Tree Detection with YOLOv12¶
Github: https://github.com/RSandAI/VHRTrees
The imagery required for the dataset has been obtained from Google Earth. We have downloaded 218 RGB images, each with a resolution of 1920x1080 pixels and a ground sample distance (GSD) of 0.5 km, covering the districts of Karacabey in Bursa, Turkey and Dikili, Aliağa, Seferihisar, and Selçuk in İzmir, Turkey. A sample image and corresponding minimum boxes for trees can be seen the figure. The dataset has been approximately split as 70% (1023 images), 15% (226 images) and 15% (222 images) for training, validation and testing, respectively.
Let's start by connecting the drive
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from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
Let's import the libraries and select the paths of the files we're going to use.
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import glob
import os
import cv2
import geopandas as gpd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from os import mkdir
from skimage.io import imsave
import skimage
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path_train = '/content/drive/MyDrive/Datasets/VHRTrees_dataset/train'
path_test = '/content/drive/MyDrive/Datasets/VHRTrees_dataset/test'
path_valid = '/content/drive/MyDrive/Datasets/VHRTrees_dataset/validation'
We will create a function to import the json with the annotations and convert it into a dataframe:
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import json
def create_dataframe(annotation_file):
with open(annotation_file, 'r') as f:
data = json.load(f)
images = data['images']
annotations = data['annotations']
categories = data['categories']
# Create dictionaries for efficient lookup
image_lookup = {image['id']: image for image in images}
category_lookup = {category['id']: category['name'] for category in categories}
rows = []
for annotation in annotations:
image_id = annotation['image_id']
image_info = image_lookup[image_id]
category_id = annotation['category_id']
category_name = category_lookup[category_id]
bbox = annotation['bbox']
x_min, y_min, width, height = bbox
rows.append({
'filename': image_info['file_name'],
'width': image_info['width'],
'height': image_info['height'],
'class': category_name,
'xmin': x_min,
'ymin': y_min,
'xmax': x_min + width,
'ymax': y_min + height
})
df = pd.DataFrame(rows)
return df
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train_df = create_dataframe(os.path.join(path_train, '_annotations.coco.json'))
test_df = create_dataframe(os.path.join(path_test, '_annotations.coco.json'))
valid_df = create_dataframe(os.path.join(path_valid, '_annotations.coco.json'))
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train_df
Out[ ]:
| filename | width | height | class | xmin | ymin | xmax | ymax | |
|---|---|---|---|---|---|---|---|---|
| 0 | st2_725_image_0_5_jpg.rf.41fc65d1f642b223a3e74... | 256 | 256 | Tree | 0 | 37 | 17.00 | 58.00 |
| 1 | st2_725_image_0_5_jpg.rf.41fc65d1f642b223a3e74... | 256 | 256 | Tree | 155 | 49 | 186.00 | 77.00 |
| 2 | st2_725_image_0_5_jpg.rf.41fc65d1f642b223a3e74... | 256 | 256 | Tree | 179 | 28 | 195.00 | 43.00 |
| 3 | st2_725_image_0_5_jpg.rf.41fc65d1f642b223a3e74... | 256 | 256 | Tree | 203 | 68 | 223.00 | 83.00 |
| 4 | st2_725_image_0_5_jpg.rf.41fc65d1f642b223a3e74... | 256 | 256 | Tree | 139 | 131 | 153.00 | 139.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60351 | t8_04_08_jpg.rf.ffbd05063ac62d1ca300228da28b8c... | 256 | 256 | Tree | 20 | 1 | 27.50 | 9.75 |
| 60352 | t8_04_08_jpg.rf.ffbd05063ac62d1ca300228da28b8c... | 256 | 256 | Tree | 82 | 150 | 98.00 | 163.25 |
| 60353 | t8_04_08_jpg.rf.ffbd05063ac62d1ca300228da28b8c... | 256 | 256 | Tree | 58 | 60 | 79.50 | 82.75 |
| 60354 | t8_04_08_jpg.rf.ffbd05063ac62d1ca300228da28b8c... | 256 | 256 | Tree | 94 | 184 | 111.78 | 210.67 |
| 60355 | t8_04_08_jpg.rf.ffbd05063ac62d1ca300228da28b8c... | 256 | 256 | Tree | 149 | 122 | 176.41 | 144.22 |
60356 rows × 8 columns
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train_image_list = [f for f in os.listdir(path_train) if f.endswith('.jpg')]
test_image_list = [f for f in os.listdir(path_test) if f.endswith('.jpg')]
valid_image_list = [f for f in os.listdir(path_valid) if f.endswith('.jpg')]
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len(train_image_list)
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1023
Now let's plot an example of images with their objects
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import matplotlib.pyplot as plt
import matplotlib.patches as patches
# Select 10 random images from the training set
num_images_to_plot = 10
image_indices = np.random.choice(len(train_image_list), size=num_images_to_plot, replace=False)
fig, axes = plt.subplots(2, 5, figsize=(20, 8)) # Create a 2x5 grid of subplots
for i, index in enumerate(image_indices):
image_filename = train_image_list[index]
image_path = os.path.join(path_train, image_filename)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
row = i // 5
col = i % 5
ax = axes[row, col]
ax.imshow(image_rgb)
image_df = train_df[train_df['filename'] == image_filename]
for _, row in image_df.iterrows():
xmin = row['xmin']
ymin = row['ymin']
xmax = row['xmax']
ymax = row['ymax']
width = xmax - xmin
height = ymax - ymin
rect = patches.Rectangle((xmin, ymin), width, height, linewidth=2, edgecolor='r', facecolor='none')
ax.add_patch(rect)
ax.text(xmin, ymin - 5, row['class'], color='r') # display class label
ax.axis('off')
plt.tight_layout()
plt.show()
We generate the BoundBoxes and add the ID column
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train_df['class'] = 0
test_df['class'] = 0
valid_df['class'] = 0
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train_df['bounds'] = train_df.apply(lambda row: (int(row['xmin']), int(row['ymin']), int(row['xmax']), int(row['ymax'])), axis=1)
valid_df['bounds'] = valid_df.apply(lambda row: (int(row['xmin']), int(row['ymin']), int(row['xmax']), int(row['ymax'])), axis=1)
test_df['bounds'] = test_df.apply(lambda row: (int(row['xmin']), int(row['ymin']), int(row['xmax']), int(row['ymax'])), axis=1)
Creation of directories for storing data (images and labels)
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!mkdir tree_data
%cd tree_data
/content/tree_data
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!mkdir images
!mkdir labels
%cd images
!mkdir train
!mkdir validation
%cd ..
%cd labels
!mkdir train
!mkdir validation
%cd ..
%cd ..
