you only look once (YOLO) [🚧 WIP]#
Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO11. the latest version of the acclaimed real-time object detection and image segmentation model YOLO11 models pretrained on the COCO dataset for Detection, Segmentation, and Pose Estimation. Additionally, Classification models pretrained on the ImageNet dataset are available. Tracking mode is compatible with all Detection, Segmentation, and Pose models. All Models are automatically downloaded from the latest Ultralytics release upon first use.
import cv2
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display, Image
import torch
import random
# Set random seeds for reproducibility
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
<torch._C.Generator at 0x138350bf0>
# Initialize detection model
from ultralytics import YOLO
yolo_model = YOLO("yolov8n.pt")
!yolo task=detect mode=predict model=yolov8n.pt conf=0.25 source='https://ultralytics.com/images/bus.jpg' save=True
Ultralytics 8.3.4 🚀 Python-3.12.4 torch-2.4.1 CPU (Apple M3 Max)
YOLOv8n summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs
Downloading https://ultralytics.com/images/bus.jpg to 'bus.jpg'...
100%|█████████████████████████████████████████| 134k/134k [00:00<00:00, 215MB/s]
image 1/1 /Users/n0man/Code/n03an.me/notebooks/va/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 29.8ms
Speed: 1.6ms preprocess, 29.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)
Results saved to /Users/n0man/Code/n03an.me/runs/detect/predict7
💡 Learn more at https://docs.ultralytics.com/modes/predict
set up the device (mps for macOS, cuda for NVIDIA GPUs, or cpu for CPU-only inference)
device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"
# Run detection
def detect_objects(image_path):
results = yolo_model(image_path)
# Visualize results
plt.figure(figsize=(12, 8))
plt.imshow(results[0].plot()[:, :, ::-1]) # Convert BGR to RGB
plt.title("Object Detection")
plt.axis('off')
plt.show()
# Run detection
detect_objects("images/bus.jpg")
image 1/1 /Users/n0man/Code/n03an.me/notebooks/va/images/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 27.2ms
Speed: 1.5ms preprocess, 27.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 480)
Detecting Events#
Will try somethin rubbish - the function detect_event takes the results from the YOLO model and checks for the presence of specific classes (like “person”) to determine interesting event. If there are more than 3 people, it flags it as “fighting”, and if there are 5 or more, it flags it as a “crowd” situation 🤦🏻♂️🤦🏻♂️🤦🏻♂️ as rubbish as it could get but you get the point…
FIRE_CLASSES = ["fire", "smoke"]
CROWD_THRESHOLD = 5
def detect_event(yolo_results):
people_count = 0
fire_detected = False
for det in yolo_results.boxes:
cls = int(det.cls[0])
label = yolo_model.names[cls]
if label == "person":
people_count += 1
elif label in FIRE_CLASSES:
fire_detected = True
fighting_detected = people_count >= 3 and not fire_detected
overcrowded = people_count >= CROWD_THRESHOLD
return fire_detected, fighting_detected, overcrowded, people_count
we will use lightweight BLIP model Salesforce/blip-image-captioning-base (possibly the one that could run on Jetson edge devies like Jetson Xavier NX??) to generate image captions.
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image as PILImage
blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
blip_model = BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-base",
device_map="auto",
trust_remote_code=True,
use_safetensors=True # <--- this avoids torch.load
)
def generate_caption(frame):
pil_image = PILImage.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
inputs = blip_processor(images=pil_image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
output = blip_model.generate(**inputs, max_new_tokens=50)
caption = blip_processor.batch_decode(output, skip_special_tokens=True)[0]
return caption
/Users/n0man/Code/n03an.me/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Detecting Events and Captions…#
image_path = "images/bus.jpg"
frame = cv2.imread(image_path)
detect_objects(image_path)
results = yolo_model(frame)[0]
fire, fight, crowd, count = detect_event(results)
if fire or fight or crowd:
print("\n[EVENT DETECTED]")
if fire: print("🔥 Fire detected")
if fight: print("🔴 Possible fighting detected")
if crowd: print(f"👯👯👯👯 Overcrowding detected ({count} people)")
caption = generate_caption(frame)
print(f"Captions : {caption}")
else:
print("✅ No major event detected in the image.")
