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ottoykh
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Smart-Traffic

Image Segmentation
Transformers
PyTorch
TensorBoard
ONNX
English
yolov8-seg
Inference Endpoints
Model card Files Metrics Community
1
ottoykh commited on
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  1. result/BoxF1_curve.png +0 -0
  2. result/BoxPR_curve.png +0 -0
  3. result/BoxP_curve.png +0 -0
  4. result/BoxR_curve.png +0 -0
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  6. result/MaskPR_curve.png +0 -0
  7. result/MaskP_curve.png +0 -0
  8. result/MaskR_curve.png +0 -0
  9. result/args.yaml +98 -0
  10. result/confusion_matrix.png +0 -0
  11. result/confusion_matrix_normalized.png +0 -0
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  22. result/train_batch10621.jpg +0 -0
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  32. trafficImage/__init__.py +0 -0
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+ task: segment
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+ mode: train
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+ model: yolov8s-seg.pt
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+ data: /content/Road-Traffic-4/data.yaml
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+ epochs: 100
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+ patience: 50
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+ save_dir: runs/segment/train
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+ {"names": ["Bus", "Government-car", "Minibus", "Motorbike", "Private-car", "Taxi", "Truck"], "yaml": {"nc": 7, "depth_multiple": 0.33, "width_multiple": 0.5, "backbone": [[-1, 1, "Conv", [64, 3, 2]], [-1, 1, "Conv", [128, 3, 2]], [-1, 3, "C2f", [128, true]], [-1, 1, "Conv", [256, 3, 2]], [-1, 6, "C2f", [256, true]], [-1, 1, "Conv", [512, 3, 2]], [-1, 6, "C2f", [512, true]], [-1, 1, "Conv", [1024, 3, 2]], [-1, 3, "C2f", [1024, true]], [-1, 1, "SPPF", [1024, 5]]], "head": [[-1, 1, "nn.Upsample", ["None", 2, "nearest"]], [[-1, 6], 1, "Concat", [1]], [-1, 3, "C2f", [512]], [-1, 1, "nn.Upsample", ["None", 2, "nearest"]], [[-1, 4], 1, "Concat", [1]], [-1, 3, "C2f", [256]], [-1, 1, "Conv", [256, 3, 2]], [[-1, 12], 1, "Concat", [1]], [-1, 3, "C2f", [512]], [-1, 1, "Conv", [512, 3, 2]], [[-1, 9], 1, "Concat", [1]], [-1, 3, "C2f", [1024]], [[15, 18, 21], 1, "Segment", ["nc", 32, 256]]], "yaml_file": "/usr/src/app/ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml", "ch": 3}, "nc": 7, "args": {"model": "yolov8s-seg.pt", "batch": 16, "imgsz": 640}, "ultralytics_version": "8.0.196", "model_type": "yolov8-seg"}
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58
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96
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100
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result/results.png ADDED
result/state_dict.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:428024acedacbbacf005ebe03e00951a68fd3b317adf3a84628f643105e685bd
3
+ size 23767263
result/train_batch0.jpg ADDED
result/train_batch1.jpg ADDED
result/train_batch10620.jpg ADDED
result/train_batch10621.jpg ADDED
result/train_batch10622.jpg ADDED
result/train_batch2.jpg ADDED
result/val_batch0_labels.jpg ADDED
result/val_batch0_pred.jpg ADDED
result/val_batch1_labels.jpg ADDED
result/val_batch1_pred.jpg ADDED
result/val_batch2_labels.jpg ADDED
result/val_batch2_pred.jpg ADDED
trafficImage/Trace.py ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pytz
2
+ import datetime
3
+ import os
4
+ import requests
5
+ from urllib.parse import urlparse
6
+ import time
7
+ from ultralytics import YOLO
8
+
9
+ def process_images(image_urls):
10
+ hong_kong_timezone = pytz.timezone('Asia/Hong_Kong')
11
+
12
+ while True:
13
+ current_time = datetime.datetime.now(tz=hong_kong_timezone).strftime("%Y%m%d%H%M%S")
14
+ folder_name = f"/content/{current_time}"
15
+ print(folder_name)
16
+ os.makedirs(folder_name, exist_ok=True)
17
+
18
+ for image_url in image_urls:
19
+ response = requests.get(image_url)
20
+ image_data = response.content
21
+ parsed_url = urlparse(image_url)
22
+ image_name = os.path.basename(parsed_url.path)
23
+ file_name = os.path.join(folder_name, image_name)
24
+ with open(file_name, "wb") as file:
25
+ file.write(image_data)
26
+ print(file_name)
27
+
28
+ folder_name_formatted = f"'{folder_name}'"
29
+
30
+ yolo = YOLO('/content/Smart-Traffic/best.pt')
31
+ yolo.set_conf(0.45)
32
+ yolo.predict(source=folder_name_formatted, save=True, save_txt=True)
33
+
34
+ time.sleep(120)
trafficImage/__init__.py ADDED
File without changes