/content/tree_data/images /content/tree_data /content/tree_data/labels /content/tree_data /content
Let's copy images and labels to the created folders, converting to x-center, y-center, x-extend, y-extend and normalizing with the image size:
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import numpy as np
import shutil
import ast
import glob
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INPUT_PATH = '/content/drive/MyDrive/Datasets/VHRTrees_dataset/'
OUTPUT_PATH = '/content/tree_data'
im_size = 256
def process_data(data, data_type='train'):
for _, row in data.iterrows():
image_name = os.path.splitext(row['filename'])[0]
bounding_boxes = row['bboxes'] # Changed to list of tuples
classes = row['classes']
yolo_data = []
for bbox, Class in zip(bounding_boxes, classes):
x_min = bbox[0]
y_min = bbox[1]
x_max = bbox[2]
y_max = bbox[3]
x_center = (x_min + x_max) / 2.0 / im_size
y_center = (y_min + y_max) / 2.0 / im_size
x_extend = (x_max - x_min) / im_size
y_extend = (y_max - y_min) / im_size
yolo_data.append([Class, x_center, y_center, x_extend, y_extend])
yoy_data = np.array(yolo_data)
np.savetxt(
os.path.join(OUTPUT_PATH, f"labels/{data_type}/{image_name}.txt"),
yolo_data,
fmt = ["%d", "%f", "%f", "%f", "%f"]
)
print(os.path.join(INPUT_PATH, f"{data_type}/{image_name}.jpg"))
print(os.path.join(OUTPUT_PATH, f"images/{data_type}/{image_name}.jpg"))
shutil.copyfile(
os.path.join(INPUT_PATH, f"{data_type}/{image_name}.jpg"),
os.path.join(OUTPUT_PATH, f"images/{data_type}/{image_name}.jpg")
)
df_train_group = train_df.groupby('filename')['bounds'].apply(list).reset_index(name='bboxes')
df_train_group['classes'] = train_df.groupby('filename')['class'].apply(list).reset_index(drop=True)
df_valid_group = valid_df.groupby('filename')['bounds'].apply(list).reset_index(name='bboxes')
df_valid_group['classes'] = valid_df.groupby('filename')['class'].apply(list).reset_index(drop=True)
process_data(df_train_group, data_type='train')
process_data(df_valid_group, data_type='validation')
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Conversion check:
In [ ]:
f = open('/content/tree_data/labels/train/'+os.listdir("/content/tree_data/labels/train/")[3])
print(f.name)
for l in f:
print(l)
/content/tree_data/labels/train/a13_01_01_jpg.rf.857a2a3ee3e0be828957cd2b4a08c368.txt 0 0.632812 0.265625 0.062500 0.070312 0 0.576172 0.107422 0.082031 0.113281 0 0.371094 0.214844 0.062500 0.078125 0 0.279297 0.167969 0.082031 0.101562 0 0.205078 0.310547 0.082031 0.160156 0 0.039062 0.312500 0.078125 0.117188 0 0.021484 0.429688 0.042969 0.062500 0 0.128906 0.570312 0.156250 0.171875 0 0.187500 0.486328 0.085938 0.089844 0 0.283203 0.572266 0.074219 0.089844 0 0.314453 0.433594 0.058594 0.093750 0 0.414062 0.505859 0.078125 0.089844 0 0.408203 0.332031 0.066406 0.070312 0 0.525391 0.410156 0.105469 0.156250 0 0.673828 0.427734 0.097656 0.152344 0 0.775391 0.500000 0.074219 0.093750 0 0.830078 0.410156 0.066406 0.085938 0 0.867188 0.525391 0.070312 0.105469 0 0.925781 0.427734 0.070312 0.082031 0 0.958984 0.544922 0.066406 0.066406 0 0.923828 0.304688 0.058594 0.093750 0 0.953125 0.222656 0.093750 0.125000 0 0.906250 0.089844 0.070312 0.093750 0 0.492188 0.833984 0.039062 0.042969 0 0.820312 0.066406 0.078125 0.093750 0 0.648438 0.033203 0.117188 0.066406 0 0.853516 0.761719 0.074219 0.093750 0 0.779297 0.826172 0.058594 0.082031 0 0.736328 0.908203 0.082031 0.089844 0 0.851562 0.927734 0.093750 0.121094 0 0.496094 0.968750 0.093750 0.062500 0 0.398438 0.960938 0.046875 0.078125 0 0.312500 0.953125 0.125000 0.093750 0 0.216797 0.894531 0.074219 0.085938 0 0.208984 0.744141 0.082031 0.097656 0 0.294922 0.816406 0.136719 0.078125 0 0.566406 0.738281 0.078125 0.101562 0 0.693359 0.646484 0.097656 0.105469 0 0.781250 0.650391 0.039062 0.050781 0 0.923828 0.658203 0.042969 0.082031 0 0.978516 0.869141 0.042969 0.113281 0 0.949219 0.849609 0.039062 0.050781 0 0.812500 0.291016 0.062500 0.082031 0 0.052734 0.949219 0.089844 0.093750 0 0.117188 0.882812 0.140625 0.125000 0 0.062500 0.771484 0.093750 0.097656 0 0.015625 0.677734 0.031250 0.105469 0 0.076172 0.628906 0.074219 0.093750 0 0.125000 0.083984 0.062500 0.089844 0 0.160156 0.021484 0.093750 0.042969 0 0.253906 0.046875 0.070312 0.085938 0 0.500000 0.013672 0.062500 0.027344 0 0.357422 0.021484 0.050781 0.042969 0 0.021484 0.164062 0.035156 0.046875 0 0.578125 0.599609 0.046875 0.058594 0 0.513672 0.542969 0.066406 0.062500 0 0.480469 0.101562 0.085938 0.085938 0 0.560547 0.257812 0.074219 0.070312 0 0.388672 0.763672 0.042969 0.042969 0 0.574219 0.847656 0.062500 0.054688 0 0.476562 0.703125 0.117188 0.195312 0 0.742188 0.269531 0.078125 0.140625
In [ ]:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
def plot_image_with_boxes_from_txt(image_path, txt_path):
try:
img = Image.open(image_path)
fig, ax = plt.subplots(1)
ax.imshow(img)
with open(txt_path, 'r') as file:
for line in file:
parts = line.strip().split()
if len(parts) >= 5: # Ensure enough elements
class_id = int(parts[0])
x_center = float(parts[1])
y_center = float(parts[2])
width = float(parts[3])
height = float(parts[4])
image_width, image_height = img.size
xmin = int((x_center - width / 2) * image_width)
ymin = int((y_center - height / 2) * image_height)
xmax = int((x_center + width / 2) * image_width)
ymax = int((y_center + height / 2) * image_height)
width = xmax - xmin
height = ymax - ymin
rect = patches.Rectangle((xmin, ymin), width, height, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
plt.show()
except FileNotFoundError:
print(f"Error: Image or text file not found at {image_path} or {txt_path}")
except Exception as e:
print(f"An error occurred: {e}")
# Example usage (replace with your actual paths)
# Make sure that corresponding image and text files exist.
image_folder = "/content/tree_data/images/train" #update to your path
txt_folder = "/content/tree_data/labels/train" #update to your path
image_files = [f for f in os.listdir(image_folder) if f.endswith(('.png', '.jpg', '.jpeg'))]
for image_file in image_files[0:4]:
image_path = os.path.join(image_folder, image_file)
txt_file = image_file[:-4] + ".txt" # Assuming txt file has same name as image
txt_path = os.path.join(txt_folder, txt_file)
plot_image_with_boxes_from_txt(image_path, txt_path)
We create the YAML file with the image paths
In [ ]:
%%writefile tree_detection.yaml
train: /content/tree_data/images/train
val: /content/tree_data/images/validation
nc: 1
names: ['Tree']
Writing tree_detection.yaml
Let's clone YOLOv12's github and install the Ultralyltics library
In [ ]:
!git clone https://github.com/sunsmarterjie/yolov12
%cd yolov12
Cloning into 'yolov12'... remote: Enumerating objects: 814, done. remote: Counting objects: 100% (107/107), done. remote: Compressing objects: 100% (96/96), done. remote: Total 814 (delta 58), reused 19 (delta 11), pack-reused 707 (from 2) Receiving objects: 100% (814/814), 1.47 MiB | 12.63 MiB/s, done. Resolving deltas: 100% (348/348), done. /content/yolov12
In [ ]:
!pip install ultralytics
from IPython import display
import ultralytics
ultralytics.checks()
Ultralytics 8.3.63 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB) Setup complete ✅ (2 CPUs, 12.7 GB RAM, 33.3/235.7 GB disk)
In [ ]:
from ultralytics import YOLO
In [ ]:
import os
HOME = os.getcwd()
print(HOME)
/content/yolov12
We can start training for 200 epochs
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%cd {HOME}
!yolo task=detect mode=train model=yolo12x.pt data=/content/tree_detection.yaml epochs=200 imgsz=256
/content/yolov12 Creating new Ultralytics Settings v0.0.6 file ✅ View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json' Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings. Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12x.pt to 'yolo12x.pt'... 100% 114M/114M [00:00<00:00, 266MB/s] Ultralytics 8.3.78 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB) engine/trainer: task=detect, mode=train, model=yolo12x.pt, data=/content/tree_detection.yaml, epochs=200, time=None, patience=100, batch=16, imgsz=256, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=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, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'... 100% 755k/755k [00:00<00:00, 14.6MB/s] WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1740147662.028750 3917 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1740147662.122700 3917 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 2784 ultralytics.nn.modules.conv.Conv [3, 96, 3, 2] 1 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] 2 -1 2 389760 ultralytics.nn.modules.block.C3k2 [192, 384, 2, True, 0.25] 3 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] 4 -1 2 1553664 ultralytics.nn.modules.block.C3k2 [384, 768, 2, True, 0.25] 5 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] 6 -1 4 9512128 ultralytics.nn.modules.block.A2C2f [768, 768, 4, True, 4, True, 1.2] 7 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] 8 -1 4 9512128 ultralytics.nn.modules.block.A2C2f [768, 768, 4, True, 1, True, 1.2] 9 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 10 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 11 -1 2 4727040 ultralytics.nn.modules.block.A2C2f [1536, 768, 2, False, -1, True, 1.2] 12 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 13 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 14 -1 2 1331328 ultralytics.nn.modules.block.A2C2f [1536, 384, 2, False, -1, True, 1.2] 15 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] 16 [-1, 11] 1 0 ultralytics.nn.modules.conv.Concat [1] 17 -1 2 4579584 ultralytics.nn.modules.block.A2C2f [1152, 768, 2, False, -1, True, 1.2] 18 -1 1 5309952 ultralytics.nn.modules.conv.Conv [768, 768, 3, 2] 19 [-1, 8] 1 0 ultralytics.nn.modules.conv.Concat [1] 20 -1 2 5612544 ultralytics.nn.modules.block.C3k2 [1536, 768, 2, True] 21 [14, 17, 20] 1 3146707 ultralytics.nn.modules.head.Detect [1, [384, 768, 768]] YOLOv12x summary: 488 layers, 59,119,539 parameters, 59,119,523 gradients, 199.8 GFLOPs Transferred 1239/1245 items from pretrained weights TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/ Freezing layer 'model.21.