image 1/1 /Users/n0man/Code/n03an.me/notebooks/va/images/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 34.6ms
Speed: 2.4ms preprocess, 34.6ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 480)
0: 640x480 4 persons, 1 bus, 1 stop sign, 21.7ms
Speed: 1.1ms preprocess, 21.7ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 480)
[EVENT DETECTED]
🔴 Possible fighting detected
Captions : a group of people walking around a bus
image_path = "images/park.jpg"
frame = cv2.imread(image_path)
detect_objects(image_path)
results = yolo_model(frame)[0]
fire, fight, crowd, count = detect_event(results)
if fire or fight or crowd:
print("\n[EVENT DETECTED]")
if fire: print("🔥 Fire detected")
if fight: print("🔴 Possible fighting detected")
if crowd: print(f"👯👯👯👯 Overcrowding detected ({count} people)")
caption = generate_caption(frame)
print(f"Captions : {caption}")
else:
print("✅ No major event detected in the image.")
image 1/1 /Users/n0man/Code/n03an.me/notebooks/va/images/park.jpg: 384x640 8 persons, 1 bicycle, 1 dog, 26.7ms
Speed: 1.5ms preprocess, 26.7ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)
0: 384x640 8 persons, 1 bicycle, 1 dog, 17.2ms
Speed: 1.3ms preprocess, 17.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)
[EVENT DETECTED]
🔴 Possible fighting detected
👯👯👯👯 Overcrowding detected (8 people)
Captions : a group of people walking along a river
Extracting Attributes and meta information using RT-DETR#
from ultralytics import RTDETR
# Load a COCO-pretrained RT-DETR-l model
model = RTDETR("rtdetr-l.pt")
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/rtdetr-l.pt to 'rtdetr-l.pt'...
100%|██████████| 63.4M/63.4M [00:02<00:00, 29.4MB/s]
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the RT-DETR-l model on the 'bus.jpg' image
results = model("images/nemo.png")
rt-detr-l summary: 494 layers, 32,148,140 parameters, 0 gradients, 103.8 GFLOPs
New https://pypi.org/project/ultralytics/8.3.176 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.4 🚀 Python-3.12.4 torch-2.4.1 CPU (Apple M3 Max)
engine/trainer: task=detect, mode=train, model=rtdetr-l.pt, data=coco8.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=640, 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=4, 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, label_smoothing=0.0, 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=/Users/n0man/Code/n03an.me/runs/detect/train
Dataset 'coco8.yaml' images not found ⚠️, missing path '/Users/n0man/Code/datasets/coco8/images/val'
Downloading https://ultralytics.com/assets/coco8.zip to '/Users/n0man/Code/datasets/coco8.zip'...
100%|██████████| 433k/433k [00:00<00:00, 11.8MB/s]
Unzipping /Users/n0man/Code/datasets/coco8.zip to /Users/n0man/Code/datasets/coco8...: 100%|██████████| 25/25 [00:00<00:00, 4312.64file/s]
Dataset download success ✅ (0.9s), saved to /Users/n0man/Code/datasets
WARNING ⚠️ no model scale passed. Assuming scale='l'.
