOptimizer stripped from runs/train/exp3/weights/best.pt, 122.4MB\n\nValidating runs/train/exp3/weights/best.pt...\nFusing layers... \nyolov9 summary: 700 layers, 60788602 parameters, 0 gradients, 265.1 GFLOPs\n                 Class     Images  Instances          P          R      mAP50   \n                   all       1000       1891      0.552      0.503      0.545      0.348\n                  Mask       1000         90      0.896      0.567       0.75      0.561\n                   can       1000         20      0.375       0.25      0.335      0.108\n             cellphone       1000         71      0.867      0.972      0.987      0.839\n           electronics       1000         40      0.361      0.475      0.392      0.186\n               gbottle       1000         82      0.432      0.639      0.606      0.364\n                 glove       1000         55       0.86      0.764      0.863      0.673\n                 metal       1000         22      0.384      0.136      0.157     0.0728\n                  misc       1000         51      0.327      0.275      0.301      0.192\n                   net       1000        148      0.682      0.703      0.689      0.421\n                  pbag       1000        330      0.739      0.927      0.923      0.769\n               pbottle       1000        284      0.522      0.599      0.596      0.366\n               plastic       1000         59       0.37     0.0847      0.141     0.0676\n                   rod       1000          9      0.201      0.222      0.252     0.0756\n            sunglasses       1000          3      0.493      0.329      0.486      0.222\n                  tire       1000        627      0.765      0.601       0.69      0.306\nResults saved to \u001b[1mruns/train/exp3\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"import zipfile\nimport os\nfrom IPython.display import FileLink\n\ndef zip_dir(directory = os.curdir, file_name = 'directory.zip'):\n    os.chdir(directory)\n    zip_ref = zipfile.ZipFile(file_name, mode='w')\n    for folder, _, files in os.walk(directory):\n        for file in files:\n            if file_name in file:\n                pass\n            else:\n                zip_ref.write(os.path.join(folder, file))\n\n    return FileLink(file_name)","metadata":{"execution":{"iopub.status.busy":"2024-03-09T18:10:25.500691Z","iopub.execute_input":"2024-03-09T18:10:25.501588Z","iopub.status.idle":"2024-03-09T18:10:25.507719Z","shell.execute_reply.started":"2024-03-09T18:10:25.501553Z","shell.execute_reply":"2024-03-09T18:10:25.506701Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"zip_dir()","metadata":{"execution":{"iopub.status.busy":"2024-03-09T18:10:27.620253Z","iopub.execute_input":"2024-03-09T18:10:27.620609Z","iopub.status.idle":"2024-03-09T18:10:28.395913Z","shell.execute_reply.started":"2024-03-09T18:10:27.620580Z","shell.execute_reply":"2024-03-09T18:10:28.395023Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"/kaggle/working/yolov9/runs/directory.zip","text/html":"<a href='directory.zip' target='_blank'>directory.zip</a><br>"},"metadata":{}}]},{"cell_type":"code","source":"import shutil\n\n# Directory to zip\ndir_to_zip = \"/kaggle/working/yolov9\"\n\n# Zip file name\nzip_file = \"/kaggle/working/yolov9.zip\"\n\n# Create a zip file of the directory\nshutil.make_archive(zip_file, 'zip', dir_to_zip)\n\n# Print the path to the zip file\nprint(\"Download the zip file from:\", zip_file)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-09T18:12:00.225976Z","iopub.execute_input":"2024-03-09T18:12:00.226356Z","iopub.status.idle":"2024-03-09T18:12:33.355380Z","shell.execute_reply.started":"2024-03-09T18:12:00.226323Z","shell.execute_reply":"2024-03-09T18:12:33.354402Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Download the zip file from: /kaggle/working/yolov9.zip\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}