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'... 100% 5.35M/5.35M [00:00<00:00, 64.8MB/s] AMP: checks passed ✅ train: Scanning /content/tree_data/labels/train... 1023 images, 0 backgrounds, 0 corrupt: 100% 1023/1023 [00:00<00:00, 1363.62it/s] train: WARNING ⚠️ /content/tree_data/images/train/a10_05_06_jpg.rf.ded1c5a0ab47883bd283362fb1525c64.jpg: 1 duplicate labels removed train: New cache created: /content/tree_data/labels/train.cache albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Scanning /content/tree_data/labels/validation... 226 images, 0 backgrounds, 0 corrupt: 100% 226/226 [00:00<00:00, 579.98it/s] val: New cache created: /content/tree_data/labels/validation.cache Plotting labels to runs/detect/train/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 205 weight(decay=0.0), 214 weight(decay=0.0005), 211 bias(decay=0.0) TensorBoard: model graph visualization added ✅ Image sizes 256 train, 256 val Using 2 dataloader workers Logging results to runs/detect/train Starting training for 200 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/200 5.05G 1.769 1.027 1.098 1290 256: 100% 64/64 [00:25<00:00, 2.55it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.07it/s] all 226 13227 0.788 0.706 0.64 0.316 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 2/200 4.18G 1.596 0.6545 0.9915 1029 256: 100% 64/64 [00:20<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.90it/s] all 226 13227 0.722 0.919 0.754 0.355 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/200 4.31G 1.643 0.6975 0.9934 1455 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.887 0.879 0.91 0.45 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 4/200 4.38G 1.536 0.6513 0.9649 842 256: 100% 64/64 [00:21<00:00, 3.03it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.04it/s] all 226 13227 0.923 0.913 0.946 0.479 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 5/200 4.43G 1.543 0.6554 0.9564 1111 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.87it/s] all 226 13227 0.932 0.915 0.954 0.523 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 6/200 4.44G 1.473 0.6287 0.9484 1338 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.921 0.918 0.953 0.496 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 7/200 4.34G 1.472 0.6319 0.9431 1408 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.21it/s] all 226 13227 0.933 0.931 0.965 0.542 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 8/200 4.28G 1.458 0.6207 0.9372 1165 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.937 0.927 0.959 0.538 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 9/200 4.21G 1.446 0.6177 0.9298 798 256: 100% 64/64 [00:20<00:00, 3.06it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.93it/s] all 226 13227 0.94 0.939 0.966 0.568 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 10/200 4.43G 1.426 0.6071 0.9274 1177 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.10it/s] all 226 13227 0.939 0.931 0.964 0.568 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 11/200 4.51G 1.432 0.6038 0.9309 1045 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.935 0.918 0.957 0.568 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 12/200 4.48G 1.421 0.6042 0.926 1573 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.932 0.933 0.954 0.56 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/200 4.3G 1.375 0.5858 0.9198 1075 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.939 0.94 0.967 0.571 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 14/200 4.54G 1.382 0.5888 0.9219 1137 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.13it/s] all 226 13227 0.941 0.928 0.965 0.563 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 15/200 4.2G 1.39 0.5878 0.9245 1060 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.937 0.937 0.965 0.556 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 16/200 4.28G 1.412 0.6002 0.9221 1041 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.942 0.933 0.966 0.557 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 17/200 4.46G 1.368 0.5806 0.9182 1218 256: 100% 64/64 [00:20<00:00, 3.11it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.03it/s] all 226 13227 0.944 0.938 0.969 0.58 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 18/200 4.28G 1.369 0.5749 0.916 752 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.78it/s] all 226 13227 0.944 0.932 0.971 0.578 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 19/200 4.37G 1.374 0.583 0.9186 1079 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.84it/s] all 226 13227 0.942 0.928 0.97 0.553 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 20/200 4.62G 1.403 0.5874 0.9192 1299 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.92it/s] all 226 13227 0.946 0.933 0.971 0.58 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 21/200 4.45G 1.366 0.5755 0.92 1184 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.941 0.94 0.969 0.577 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 22/200 4.36G 1.376 0.5747 0.9201 1068 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.21it/s] all 226 13227 0.942 0.938 0.97 0.579 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 23/200 4.42G 1.37 0.579 0.9123 1477 256: 100% 64/64 [00:20<00:00, 3.06it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.20it/s] all 226 13227 0.942 0.938 0.973 0.582 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 24/200 4.29G 1.345 0.5664 0.913 1260 256: 100% 64/64 [00:22<00:00, 2.86it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.946 0.94 0.972 0.589 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 25/200 4.38G 1.364 0.5735 0.9145 1158 256: 100% 64/64 [00:21<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.13it/s] all 226 13227 0.941 0.944 0.973 0.578 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 26/200 4.45G 1.348 0.567 0.9159 1328 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.08it/s] all 226 13227 0.947 0.937 0.973 0.585 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 27/200 4.54G 1.367 0.5771 0.9137 1123 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.01it/s] all 226 13227 0.941 0.944 0.973 0.59 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 28/200 4.36G 1.342 0.5623 0.9093 1285 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.941 0.943 0.971 0.588 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 29/200 4.51G 1.342 0.5596 0.9093 1364 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.949 0.942 0.975 0.59 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 30/200 4.43G 1.355 0.5634 0.9136 931 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.945 0.937 0.974 0.578 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 31/200 4.38G 1.339 0.5649 0.9107 1028 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.943 0.947 0.976 0.6 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 32/200 4.24G 1.333 0.5573 0.9091 894 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.32it/s] all 226 13227 0.945 0.94 0.975 0.583 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 33/200 4.4G 1.318 0.5509 0.9088 1227 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.32it/s] all 226 13227 0.946 0.94 0.974 0.591 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 34/200 4.54G 1.313 0.5518 0.9058 1384 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.11it/s] all 226 13227 0.941 0.945 0.973 0.595 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 35/200 4.28G 1.337 0.5584 0.9104 1052 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.14it/s] all 226 13227 0.945 0.941 0.974 0.584 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 36/200 4.5G 1.325 0.5594 0.9075 1019 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.15it/s] all 226 13227 0.941 0.943 0.973 0.585 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 37/200 4.34G 1.335 0.5623 0.909 1073 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.946 0.941 0.975 0.596 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 38/200 4.49G 1.316 0.5526 0.9065 1085 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.947 0.94 0.975 0.589 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 39/200 4.33G 1.325 0.5505 0.9054 899 256: 100% 64/64 [00:20<00:00, 3.11it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.92it/s] all 226 13227 0.949 0.938 0.974 0.591 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 40/200 4.47G 1.301 0.5442 0.903 898 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.945 0.94 0.974 0.596 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 41/200 4.37G 1.322 0.552 0.908 1185 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.33it/s] all 226 13227 0.95 0.937 0.975 0.594 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 42/200 4.24G 1.305 0.5441 0.9018 1334 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.15it/s] all 226 13227 0.949 0.942 0.976 0.593 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 43/200 4.25G 1.299 0.5371 0.9014 1419 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.95 0.948 0.979 0.605 