from n params module arguments
0 -1 1 25248 ultralytics.nn.modules.block.HGStem [3, 32, 48]
1 -1 6 155072 ultralytics.nn.modules.block.HGBlock [48, 48, 128, 3, 6]
2 -1 1 1408 ultralytics.nn.modules.conv.DWConv [128, 128, 3, 2, 1, False]
3 -1 6 839296 ultralytics.nn.modules.block.HGBlock [128, 96, 512, 3, 6]
4 -1 1 5632 ultralytics.nn.modules.conv.DWConv [512, 512, 3, 2, 1, False]
5 -1 6 1695360 ultralytics.nn.modules.block.HGBlock [512, 192, 1024, 5, 6, True, False]
6 -1 6 2055808 ultralytics.nn.modules.block.HGBlock [1024, 192, 1024, 5, 6, True, True]
7 -1 6 2055808 ultralytics.nn.modules.block.HGBlock [1024, 192, 1024, 5, 6, True, True]
8 -1 1 11264 ultralytics.nn.modules.conv.DWConv [1024, 1024, 3, 2, 1, False]
9 -1 6 6708480 ultralytics.nn.modules.block.HGBlock [1024, 384, 2048, 5, 6, True, False]
10 -1 1 524800 ultralytics.nn.modules.conv.Conv [2048, 256, 1, 1, None, 1, 1, False]
11 -1 1 789760 ultralytics.nn.modules.transformer.AIFI [256, 1024, 8]
12 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 7 1 262656 ultralytics.nn.modules.conv.Conv [1024, 256, 1, 1, None, 1, 1, False]
15 [-2, -1] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3]
17 -1 1 66048 ultralytics.nn.modules.conv.Conv [256, 256, 1, 1]
18 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
19 3 1 131584 ultralytics.nn.modules.conv.Conv [512, 256, 1, 1, None, 1, 1, False]
20 [-2, -1] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3]
22 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2]
23 [-1, 17] 1 0 ultralytics.nn.modules.conv.Concat [1]
24 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3]
25 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2]
26 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
27 -1 3 2232320 ultralytics.nn.modules.block.RepC3 [512, 256, 3]
28 [21, 24, 27] 1 7466252 ultralytics.nn.modules.head.RTDETRDecoder [80, [256, 256, 256]]
rt-detr-l summary: 681 layers, 32,970,476 parameters, 32,970,476 gradients, 108.3 GFLOPs
Transferred 322/941 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir /Users/n0man/Code/n03an.me/runs/detect/train', view at http://localhost:6006/
train: Scanning /Users/n0man/Code/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4 [00:00<00:00, 2172.65it/s]
train: New cache created: /Users/n0man/Code/datasets/coco8/labels/train.cache
val: Scanning /Users/n0man/Code/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4 [00:00<00:00, 3964.37it/s]
val: New cache created: /Users/n0man/Code/datasets/coco8/labels/val.cache
Plotting labels to /Users/n0man/Code/n03an.me/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=0.000119, momentum=0.9) with parameter groups 143 weight(decay=0.0), 206 weight(decay=0.0005), 226 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to /Users/n0man/Code/n03an.me/runs/detect/train
Starting training for 100 epochs...
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
1/100 0G 1.284 1.097 1.412 21 640: 100%|██████████| 1/1 [00:04<00:00, 4.54s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
2/100 0G 1.377 1.018 1.074 23 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
3/100 0G 1.068 1.469 1.542 15 640: 100%|██████████| 1/1 [00:04<00:00, 4.28s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.15s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
4/100 0G 1.183 1.485 0.9249 30 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
5/100 0G 1.343 1.214 1.444 20 640: 100%|██████████| 1/1 [00:04<00:00, 4.01s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
6/100 0G 1.153 1.343 1.387 27 640: 100%|██████████| 1/1 [00:04<00:00, 4.29s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.30s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
7/100 0G 1.182 1.369 0.9188 23 640: 100%|██████████| 1/1 [00:04<00:00, 4.75s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.28s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
8/100 0G 1.073 1.679 1.09 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.49s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
9/100 0G 1.264 1.106 0.9364 34 640: 100%|██████████| 1/1 [00:04<00:00, 4.29s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.25s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
10/100 0G 1.213 1.173 1.464 22 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.27s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
11/100 0G 1.193 1.152 1.014 28 640: 100%|██████████| 1/1 [00:04<00:00, 4.11s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
12/100 0G 1.309 1.268 1.08 18 640: 100%|██████████| 1/1 [00:04<00:00, 4.29s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
13/100 0G 1.185 1.02 0.898 39 640: 100%|██████████| 1/1 [00:04<00:00, 4.31s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