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 44/200 4.48G 1.331 0.5506 0.9087 1019 256: 100% 64/64 [00:22<00:00, 2.90it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.952 0.939 0.977 0.598 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 45/200 4.34G 1.307 0.5417 0.9047 1386 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.87it/s] all 226 13227 0.946 0.943 0.975 0.601 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 46/200 4.45G 1.301 0.5438 0.9046 1029 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.943 0.945 0.975 0.596 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 47/200 4.38G 1.313 0.5455 0.9023 1330 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.945 0.946 0.977 0.605 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 48/200 4.41G 1.301 0.5394 0.9032 971 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.01it/s] all 226 13227 0.952 0.941 0.976 0.598 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 49/200 4.24G 1.31 0.5425 0.9035 1223 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.03it/s] all 226 13227 0.949 0.945 0.975 0.599 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 50/200 4.64G 1.314 0.5449 0.905 1023 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.06it/s] all 226 13227 0.946 0.943 0.975 0.6 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 51/200 4.37G 1.303 0.5385 0.8996 1134 256: 100% 64/64 [00:20<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.94it/s] all 226 13227 0.943 0.946 0.975 0.606 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 52/200 4.36G 1.294 0.5386 0.9051 1247 256: 100% 64/64 [00:21<00:00, 2.91it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.947 0.942 0.976 0.604 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 53/200 4.43G 1.31 0.5396 0.8999 1044 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.943 0.95 0.977 0.608 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 54/200 4.29G 1.3 0.5377 0.9001 1316 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.946 0.945 0.977 0.608 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 55/200 4.48G 1.298 0.5355 0.9027 972 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.942 0.952 0.973 0.589 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 56/200 4.38G 1.287 0.5309 0.8993 1081 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.946 0.948 0.977 0.606 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 57/200 4.31G 1.28 0.5291 0.899 1506 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.947 0.95 0.979 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 58/200 4.24G 1.291 0.5286 0.8997 1012 256: 100% 64/64 [00:20<00:00, 3.07it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.946 0.944 0.977 0.607 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 59/200 4.52G 1.279 0.5281 0.8998 1534 256: 100% 64/64 [00:20<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.945 0.948 0.978 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 60/200 4.47G 1.282 0.5269 0.8972 1130 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.00it/s] all 226 13227 0.947 0.947 0.977 0.606 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 61/200 4.29G 1.286 0.5366 0.8936 1512 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.947 0.944 0.976 0.6 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 62/200 4.53G 1.313 0.5401 0.9035 1496 256: 100% 64/64 [00:20<00:00, 3.06it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.946 0.947 0.976 0.607 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 63/200 4.41G 1.291 0.532 0.8977 1463 256: 100% 64/64 [00:20<00:00, 3.07it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.12it/s] all 226 13227 0.949 0.942 0.977 0.601 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 64/200 4.4G 1.295 0.5334 0.9038 918 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.95 0.941 0.977 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 65/200 4.34G 1.292 0.5326 0.9006 1008 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.33it/s] all 226 13227 0.951 0.943 0.977 0.608 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 66/200 4.34G 1.309 0.5347 0.9015 1127 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.35it/s] all 226 13227 0.949 0.943 0.975 0.598 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 67/200 4.26G 1.285 0.5299 0.8959 1029 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.07it/s] all 226 13227 0.944 0.95 0.977 0.608 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 68/200 4.55G 1.26 0.5228 0.896 1263 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.95 0.945 0.977 0.603 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 69/200 4.36G 1.279 0.5227 0.894 1395 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.949 0.947 0.977 0.605 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 70/200 4.3G 1.274 0.522 0.893 1123 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.946 0.939 0.975 0.6 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 71/200 4.4G 1.274 0.5276 0.8984 1122 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.941 0.947 0.976 0.606 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 72/200 4.49G 1.266 0.5253 0.8963 1241 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.95 0.943 0.977 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 73/200 4.36G 1.282 0.5224 0.8971 1292 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.948 0.947 0.977 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 74/200 4.53G 1.264 0.5181 0.8953 916 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.95 0.947 0.978 0.614 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 75/200 4.29G 1.279 0.5216 0.8993 1241 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.944 0.955 0.979 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 76/200 4.36G 1.279 0.5255 0.896 1217 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.946 0.949 0.978 0.603 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 77/200 4.27G 1.276 0.5235 0.8942 1169 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.16it/s] all 226 13227 0.942 0.949 0.976 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 78/200 4.27G 1.27 0.5211 0.8943 1108 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.944 0.953 0.979 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 79/200 4.22G 1.265 0.518 0.8972 1056 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.948 0.945 0.976 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 80/200 4.41G 1.272 0.5221 0.8983 836 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.946 0.951 0.977 0.602 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 81/200 4.24G 1.272 0.5192 0.8948 1856 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.949 0.949 0.978 0.608 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 82/200 4.31G 1.265 0.5166 0.8938 947 256: 100% 64/64 [00:21<00:00, 2.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.948 0.949 0.976 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 83/200 4.51G 1.258 0.5136 0.8954 1211 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.90it/s] all 226 13227 0.947 0.949 0.977 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 84/200 4.36G 1.255 0.5111 0.891 1136 256: 100% 64/64 [00:20<00:00, 3.11it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.83it/s] all 226 13227 0.95 0.949 0.978 0.613 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 85/200 4.32G 1.273 0.516 0.8947 1069 256: 100% 64/64 [00:20<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.12it/s] all 226 13227 0.946 0.952 0.979 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 86/200 4.4G 1.249 0.5093 0.8924 1201 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.945 0.954 0.98 0.611 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 87/200 4.52G 1.251 0.5097 0.8932 876 256: 100% 64/64 [00:20<00:00, 3.07it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.17it/s] all 226 13227 0.947 0.952 0.979 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 88/200 4.54G 1.268 0.516 0.8968 1278 256: 100% 64/64 [00:20<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.952 0.944 0.977 0.604 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 89/200 4.64G 1.264 0.5127 0.8905 982 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.946 0.952 0.978 0.613 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 90/200 4.4G 1.266 0.5166 0.8928 1278 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.946 0.95 0.978 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 91/200 4.38G 1.255 0.5109 0.8903 1306 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.951 0.945 0.978 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 92/200 4.28G 1.245 0.5076 0.8922 866 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.951 0.945 0.977 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 93/200 4.26G 1.268 0.5133 0.8912 810 256: 100% 64/64 [00:21<00:00, 2.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.942 0.951 0.976 0.616 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 94/200 4.39G 1.243 0.5071 0.891 889 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.949 0.946 0.978 0.616 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 95/200 4.35G 1.276 0.5175 0.8928 1581 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.21it/s] all 226 13227 0.949 0.947 0.977 0.611 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 96/200 4.65G 1.261 0.5108 0.8946 1231 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.00it/s] all 226 13227 0.947 0.948 0.978 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 97/200 4.54G 1.263 0.5123 0.8939 908 256: 100% 64/64 [00:21<00:00, 2.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.21it/s] all 226 13227 0.946 0.949 0.978 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 98/200 4.34G 1.264 0.5143 0.8955 1304 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.19it/s] all 226 13227 0.946 0.95 0.978 0.606 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 99/200 4.27G 1.231 0.5024 0.8916 960 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.11it/s] all 226 13227 0.952 0.947 0.978 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 100/200 4.38G 1.247 0.5033 0.894 1333 256: 100% 64/64 [00:20<00:00, 3.06it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.948 0.947 0.978 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 101/200 4.46G 1.268 0.5097 0.8901 850 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.947 0.95 0.978 0.613 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 102/200 4.52G 1.255 0.5099 0.8946 985 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.946 0.947 0.976 0.609 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 103/200 4.34G 1.256 0.5107 0.8957 1327 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.08it/s] all 226 13227 0.941 0.956 0.977 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 104/200 4.41G 1.244 0.5061 0.8908 1096 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.15it/s] all 226 13227 0.948 0.952 0.979 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 105/200 4.25G 1.239 0.5039 0.8869 992 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.21it/s] all 226 13227 0.948 0.95 0.979 0.618 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 106/200 4.39G 1.239 0.5041 0.8898 1081 256: 100% 64/64 [00:21<00:00, 2.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.18it/s] all 226 13227 0.947 0.948 0.976 0.61 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 107/200 4.41G 1.241 0.5022 0.8892 1162 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.947 0.95 0.976 0.613 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 108/200 4.57G 1.237 0.5027 0.8925 1138 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.946 0.95 0.978 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 109/200 4.25G 1.248 0.5053 0.8906 1346 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.948 0.952 0.978 0.612 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 110/200 4.37G 1.253 0.5065 0.8952 1011 256: 100% 64/64 [00:20<00:00, 3.07it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.92it/s] all 226 13227 0.945 0.956 0.979 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 111/200 4.41G 1.24 0.5 0.8896 1325 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.31it/s] all 226 13227 0.949 0.951 0.978 0.618 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 112/200 4.71G 1.24 0.4991 0.8909 1037 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.82it/s] all 226 13227 0.945 0.95 0.976 0.615 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 113/200 4.31G 1.242 0.5049 0.8886 885 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.95 0.95 0.978 0.616 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 114/200 4.44G 1.244 0.5023 0.8907 1012 256: 100% 64/64 [00:20<00:00, 3.06it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.01it/s] all 226 13227 0.95 0.951 0.979 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 115/200 4.37G 1.229 0.4967 0.8865 1084 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.951 0.949 0.979 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 116/200 4.38G 1.25 0.5027 0.8917 1013 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.947 0.952 0.979 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 117/200 4.53G 1.226 0.4959 0.8894 985 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.943 0.953 0.978 0.616 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 118/200 4.39G 1.214 0.4894 0.8866 1404 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.949 0.95 0.978 0.62 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 119/200 4.24G 1.242 0.5027 0.8893 608 256: 100% 64/64 [00:20<00:00, 3.12it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.95it/s] all 226 13227 0.948 0.948 0.977 0.618 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 120/200 4.25G 1.222 0.4949 0.8898 1092 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.942 0.956 0.976 0.616 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 121/200 4.26G 1.228 0.4983 0.8872 1314 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.944 0.952 0.978 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 122/200 4.42G 1.242 0.4976 0.889 1164 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.18it/s] all 226 13227 0.947 0.949 0.977 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 123/200 4.36G 1.22 0.4904 0.8861 767 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.94 0.957 0.977 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 124/200 4.33G 1.237 0.4931 0.8875 1326 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.09it/s] all 226 13227 0.947 0.951 0.977 0.615 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 125/200 4.5G 1.223 0.4953 0.8871 950 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.951 0.949 0.979 0.622 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 126/200 4.39G 1.218 0.4913 0.888 1347 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.18it/s] all 226 13227 0.952 0.945 0.978 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 127/200 4.49G 1.224 0.4922 0.8887 1173 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.954 0.945 0.98 0.62 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 128/200 4.33G 1.231 0.495 0.8896 1081 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.31it/s] all 226 13227 0.95 0.947 0.978 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 129/200 4.66G 1.218 0.4907 0.8846 951 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.942 0.956 0.978 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 130/200 4.32G 1.218 0.4884 0.8864 1160 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.944 0.954 0.978 0.62 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 131/200 4.37G 1.217 0.4913 0.8867 1202 256: 100% 64/64 [00:20<00:00, 3.09it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.81it/s] all 226 13227 0.946 0.952 0.978 0.622 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 132/200 4.23G 1.208 0.4849 0.8864 1052 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.944 0.953 0.978 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 133/200 4.25G 1.216 0.4889 0.8831 1010 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.945 0.954 0.977 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 134/200 4.46G 1.227 0.4902 0.8871 973 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.946 0.953 0.979 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 135/200 4.4G 1.23 0.4944 0.8859 1277 256: 100% 64/64 [00:20<00:00, 3.07it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.946 0.951 0.978 0.62 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 136/200 4.55G 1.203 0.4813 0.885 1358 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.93it/s] all 226 13227 0.946 0.956 0.978 0.618 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 137/200 4.28G 1.222 0.4861 0.8862 1102 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.945 0.955 0.979 0.617 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 138/200 4.27G 1.231 0.4898 0.8887 1064 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.947 0.953 0.977 0.619 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 139/200 4.51G 1.217 0.4849 0.8857 912 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.948 0.95 0.978 0.621 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 140/200 4.25G 1.229 0.4879 0.8898 1353 256: 100% 64/64 [00:21<00:00, 3.03it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.95 0.951 0.977 0.618 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 141/200 4.34G 1.214 0.4827 0.8887 1271 256: 100% 64/64 [00:21<00:00, 3.03it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.949 0.952 0.977 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 142/200 4.25G 1.21 0.4835 0.8865 999 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.91it/s] all 226 13227 0.947 0.953 0.978 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 143/200 4.39G 1.204 0.4822 0.8842 1614 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.96it/s] all 226 13227 0.951 0.95 0.979 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 144/200 4.35G 1.214 0.4797 0.885 1236 256: 100% 64/64 [00:20<00:00, 3.14it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.84it/s] all 226 13227 0.95 0.95 0.977 0.622 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 145/200 4.45G 1.214 0.4863 0.8861 1508 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.947 0.954 0.979 0.618 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 146/200 4.27G 1.209 0.4805 0.8854 1347 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.948 0.952 0.977 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 147/200 4.6G 1.236 0.4896 0.8871 1346 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.945 0.953 0.976 0.621 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 148/200 4.3G 1.202 0.4771 0.8868 1180 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.948 0.953 0.979 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 149/200 4.49G 1.203 0.4753 0.8834 1077 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.95 0.951 0.978 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 150/200 4.38G 1.203 0.4797 