14/100 0G 0.8898 2.022 1.57 10 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
15/100 0G 1.121 1.256 0.7394 32 640: 100%|██████████| 1/1 [00:04<00:00, 4.02s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.18s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
16/100 0G 1.156 1.312 1.134 25 640: 100%|██████████| 1/1 [00:04<00:00, 4.31s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
17/100 0G 1.286 0.9381 1.079 26 640: 100%|██████████| 1/1 [00:04<00:00, 4.02s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.23s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
18/100 0G 1.222 0.9205 0.9124 31 640: 100%|██████████| 1/1 [00:04<00:00, 4.07s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
19/100 0G 1.302 0.926 0.8966 40 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
20/100 0G 1.18 0.9929 1.1 16 640: 100%|██████████| 1/1 [00:04<00:00, 4.59s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.14s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
21/100 0G 1.23 1.23 1.398 15 640: 100%|██████████| 1/1 [00:04<00:00, 4.51s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
22/100 0G 0.9727 1.34 1.159 23 640: 100%|██████████| 1/1 [00:04<00:00, 4.46s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.18s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
23/100 0G 1.068 1.191 1.192 23 640: 100%|██████████| 1/1 [00:04<00:00, 4.04s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.14s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
24/100 0G 1.191 1.102 1.766 15 640: 100%|██████████| 1/1 [00:04<00:00, 4.52s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.18s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
25/100 0G 1.196 1.091 1.231 25 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
26/100 0G 1.224 0.7853 1.279 27 640: 100%|██████████| 1/1 [00:03<00:00, 3.96s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
27/100 0G 1.149 1.153 1.294 21 640: 100%|██████████| 1/1 [00:03<00:00, 4.00s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.18s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
28/100 0G 1.225 0.8267 1.087 33 640: 100%|██████████| 1/1 [00:03<00:00, 3.93s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
29/100 0G 0.951 1.319 0.8126 31 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
30/100 0G 1.16 1.034 1.211 25 640: 100%|██████████| 1/1 [00:03<00:00, 3.96s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.23s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
31/100 0G 1.103 1.012 0.7676 42 640: 100%|██████████| 1/1 [00:04<00:00, 4.43s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.28s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
32/100 0G 1.227 0.9841 1.14 20 640: 100%|██████████| 1/1 [00:04<00:00, 4.39s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.29s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
33/100 0G 0.7112 2.249 1.21 9 640: 100%|██████████| 1/1 [00:04<00:00, 4.48s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
34/100 0G 1.075 1.015 0.9163 30 640: 100%|██████████| 1/1 [00:04<00:00, 4.17s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
35/100 0G 1.088 1.078 1.118 30 640: 100%|██████████| 1/1 [00:04<00:00, 4.30s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
36/100 0G 1.044 1.221 0.7632 26 640: 100%|██████████| 1/1 [00:04<00:00, 4.53s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.18s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
37/100 0G 0.9202 1.333 0.8312 20 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.27s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
38/100 0G 0.9601 1.411 0.7987 16 640: 100%|██████████| 1/1 [00:04<00:00, 4.66s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.27s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
39/100 0G 1.083 0.8606 1.248 18 640: 100%|██████████| 1/1 [00:04<00:00, 4.66s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.23s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
40/100 0G 1.077 0.9241 0.8151 25 640: 100%|██████████| 1/1 [00:04<00:00, 4.58s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0 0 0 0
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
41/100 0G 0.9988 1.316 0.9092 23 640: 100%|██████████| 1/1 [00:04<00:00, 4.80s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00538 0.0167 0.0183 0.00415
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
42/100 0G 1.222 1.02 1.441 11 640: 100%|██████████| 1/1 [00:04<00:00, 4.28s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.33s/it]
all 4 17 0.00538 0.0167 0.0183 0.00415
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
43/100 0G 1.072 1.31 1.099 26 640: 100%|██████████| 1/1 [00:04<00:00, 4.30s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.18s/it]
all 4 17 0.00314 0.0167 0.0173 0.00224
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