0.88 1364 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.95 0.95 0.979 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 151/200 4.37G 1.199 0.4761 0.8811 1475 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.948 0.956 0.979 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 152/200 4.58G 1.198 0.4768 0.8837 1182 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.948 0.954 0.979 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 153/200 4.72G 1.189 0.4732 0.8827 1091 256: 100% 64/64 [00:20<00:00, 3.12it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.01it/s] all 226 13227 0.95 0.95 0.978 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 154/200 4.66G 1.197 0.4796 0.8806 1135 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.948 0.952 0.977 0.622 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 155/200 4.39G 1.199 0.4775 0.8827 1173 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.31it/s] all 226 13227 0.947 0.951 0.978 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 156/200 4.39G 1.204 0.4766 0.8828 1370 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.25it/s] all 226 13227 0.949 0.953 0.978 0.621 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 157/200 4.42G 1.203 0.4782 0.882 1340 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.947 0.951 0.976 0.622 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 158/200 4.5G 1.197 0.4763 0.8815 1061 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.00it/s] all 226 13227 0.953 0.945 0.977 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 159/200 4.51G 1.192 0.4704 0.881 1706 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.954 0.947 0.977 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 160/200 4.34G 1.187 0.4702 0.8833 1153 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.32it/s] all 226 13227 0.945 0.955 0.978 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 161/200 4.24G 1.205 0.4767 0.8811 1332 256: 100% 64/64 [00:20<00:00, 3.11it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.12it/s] all 226 13227 0.948 0.954 0.979 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 162/200 4.25G 1.199 0.4731 0.8824 1741 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.946 0.956 0.976 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 163/200 4.33G 1.189 0.4703 0.8811 973 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.35it/s] all 226 13227 0.946 0.954 0.976 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 164/200 4.29G 1.193 0.4752 0.8793 1241 256: 100% 64/64 [00:21<00:00, 3.00it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.947 0.954 0.977 0.623 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 165/200 4.63G 1.192 0.4715 0.8829 871 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.949 0.953 0.977 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 166/200 4.53G 1.187 0.4708 0.8807 1024 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.28it/s] all 226 13227 0.947 0.956 0.977 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 167/200 4.23G 1.189 0.4714 0.884 1065 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.947 0.956 0.978 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 168/200 4.38G 1.19 0.4694 0.878 1364 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.95 0.95 0.977 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 169/200 4.66G 1.181 0.4669 0.8807 1042 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.949 0.954 0.978 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 170/200 4.62G 1.185 0.4688 0.8794 1573 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.948 0.951 0.977 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 171/200 4.3G 1.179 0.4627 0.8821 1144 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.87it/s] all 226 13227 0.95 0.951 0.978 0.628 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 172/200 4.32G 1.171 0.4637 0.8772 1530 256: 100% 64/64 [00:21<00:00, 3.03it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.14it/s] all 226 13227 0.948 0.953 0.976 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 173/200 4.43G 1.18 0.4661 0.8784 1028 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.945 0.955 0.976 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 174/200 4.36G 1.175 0.4631 0.8783 1097 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.29it/s] all 226 13227 0.947 0.956 0.979 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 175/200 4.24G 1.175 0.4649 0.8758 1453 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.95 0.952 0.977 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 176/200 4.36G 1.162 0.4594 0.881 842 256: 100% 64/64 [00:21<00:00, 3.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.33it/s] all 226 13227 0.945 0.956 0.977 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 177/200 4.28G 1.177 0.4622 0.88 972 256: 100% 64/64 [00:20<00:00, 3.13it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.89it/s] all 226 13227 0.946 0.954 0.977 0.624 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 178/200 4.5G 1.171 0.4618 0.8778 1346 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.81it/s] all 226 13227 0.952 0.948 0.978 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 179/200 4.35G 1.175 0.4613 0.8762 1283 256: 100% 64/64 [00:20<00:00, 3.13it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.89it/s] all 226 13227 0.952 0.95 0.978 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 180/200 4.39G 1.175 0.4634 0.8804 1659 256: 100% 64/64 [00:20<00:00, 3.14it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.91it/s] all 226 13227 0.948 0.955 0.978 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 181/200 4.38G 1.171 0.4624 0.8767 1024 256: 100% 64/64 [00:20<00:00, 3.08it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.87it/s] all 226 13227 0.95 0.95 0.978 0.628 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 182/200 4.43G 1.172 0.46 0.8774 1192 256: 100% 64/64 [00:20<00:00, 3.11it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.91it/s] all 226 13227 0.946 0.953 0.977 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 183/200 4.32G 1.165 0.4586 0.8729 1157 256: 100% 64/64 [00:21<00:00, 2.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.951 0.95 0.977 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 184/200 4.34G 1.157 0.4562 0.8747 1260 256: 100% 64/64 [00:21<00:00, 2.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.952 0.949 0.977 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 185/200 4.41G 1.165 0.4586 0.8766 1133 256: 100% 64/64 [00:21<00:00, 2.98it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.952 0.95 0.978 0.628 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 186/200 4.34G 1.146 0.456 0.8713 1732 256: 100% 64/64 [00:20<00:00, 3.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.27it/s] all 226 13227 0.948 0.953 0.978 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 187/200 4.37G 1.175 0.4593 0.8757 1345 256: 100% 64/64 [00:21<00:00, 2.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.946 0.957 0.977 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 188/200 4.27G 1.168 0.4572 0.8764 1154 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.30it/s] all 226 13227 0.946 0.957 0.977 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 189/200 4.32G 1.171 0.4563 0.8787 1172 256: 100% 64/64 [00:21<00:00, 3.04it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.07it/s] all 226 13227 0.944 0.957 0.976 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 190/200 4.45G 1.155 0.4514 0.8758 929 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.08it/s] all 226 13227 0.948 0.954 0.977 0.628 Closing dataloader mosaic albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 191/200 4.12G 1.178 0.4503 0.8904 559 256: 100% 64/64 [00:21<00:00, 2.92it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.946 0.955 0.976 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 192/200 4.14G 1.164 0.447 0.8848 761 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.26it/s] all 226 13227 0.948 0.954 0.976 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 193/200 4.13G 1.155 0.4442 0.8876 724 256: 100% 64/64 [00:20<00:00, 3.13it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s] all 226 13227 0.952 0.948 0.976 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 194/200 4.21G 1.156 0.4432 0.8877 858 256: 100% 64/64 [00:20<00:00, 3.11it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.92it/s] all 226 13227 0.948 0.953 0.976 0.625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 195/200 4.17G 1.154 0.4429 0.887 565 256: 100% 64/64 [00:20<00:00, 3.12it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.78it/s] all 226 13227 0.946 0.955 0.976 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 196/200 4.12G 1.155 0.4428 0.8876 732 256: 100% 64/64 [00:20<00:00, 3.14it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.86it/s] all 226 13227 0.948 0.953 0.976 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 197/200 4.16G 1.158 0.4429 0.885 766 256: 100% 64/64 [00:20<00:00, 3.10it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.04it/s] all 226 13227 0.946 0.954 0.977 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 198/200 4.13G 1.152 0.4421 0.8855 622 256: 100% 64/64 [00:21<00:00, 3.02it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.24it/s] all 226 13227 0.948 0.953 0.976 0.627 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 199/200 4.2G 1.14 0.4388 0.8809 792 256: 100% 64/64 [00:20<00:00, 3.13it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.31it/s] all 226 13227 0.948 0.955 0.976 0.626 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 200/200 4.1G 1.149 0.4405 0.8853 830 256: 100% 64/64 [00:20<00:00, 3.12it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.23it/s] all 226 13227 0.947 0.955 0.976 0.626 200 epochs completed in 1.719 hours. Optimizer stripped from runs/detect/train/weights/last.pt, 119.1MB Optimizer stripped from runs/detect/train/weights/best.pt, 119.1MB Validating runs/detect/train/weights/best.pt... Ultralytics 8.3.78 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB) YOLOv12x summary (fused): 283 layers, 59,044,499 parameters, 0 gradients, 198.5 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:07<00:00, 1.00it/s] all 226 13227 0.952 0.95 0.978 0.628 Speed: 0.1ms preprocess, 10.8ms inference, 0.0ms loss, 10.9ms postprocess per image Results saved to runs/detect/train 💡 Learn more at https://docs.ultralytics.com/modes/train
Analysis of results (Confusion matrix and metrics)
In [ ]:
from IPython.display import display, Image
Image(filename=f'{HOME}/runs/detect/train/confusion_matrix.png', width=600)
Out[ ]:
In [ ]:
Image(filename=f'{HOME}/runs/detect/train/results.png', width=600)
Out[ ]:
Applying the prediction to the validation images:
In [ ]:
!yolo task=detect mode=predict model=/content/yolov12/runs/detect/train/weights/best.pt conf=0.30 source= /content/tree_data/images/validation save=true save_txt=true
Ultralytics 8.3.78 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla T4, 15095MiB)
YOLOv12x summary (fused): 283 layers, 59,044,499 parameters, 0 gradients, 198.5 GFLOPs
image 1/226 /content/tree_data/images/validation/43_SA_04_04_jpg.rf.80c9eb91d06689d3f80b5d482d81d0b2.jpg: 256x256 58 Trees, 35.2ms
image 2/226 /content/tree_data/images/validation/43_SA_05_04_jpg.rf.bd5765d84c7467f6a65f34ae6e517959.jpg: 256x256 42 Trees, 29.8ms
image 3/226 /content/tree_data/images/validation/46_SA_02_01_jpg.rf.68170e84139dc504d8e44e1e10a3e0bc.jpg: 256x256 50 Trees, 29.7ms
image 4/226 /content/tree_data/images/validation/46_SA_04_04_png_jpg.rf.eac3bd74bdaf60220647c56a9c5d53c6.jpg: 256x256 88 Trees, 30.9ms
image 5/226 /content/tree_data/images/validation/46_SA_04_05_jpg.rf.2852620073f207f08575ac7748bbfdb2.jpg: 256x256 84 Trees, 27.3ms
image 6/226 /content/tree_data/images/validation/46_SA_04_08_jpg.rf.c8dd5506bacd5bd32c5ebd4fdb1c9290.jpg: 256x256 64 Trees, 27.2ms
image 7/226 /content/tree_data/images/validation/46_SA_05_06_png_jpg.rf.71280b8b0ef588c811cc4a1913640578.jpg: 256x256 97 Trees, 27.3ms
image 8/226 /content/tree_data/images/validation/a10_01_02_jpg.rf.5d520965be3e378b7711e042f20cf652.jpg: 256x256 42 Trees, 29.0ms
image 9/226 /content/tree_data/images/validation/a10_02_06_jpg.rf.c193a58f54f5d90cffe854694f917c5a.jpg: 256x256 103 Trees, 26.9ms
image 10/226 /content/tree_data/images/validation/a10_03_01_jpg.rf.f6f3544cabf850adc2690d35b928bded.jpg: 256x256 58 Trees, 27.2ms
image 11/226 /content/tree_data/images/validation/a10_03_06_jpg.rf.0fd7e515a1dcfe150177eb867e6e5ad1.jpg: 256x256 37 Trees, 28.7ms
image 12/226 /content/tree_data/images/validation/a10_05_01_jpg.rf.3dd2538c32488cc9f72dc0b8fb9e4287.jpg: 256x256 32 Trees, 27.0ms
image 13/226 /content/tree_data/images/validation/a13_01_03_jpg.rf.87c9e4f81c9a2f3630e862437220e569.jpg: 256x256 65 Trees, 26.4ms
image 14/226 /content/tree_data/images/validation/a13_04_02_jpg.rf.a0e6a72cf6399bab673b0690d11375e4.jpg: 256x256 42 Trees, 26.5ms
image 15/226 /content/tree_data/images/validation/a13_04_05_jpg.rf.7d80221e09514f770ad7c2be96cd44aa.jpg: 256x256 55 Trees, 27.2ms
image 16/226 /content/tree_data/images/validation/a13_05_01_jpg.rf.84567cffcc29bfe757776823823fa8fa.jpg: 256x256 70 Trees, 40.1ms
image 17/226 /content/tree_data/images/validation/a13_05_02_jpg.rf.972290a3a590c39ba06a40479342daae.jpg: 256x256 51 Trees, 28.0ms
image 18/226 /content/tree_data/images/validation/a13_05_04_jpg.rf.be1940fdee9b41fe341145d2e48bcd80.jpg: 256x256 57 Trees, 29.2ms
image 19/226 /content/tree_data/images/validation/a16_01_08_jpg.rf.47ec17a425e594ded2c089b544448f52.jpg: 256x256 64 Trees, 26.2ms
image 20/226 /content/tree_data/images/validation/a4_01_03_jpg.rf.2de768a218b21dd9368e4d969f30abd9.jpg: 256x256 72 Trees, 26.3ms
image 21/226 /content/tree_data/images/validation/a4_03_04_jpg.rf.b276f813d6d2145b4af1f878cd32a4b7.jpg: 256x256 76 Trees, 26.0ms
image 22/226 /content/tree_data/images/validation/a4_03_07_jpg.rf.7adf3847c26ca5c93d04999177ee38ca.jpg: 256x256 56 Trees, 26.3ms
image 23/226 /content/tree_data/images/validation/a5_02_01_jpg.rf.900ca050acaebee6c7a2a400cbddc70e.jpg: 256x256 70 Trees, 31.2ms
image 24/226 /content/tree_data/images/validation/a5_04_01_jpg.rf.5b577f1f4ef71bc76aab8480844b2b1f.jpg: 256x256 55 Trees, 27.0ms
image 25/226 /content/tree_data/images/validation/a5_04_09_jpg.rf.8e23d833ae100a4fc6e140e72d875745.jpg: 256x256 67 Trees, 26.3ms
image 26/226 /content/tree_data/images/validation/a5_05_05_jpg.rf.ca1dec90883c5fd9bfa3fb3fee39cae0.jpg: 256x256 83 Trees, 26.4ms
image 27/226 /content/tree_data/images/validation/b11_01_03_jpg.rf.925d254fb86f3b607d839b1ce52b97f1.jpg: 256x256 177 Trees, 26.5ms
image 28/226 /content/tree_data/images/validation/b11_02_05_png.rf.aec32cf0bf230373f3e284d5f7c3bec9.jpg: 256x256 165 Trees, 27.0ms
image 29/226 /content/tree_data/images/validation/b11_03_04_jpg.rf.07fc6713c3347a4da31448cc8969369a.jpg: 256x256 163 Trees, 33.3ms
image 30/226 /content/tree_data/images/validation/b11_04_04_png.rf.96b03c6d2a483aefa3e101b7563a4a6b.jpg: 256x256 173 Trees, 26.5ms
image 31/226 /content/tree_data/images/validation/b11_05_07_jpg.rf.56ed7c139ab248934f4bf3c9a3317ce5.jpg: 256x256 132 Trees, 28.4ms
image 32/226 /content/tree_data/images/validation/b12_01_03_jpg.rf.52c0fd5fede361a9e6c7045488777ab3.jpg: 256x256 167 Trees, 26.6ms
image 33/226 /content/tree_data/images/validation/b12_01_05_jpg.rf.a91ccf9c8e971ca818e49af26230c3d7.jpg: 256x256 155 Trees, 28.3ms
image 34/226 /content/tree_data/images/validation/b12_02_05_jpg.rf.e13e66db34cc96443c6880cbaa18cc60.jpg: 256x256 156 Trees, 27.1ms
image 35/226 /content/tree_data/images/validation/b12_04_03_png_jpg.rf.d75ef51cdb6e5270b2576dc11f8b1862.jpg: 256x256 158 Trees, 28.9ms
image 36/226 /content/tree_data/images/validation/b13_02_05_jpg.rf.a0f6e33a182d4bc6a83ef03081b26d8c.jpg: 256x256 171 Trees, 27.2ms
image 37/226 /content/tree_data/images/validation/b13_03_04_png_jpg.rf.56cc1f3c1d5d42e6e2b8e643f83ecd6e.jpg: 256x256 157 Trees, 36.8ms
image 38/226 /content/tree_data/images/validation/b13_04_08_jpg.rf.639aa14b7150f7c40b66d027a1664b46.jpg: 256x256 140 Trees, 44.9ms
image 39/226 /content/tree_data/images/validation/b13_05_05_jpg.rf.b7fd2646184e504d8b1af1003955fcb9.jpg: 256x256 115 Trees, 28.4ms
image 40/226 /content/tree_data/images/validation/b17_01_04_jpg.rf.114513dcb75f515f7e9546443777ae24.jpg: 256x256 155 Trees, 28.1ms
image 41/226 /content/tree_data/images/validation/b17_01_05_jpg.rf.4a5645b30d535a9960a4ac1d49af7783.jpg: 256x256 164 Trees, 28.3ms
image 42/226 /content/tree_data/images/validation/c14_01_06_jpg.rf.ee3132ec8633af70c0cbf77af4a81dcd.jpg: 256x256 57 Trees, 29.7ms
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image 165/226 /content/tree_data/images/validation/t8_04_05_jpg.rf.083dfb8ff0e5d5d374bc12e125f16c28.jpg: 256x256 51 Trees, 35.7ms
image 166/226 /content/tree_data/images/validation/t8_05_03_jpg.rf.1e086de52ba5bccf252a15e6b34eba76.jpg: 256x256 41 Trees, 26.5ms
image 167/226 /content/tree_data/images/validation/tu8_02_07_jpg.rf.8fcb611aeff60295490769217f1c69cb.jpg: 256x256 8 Trees, 27.6ms
image 168/226 /content/tree_data/images/validation/tu8_03_02_jpg.rf.40921c19ad3309ff5119939dac8ddf57.jpg: 256x256 15 Trees, 26.4ms
image 169/226 /content/tree_data/images/validation/y14_903_image_0_3_jpg.rf.bd93388aeb082a25ba1e1e23ceda6a7c.jpg: 256x256 47 Trees, 25.4ms
image 170/226 /content/tree_data/images/validation/y14_907_image_0_7_jpg.rf.81f46ebe0eeb8ece2595828abfbb4785.jpg: 256x256 63 Trees, 27.8ms
image 171/226 /content/tree_data/images/validation/y14_914_image_1_4_jpg.rf.fcb6f171dda2b1efafa703bf5f5239ab.jpg: 256x256 52 Trees, 26.0ms
image 172/226 /content/tree_data/images/validation/y14_937_image_3_7_jpg.rf.0f63c71f3146728d7ab0a7da7f803d0c.jpg: 256x256 42 Trees, 26.1ms
image 173/226 /content/tree_data/images/validation/y14_938_image_3_8_jpg.rf.1efdbd9c97d713e32cf3ba7f36010532.jpg: 256x256 12 Trees, 25.2ms
image 174/226 /content/tree_data/images/validation/y14_943_image_4_3_jpg.rf.9448babee4d61b22250cf640c5ef83b1.jpg: 256x256 55 Trees, 27.6ms
image 175/226 /content/tree_data/images/validation/y14_946_image_4_6_jpg.rf.75b7a2f110bb05e3294294385cb93942.jpg: 256x256 44 Trees, 25.6ms
image 176/226 /content/tree_data/images/validation/y14_947_image_4_7_jpg.rf.ee7897fce6f141bd9ee0ba76a363435b.jpg: 256x256 13 Trees, 25.6ms
image 177/226 /content/tree_data/images/validation/y19_1005_image_4_5_jpg.rf.d0043da141931b52ed6907c9b3a37957.jpg: 256x256 74 Trees, 25.2ms
image 178/226 /content/tree_data/images/validation/y19_968_image_0_8_jpg.rf.78eef8c939115409bb55699897917e20.jpg: 256x256 84 Trees, 25.1ms
image 179/226 /content/tree_data/images/validation/y19_976_image_1_6_jpg.rf.97d1ed8928fdbd7c0f20c3ac4ad97dee.jpg: 256x256 55 Trees, 27.8ms
image 180/226 /content/tree_data/images/validation/y19_987_image_2_7_jpg.rf.adbb56a19c905d08b2795fab7d635d89.jpg: 256x256 76 Trees, 28.0ms
image 181/226 /content/tree_data/images/validation/y19_999_image_3_9_jpg.rf.08541b77df33b002ea6d032e6938e981.jpg: 256x256 30 Trees, 26.3ms
image 182/226 /content/tree_data/images/validation/y1_1025_image_0_5_jpg.rf.48a5073575e12197476cd841437f26a3.jpg: 256x256 54 Trees, 29.5ms