44/100 0G 0.939 1.238 0.8986 19 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0.00314 0.0167 0.0173 0.00224
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
45/100 0G 1.072 0.8635 0.8733 28 640: 100%|██████████| 1/1 [00:03<00:00, 3.95s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0.00314 0.0167 0.00671 0.00209
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
46/100 0G 0.9321 1.032 0.9111 31 640: 100%|██████████| 1/1 [00:03<00:00, 3.91s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00314 0.0167 0.00671 0.00209
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
47/100 0G 1.001 1.299 1.196 20 640: 100%|██████████| 1/1 [00:04<00:00, 4.29s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00476 0.0333 0.00552 0.00116
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
48/100 0G 1.062 0.8952 0.9182 33 640: 100%|██████████| 1/1 [00:04<00:00, 4.09s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0.00476 0.0333 0.00552 0.00116
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
49/100 0G 0.971 1.328 1.072 14 640: 100%|██████████| 1/1 [00:03<00:00, 3.94s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.25s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
50/100 0G 0.8615 1.082 0.9032 21 640: 100%|██████████| 1/1 [00:04<00:00, 4.07s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
51/100 0G 0.9852 0.8619 0.7137 32 640: 100%|██████████| 1/1 [00:04<00:00, 4.07s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
52/100 0G 0.8192 1.181 0.8867 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
53/100 0G 0.94 1.22 0.8548 18 640: 100%|██████████| 1/1 [00:04<00:00, 4.39s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
54/100 0G 1.116 0.7954 1.01 29 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
55/100 0G 1.106 1.049 0.9257 25 640: 100%|██████████| 1/1 [00:04<00:00, 4.04s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
56/100 0G 1.166 1.335 1.236 12 640: 100%|██████████| 1/1 [00:04<00:00, 4.03s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.15s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
57/100 0G 0.9316 1.028 1.051 20 640: 100%|██████████| 1/1 [00:04<00:00, 4.05s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
58/100 0G 1.063 0.8656 1.032 43 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
59/100 0G 1.066 0.8185 1.023 28 640: 100%|██████████| 1/1 [00:04<00:00, 4.20s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
60/100 0G 0.9158 1.158 1.165 15 640: 100%|██████████| 1/1 [00:04<00:00, 4.16s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
61/100 0G 0.8508 0.9712 0.6642 35 640: 100%|██████████| 1/1 [00:04<00:00, 4.12s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
62/100 0G 1.112 0.646 0.7845 49 640: 100%|██████████| 1/1 [00:04<00:00, 4.54s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
63/100 0G 1.07 0.9826 0.9378 26 640: 100%|██████████| 1/1 [00:04<00:00, 4.53s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.27s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
64/100 0G 0.9423 0.9209 0.8245 29 640: 100%|██████████| 1/1 [00:04<00:00, 4.10s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
65/100 0G 0.777 1.024 0.9407 18 640: 100%|██████████| 1/1 [00:04<00:00, 4.04s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]
all 4 17 0.00187 0.0167 0.00261 0.000261
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
66/100 0G 1.116 1.017 1.049 17 640: 100%|██████████| 1/1 [00:04<00:00, 4.01s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.0026 0.0167 0.00155 0.000155
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
67/100 0G 1.104 0.7374 0.7988 57 640: 100%|██████████| 1/1 [00:04<00:00, 4.00s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.0026 0.0167 0.00155 0.000155
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
68/100 0G 1.091 1.148 1.344 29 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.0026 0.0167 0.00155 0.000155
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
69/100 0G 1.04 0.9471 1.06 28 640: 100%|██████████| 1/1 [00:04<00:00, 4.54s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00253 0.0167 0.00154 0.000307
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
70/100 0G 0.9421 1.111 0.9506 18 640: 100%|██████████| 1/1 [00:04<00:00, 4.21s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00253 0.0167 0.00154 0.000307
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
71/100 0G 0.8869 1.072 0.7967 30 640: 100%|██████████| 1/1 [00:04<00:00, 4.19s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00253 0.0167 0.00154 0.000307
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
72/100 0G 1.016 0.8447 0.9521 31 640: 100%|██████████| 1/1 [00:04<00:00, 4.68s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
73/100 0G 0.959 0.7464 0.7875 38 640: 100%|██████████| 1/1 [00:04<00:00, 4.26s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
74/100 0G 0.8573 1.223 1.094 11 640: 100%|██████████| 1/1 [00:04<00:00, 4.20s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
75/100 0G 0.8538 1.001 0.7996 20 640: 100%|██████████| 1/1 [00:04<00:00, 4.05s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
76/100 0G 1.025 0.9374 0.9919 25 640: 100%|██████████| 1/1 [00:04<00:00, 4.04s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
77/100 0G 0.8009 0.9451 0.7806 26 640: 100%|██████████| 1/1 [00:03<00:00, 3.99s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
78/100 0G 1.073 0.9099 0.9775 42 640: 100%|██████████| 1/1 [00:04<00:00, 4.13s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.28s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
79/100 0G 0.6537 1.406 0.8425 14 640: 100%|██████████| 1/1 [00:04<00:00, 4.11s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
80/100 0G 0.8014 1.218 0.746 19 640: 100%|██████████| 1/1 [00:04<00:00, 4.11s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00249 0.0167 0.00154 0.000154
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
81/100 0G 1.006 0.8019 0.7255 35 640: 100%|██████████| 1/1 [00:04<00:00, 4.20s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
82/100 0G 0.9943 0.7726 0.7238 32 640: 100%|██████████| 1/1 [00:04<00:00, 4.77s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.25s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
83/100 0G 0.9247 1.012 0.7308 42 640: 100%|██████████| 1/1 [00:04<00:00, 4.90s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
84/100 0G 0.9576 0.8829 0.7759 24 640: 100%|██████████| 1/1 [00:04<00:00, 4.76s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.19s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
85/100 0G 0.8626 1.012 0.8213 18 640: 100%|██████████| 1/1 [00:04<00:00, 4.44s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
86/100 0G 0.7646 1.275 0.7179 16 640: 100%|██████████| 1/1 [00:04<00:00, 4.14s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.21s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
87/100 0G 0.9585 0.8869 0.815 23 640: 100%|██████████| 1/1 [00:04<00:00, 4.10s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
88/100 0G 0.9281 0.862 0.8755 17 640: 100%|██████████| 1/1 [00:04<00:00, 4.07s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
89/100 0G 0.9991 0.9142 1.257 16 640: 100%|██████████| 1/1 [00:04<00:00, 4.13s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
90/100 0G 0.9732 0.7642 0.8063 36 640: 100%|██████████| 1/1 [00:04<00:00, 4.36s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.29s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Closing dataloader mosaic
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
91/100 0G 0.6306 1.191 0.6511 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.23s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.31s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
92/100 0G 0.7359 0.9343 0.4631 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.48s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.38s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
93/100 0G 0.7088 1.273 0.8519 12 640: 100%|██████████| 1/1 [00:04<00:00, 4.27s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
94/100 0G 0.9116 1.07 0.561 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.23s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
95/100 0G 0.7572 1.154 0.795 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.08s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
96/100 0G 0.8463 0.8795 0.7818 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.26s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.31s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
97/100 0G 1.004 0.7446 1.227 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.14s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
98/100 0G 0.6773 1.103 0.4964 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.20s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
99/100 0G 0.7963 1.083 0.6276 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.13s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
Epoch GPU_mem giou_loss cls_loss l1_loss Instances Size
100/100 0G 0.6497 1.111 0.6663 13 640: 100%|██████████| 1/1 [00:04<00:00, 4.37s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.27s/it]
all 4 17 0.00397 0.0167 0.00235 0.000235
100 epochs completed in 0.165 hours.
Optimizer stripped from /Users/n0man/Code/n03an.me/runs/detect/train/weights/last.pt, 66.5MB
Optimizer stripped from /Users/n0man/Code/n03an.me/runs/detect/train/weights/best.pt, 66.5MB
Validating /Users/n0man/Code/n03an.me/runs/detect/train/weights/best.pt...
Ultralytics 8.3.4 🚀 Python-3.12.4 torch-2.4.1 CPU (Apple M3 Max)
rt-detr-l summary: 502 layers, 32,148,140 parameters, 0 gradients, 103.8 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.25s/it]
all 4 17 0.00538 0.0167 0.0183 0.0039
person 3 10 0.0323 0.1 0.11 0.0234
dog 1 1 0 0 0 0
horse 1 2 0 0 0 0
elephant 1 2 0 0 0 0
umbrella 1 1 0 0 0 0
potted plant 1 1 0 0 0 0
Speed: 0.3ms preprocess, 309.9ms inference, 0.0ms loss, 0.1ms postprocess per image
Results saved to /Users/n0man/Code/n03an.me/runs/detect/train
image 1/1 /Users/n0man/Code/n03an.me/notebooks/va/images/nemo.png: 640x640 4 persons, 1 bicycle, 269.2ms
Speed: 1.2ms preprocess, 269.2ms inference, 0.1ms postprocess per image at shape (1, 3, 640, 640)
results
[ultralytics.engine.results.Results object with attributes:
boxes: ultralytics.engine.results.Boxes object
keypoints: None
masks: None
names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
obb: None
orig_img: array([[[250, 250, 250],
[250, 250, 250],
[250, 250, 250],
...,
[250, 250, 250],
[250, 250, 250],
[253, 253, 253]],
[[250, 250, 250],
[250, 250, 250],
[250, 250, 250],
...,
[250, 250, 250],
[250, 250, 250],
[253, 253, 253]],
[[250, 250, 250],
[250, 250, 250],
[250, 250, 250],
...,
[250, 250, 250],
[250, 250, 250],
[253, 253, 253]],
...,
[[250, 250, 250],
[250, 250, 250],
[250, 250, 250],
...,
[250, 250, 250],
[250, 250, 250],
[253, 253, 253]],
[[250, 250, 250],
[250, 250, 250],
[250, 250, 250],
...,
[250, 250, 250],
[250, 250, 250],
[253, 253, 253]],
[[253, 253, 253],
[253, 253, 253],
[253, 253, 253],
...,
[253, 253, 253],
[253, 253, 253],
[254, 254, 254]]], dtype=uint8)
orig_shape: (620, 512)
path: '/Users/n0man/Code/n03an.me/notebooks/va/images/nemo.png'
probs: None
save_dir: '/Users/n0man/Code/n03an.me/runs/detect/train2'
speed: {'preprocess': 1.2328624725341797, 'inference': 269.17195320129395, 'postprocess': 0.1399517059326172}]
def generate_caption(frame):
pil_image = PILImage.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
inputs = blip_processor(images=pil_image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
output = blip_model.generate(**inputs, max_new_tokens=50)
caption = blip_processor.batch_decode(output, skip_special_tokens=True)[0]
return caption
def extract_color_and_attributes(caption, cls_name):
colors = ["red", "black", "white", "yellow", "blue", "green", "orange", "pink", "gray", "brown"]
caption_lower = caption.lower()
color = "unknown"
for c in colors:
# check if color near class in caption text
if c in caption_lower and cls_name.lower() in caption_lower:
idx_color = caption_lower.find(c)
idx_cls = caption_lower.find(cls_name.lower())
if abs(idx_color - idx_cls) < 20:
color = c
break
# Example: detect attribute "white cap" for person
attributes = []
if cls_name == "person":
if "white cap" in caption_lower or "white hat" in caption_lower or "white cap" in caption_lower:
attributes.append("white cap")
elif cls_name == "bicycle":
if "folding" in caption_lower:
attributes.append("folding")
if "small wheels" in caption_lower:
attributes.append("small wheels")
return color, attributes
def detect_and_caption_objects(frame, yolo_model):
results = yolo_model(frame)[0] # assuming yolomodel returns detections in results.boxes
img_h, img_w = frame.shape[:2]
detected_objects = []
for box in results.boxes:
cls_id = int(box.cls[0])
cls_name = results.names[cls_id]
x1, y1, x2, y2 = map(int, box.xyxy[0])
crop = frame[max(0,y1):min(img_h,y2), max(0,x1):min(img_w,x2)]
caption = generate_caption(crop)
color, attributes = extract_color_and_attributes(caption, cls_name)
# Build object dict
obj = {
"type": cls_name,
"color": color,
"attributes": attributes,
"caption": caption
}
detected_objects.append(obj)
return detected_objects
image_path = "images/nemo.png"
frame = cv2.imread(image_path)
detect_objects(image_path)
objects_with_attributes = detect_and_caption_objects(frame, yolo_model)
for obj in objects_with_attributes:
print(f"Object: {obj['type']}, Color: {obj['color']}, Attributes: {obj['attributes']}")
print(f"Caption: {obj['caption']}\n")
image 1/1 /Users/n0man/Code/n03an.me/notebooks/va/images/nemo.png: 640x544 1 person, 1 bicycle, 27.2ms
Speed: 1.7ms preprocess, 27.2ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 544)
0: 640x544 1 person, 1 bicycle, 33.0ms
Speed: 1.1ms preprocess, 33.0ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 544)
Object: bicycle, Color: unknown, Attributes: []
Caption: a man standing next to a yellow bike
Object: person, Color: unknown, Attributes: []
Caption: a man riding a bike on a sidewalk