image 183/226 /content/tree_data/images/validation/y1_1030_image_1_0_jpg.rf.ad1cb0818e203f048aadc29125215ae7.jpg: 256x256 75 Trees, 41.7ms
image 184/226 /content/tree_data/images/validation/y1_1043_image_2_3_jpg.rf.fd3b50f3e0f3740c2221804bda48b0f3.jpg: 256x256 53 Trees, 27.3ms
image 185/226 /content/tree_data/images/validation/y1_1048_image_2_8_jpg.rf.3b260ddee2d4cd3b54b4574b94f8a6c2.jpg: 256x256 64 Trees, 26.5ms
image 186/226 /content/tree_data/images/validation/y1_1049_image_2_9_jpg.rf.2df21e24d86aad6ec6e03dd66dad5ff4.jpg: 256x256 51 Trees, 29.3ms
image 187/226 /content/tree_data/images/validation/y1_1051_image_3_1_jpg.rf.954cf899d64c0f0b70c0930fdca6fd3a.jpg: 256x256 58 Trees, 25.6ms
image 188/226 /content/tree_data/images/validation/y1_1055_image_3_5_jpg.rf.c5db09e46f753192c3ce3779a3c96e40.jpg: 256x256 66 Trees, 26.9ms
image 189/226 /content/tree_data/images/validation/y1_1057_image_3_7_jpg.rf.2c954bef3d7c0ea64ec62e6f7199fb6a.jpg: 256x256 67 Trees, 28.6ms
image 190/226 /content/tree_data/images/validation/y1_1059_image_3_9_jpg.rf.fd3b48ec19d85b096704f3fa08cc1e64.jpg: 256x256 37 Trees, 25.6ms
image 191/226 /content/tree_data/images/validation/y1_1069_image_4_9_jpg.rf.92ea0444f15dcdeff227efac8e0df1ab.jpg: 256x256 55 Trees, 26.4ms
image 192/226 /content/tree_data/images/validation/y1_1072_image_5_2_jpg.rf.595c33b20b1f362fe54786fa5a71b943.jpg: 256x256 37 Trees, 25.1ms
image 193/226 /content/tree_data/images/validation/y22_1082_image_0_2_jpg.rf.9afeb10532f58129e8f83c8d153431d8.jpg: 256x256 62 Trees, 26.0ms
image 194/226 /content/tree_data/images/validation/y22_1091_image_1_1_jpg.rf.b2452781704103a9529f6d82000f85ef.jpg: 256x256 79 Trees, 25.5ms
image 195/226 /content/tree_data/images/validation/y22_1093_image_1_3_jpg.rf.9ba303ec29412aa41e5d56e833b85a03.jpg: 256x256 49 Trees, 28.4ms
image 196/226 /content/tree_data/images/validation/y22_1103_image_2_3_jpg.rf.7d3dd638e253b2e0a987c187b3a020c0.jpg: 256x256 44 Trees, 25.3ms
image 197/226 /content/tree_data/images/validation/y22_1113_image_3_3_jpg.rf.a74139363eb7835aaddf7178d93b1e8e.jpg: 256x256 53 Trees, 24.7ms
image 198/226 /content/tree_data/images/validation/y22_1116_image_3_6_jpg.rf.0a498a1f0362f7733673acaac57fd091.jpg: 256x256 66 Trees, 25.2ms
image 199/226 /content/tree_data/images/validation/y22_1117_image_3_7_jpg.rf.d24f5f66dd80bf26206f461bb732997c.jpg: 256x256 60 Trees, 27.5ms
image 200/226 /content/tree_data/images/validation/y22_1120_image_4_0_jpg.rf.126e7a7b96df8b05feceb0858d802ca8.jpg: 256x256 79 Trees, 29.7ms
image 201/226 /content/tree_data/images/validation/y22_1123_image_4_3_jpg.rf.46a679ae2cf991b5c7412e17f87197e6.jpg: 256x256 47 Trees, 25.9ms
image 202/226 /content/tree_data/images/validation/y22_1138_image_5_8_jpg.rf.57cb4e0b4bbac17bc84f06a82d5ab061.jpg: 256x256 20 Trees, 25.2ms
image 203/226 /content/tree_data/images/validation/y3_1148_image_0_8_jpg.rf.f2f1a55f29c4232e64724c92518c9353.jpg: 256x256 61 Trees, 24.8ms
image 204/226 /content/tree_data/images/validation/y3_1149_image_0_9_jpg.rf.f9a47366452fc8259fe1cc69d21b1fa0.jpg: 256x256 44 Trees, 25.4ms
image 205/226 /content/tree_data/images/validation/y3_1158_image_1_8_jpg.rf.1cf1e1ebdbb91369f9423cffe98573b3.jpg: 256x256 61 Trees, 26.1ms
image 206/226 /content/tree_data/images/validation/y3_1168_image_2_8_jpg.rf.c1dfde958f678e20f6471b12d9366705.jpg: 256x256 62 Trees, 26.6ms
image 207/226 /content/tree_data/images/validation/y3_1174_image_3_4_jpg.rf.d2148a81bf2615b17b45b531ebdf5fe4.jpg: 256x256 53 Trees, 26.1ms
image 208/226 /content/tree_data/images/validation/y3_1178_image_3_8_jpg.rf.ac61f7ff3336b629e76c48684eb01b8e.jpg: 256x256 59 Trees, 28.8ms
image 209/226 /content/tree_data/images/validation/y3_1188_image_4_8_jpg.rf.7a42c2b5e1d5cca856cb3adeeedc9152.jpg: 256x256 47 Trees, 26.1ms
image 210/226 /content/tree_data/images/validation/y7_1201_image_0_1_jpg.rf.5541e6bfed349d2d895fa649dc56e90e.jpg: 256x256 90 Trees, 26.1ms
image 211/226 /content/tree_data/images/validation/y7_1212_image_1_2_jpg.rf.a01bc68246917430f1c72c3e0ebb4643.jpg: 256x256 82 Trees, 28.2ms
image 212/226 /content/tree_data/images/validation/y7_1216_image_1_6_jpg.rf.e5eec40ad03a159dd8aa311b202ea608.jpg: 256x256 70 Trees, 25.5ms
image 213/226 /content/tree_data/images/validation/y7_1219_image_1_9_jpg.rf.66ee93346107d2f3964e2b2ffbec4ec4.jpg: 256x256 52 Trees, 25.4ms
image 214/226 /content/tree_data/images/validation/y7_1222_image_2_2_jpg.rf.c9e81850f0cfd72e51f8a2da0d8318c9.jpg: 256x256 58 Trees, 38.9ms
image 215/226 /content/tree_data/images/validation/y7_1231_image_3_1_jpg.rf.c19614ed89c9747470c740a21b10f1b8.jpg: 256x256 183 Trees, 28.6ms
image 216/226 /content/tree_data/images/validation/y7_1233_image_3_3_jpg.rf.03faa23eff0452a10f56c2881dd1a45d.jpg: 256x256 48 Trees, 26.4ms
image 217/226 /content/tree_data/images/validation/y7_1245_image_4_5_jpg.rf.1fd42a9e105c5d715120ec2e5346881a.jpg: 256x256 49 Trees, 25.3ms
image 218/226 /content/tree_data/images/validation/y7_1249_image_4_9_jpg.rf.adf0460ea6454d4fe643f3bc9939308d.jpg: 256x256 31 Trees, 25.0ms
image 219/226 /content/tree_data/images/validation/y8_1271_image_1_1_jpg.rf.fe8dafda5c1e5e1783d535c037cda932.jpg: 256x256 58 Trees, 24.8ms
image 220/226 /content/tree_data/images/validation/y8_1275_image_1_5_jpg.rf.6f6d85f918a40745fb49414312728347.jpg: 256x256 66 Trees, 25.6ms
image 221/226 /content/tree_data/images/validation/y8_1277_image_1_7_jpg.rf.5c4b4b064504b1cfcd7e0f27b8b0ef1f.jpg: 256x256 46 Trees, 27.1ms
image 222/226 /content/tree_data/images/validation/y8_1281_image_2_1_jpg.rf.b01a33c91834b4eca2e4646504c340eb.jpg: 256x256 55 Trees, 25.0ms
image 223/226 /content/tree_data/images/validation/y8_1305_image_4_5_jpg.rf.cf41bc9e84e02be207ba96eb0fb105fe.jpg: 256x256 40 Trees, 25.1ms
image 224/226 /content/tree_data/images/validation/y9_1371_image_5_1_jpg.rf.2cb17e9704752e1abed748b0f6ca56bc.jpg: 256x256 34 Trees, 25.3ms
image 225/226 /content/tree_data/images/validation/y9_1376_image_5_6_jpg.rf.1eddee2cb6da5b3ec0d1b8315799453f.jpg: 256x256 34 Trees, 25.8ms
image 226/226 /content/tree_data/images/validation/y9_1378_image_5_8_jpg.rf.78da21de8d8a58be3925567b83ffeef4.jpg: 256x256 35 Trees, 25.1ms
Speed: 0.5ms preprocess, 29.3ms inference, 3.0ms postprocess per image at shape (1, 3, 256, 256)
Results saved to runs/detect/predict
226 labels saved to runs/detect/predict/labels
💡 Learn more at https://docs.ultralytics.com/modes/predict
In [ ]:
for image_path in glob.glob(f'{HOME}/runs/detect/predict/*.jpg')[:8]:
display(Image(filename=image_path, height=400))
print("\n")
Generation of the dataframe with the resulting bounds from the predicted images:
In [ ]:
from skimage import io
df_full = pd.DataFrame([])
imgs_to_pred = [f for f in os.listdir('/content/yolov12/runs/detect/predict/labels/') if f.endswith('.txt')]
for images in imgs_to_pred:
filename = os.path.splitext(images)[0]
path = f'/content/yolov12/runs/detect/predict/labels/'+filename+'.txt'
cols = ['class', 'x-center', 'y-center', 'x_extend', 'y_extend']
df = pd.read_csv(path, sep=" ", header=None)
df.columns = cols
df['x-center'] = np.round(df['x-center'] * im_size)
df['y-center'] = np.round(df['y-center'] * im_size)
df['x_extend'] = np.round(df['x_extend'] * im_size)
df['y_extend'] = np.round(df['y_extend'] * im_size)
df['image_filename'] = filename
df_full = pd.concat([df_full, df])
In [ ]:
df_full
Out[ ]:
| class | x-center | y-center | x_extend | y_extend | image_filename | |
|---|---|---|---|---|---|---|
| 0 | 0 | 189.0 | 114.0 | 10.0 | 11.0 | st2_727_image_0_7_jpg.rf.ffec547e479bf00bf8c94... |
| 1 | 0 | 146.0 | 131.0 | 12.0 | 12.0 | st2_727_image_0_7_jpg.rf.ffec547e479bf00bf8c94... |
| 2 | 0 | 242.0 | 11.0 | 12.0 | 12.0 | st2_727_image_0_7_jpg.rf.ffec547e479bf00bf8c94... |
| 3 | 0 | 178.0 | 166.0 | 10.0 | 10.0 | st2_727_image_0_7_jpg.rf.ffec547e479bf00bf8c94... |
| 4 | 0 | 250.0 | 106.0 | 11.0 | 10.0 | st2_727_image_0_7_jpg.rf.ffec547e479bf00bf8c94... |
| ... | ... | ... | ... | ... | ... | ... |
| 17 | 0 | 37.0 | 195.0 | 17.0 | 17.0 | sa24_406_image_4_6_jpg.rf.1eef76fafc641fce23a4... |
| 18 | 0 | 56.0 | 24.0 | 16.0 | 16.0 | sa24_406_image_4_6_jpg.rf.1eef76fafc641fce23a4... |
| 19 | 0 | 55.0 | 175.0 | 14.0 | 14.0 | sa24_406_image_4_6_jpg.rf.1eef76fafc641fce23a4... |
| 20 | 0 | 37.0 | 6.0 | 11.0 | 12.0 | sa24_406_image_4_6_jpg.rf.1eef76fafc641fce23a4... |
| 21 | 0 | 38.0 | 158.0 | 12.0 | 12.0 | sa24_406_image_4_6_jpg.rf.1eef76fafc641fce23a4... |
13909 rows × 6 columns
In [ ]:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
import os
def plot_image_with_predictions(df, image_folder, num_images=10):
image_files = df['image_filename'].unique()[:num_images] # Select unique image filenames
for image_file in image_files:
try:
image_path = os.path.join(image_folder, image_file + ".jpg") # Adjust the file extension if needed
img = Image.open(image_path)
fig, ax = plt.subplots(1)
ax.imshow(img)
# Filter the dataframe for the current image
image_df = df[df['image_filename'] == image_file]
for _, row in image_df.iterrows():
x_center = row['x-center']
y_center = row['y-center']
width = row['x_extend']
height = row['y_extend']
xmin = int(x_center - width / 2)
ymin = int(y_center - height / 2)
xmax = int(x_center + width / 2)
ymax = int(y_center + height / 2)
width = xmax - xmin
height = ymax - ymin
rect = patches.Rectangle((xmin, ymin), width, height, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
plt.show()
except FileNotFoundError:
print(f"Error: Image file not found at {image_path}")
except Exception as e:
print(f"An error occurred: {e}")
image_folder = "/content/tree_data/images/validation" # Replace with the actual folder path
plot_image_with_predictions(df_full, image_folder)
In [ ]: