nazlicanto commited on
Commit
4e707ae
1 Parent(s): b0820c1

creation of the space

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. 0defect_segmentation.ipynb +0 -0
  2. README.md +37 -1
  3. app06.py +38 -0
  4. model/config.json +82 -0
  5. model/preprocessor_config.json +23 -0
  6. model/pytorch_model.bin +3 -0
  7. requirements.txt +6 -0
  8. training/train/WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg +0 -0
  9. training/train/WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.xml +14 -0
  10. training/train/WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.jpg +0 -0
  11. training/train/WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.xml +14 -0
  12. training/train/WIN_20221017_18_46_59_Pro_jpg.rf.97f5d550dde3b43965a5400369167e52.jpg +0 -0
  13. training/train/WIN_20221017_18_46_59_Pro_jpg.rf.97f5d550dde3b43965a5400369167e52.xml +14 -0
  14. training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fbf91369966a68271571a4e676c89f9d.jpg +0 -0
  15. training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fbf91369966a68271571a4e676c89f9d.xml +14 -0
  16. training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fff6f5430ca351843aeb9277506c1fe9.jpg +0 -0
  17. training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fff6f5430ca351843aeb9277506c1fe9.xml +14 -0
  18. training/train/WIN_20221017_18_48_12_Pro_jpg.rf.a0952389431e2d9456613cafc60cbabb.jpg +0 -0
  19. training/train/WIN_20221017_18_48_12_Pro_jpg.rf.a0952389431e2d9456613cafc60cbabb.xml +81 -0
  20. training/train/WIN_20221017_18_48_12_Pro_jpg.rf.aea23e817b46f38ede677defa9534b06.jpg +0 -0
  21. training/train/WIN_20221017_18_48_12_Pro_jpg.rf.aea23e817b46f38ede677defa9534b06.xml +81 -0
  22. training/train/WIN_20221017_18_50_25_Pro_jpg.rf.7453d114331880fb6a9bc898fd3f9b22.jpg +0 -0
  23. training/train/WIN_20221017_18_50_25_Pro_jpg.rf.7453d114331880fb6a9bc898fd3f9b22.xml +117 -0
  24. training/train/WIN_20221017_18_50_25_Pro_jpg.rf.74b08bfb0e81909af021c5e33a1074c5.jpg +0 -0
  25. training/train/WIN_20221017_18_50_25_Pro_jpg.rf.74b08bfb0e81909af021c5e33a1074c5.xml +117 -0
  26. training/train/WIN_20221017_18_51_03_Pro_jpg.rf.778a411efb67a46622fb530a8d63f2a3.jpg +0 -0
  27. training/train/WIN_20221017_18_51_03_Pro_jpg.rf.778a411efb67a46622fb530a8d63f2a3.xml +14 -0
  28. training/train/WIN_20221017_18_51_03_Pro_jpg.rf.7be31d8b1cb485eeb5896d4dea17fe86.jpg +0 -0
  29. training/train/WIN_20221017_18_51_03_Pro_jpg.rf.7be31d8b1cb485eeb5896d4dea17fe86.xml +14 -0
  30. training/train/WIN_20221017_18_53_01_Pro_jpg.rf.414cbf44c5148bb91fa5ab9fc1eaf418.jpg +0 -0
  31. training/train/WIN_20221017_18_53_01_Pro_jpg.rf.414cbf44c5148bb91fa5ab9fc1eaf418.xml +47 -0
  32. training/train/WIN_20221017_18_53_01_Pro_jpg.rf.bdc4c3e8d5735c97bcf05d503cccae96.jpg +0 -0
  33. training/train/WIN_20221017_18_53_01_Pro_jpg.rf.bdc4c3e8d5735c97bcf05d503cccae96.xml +47 -0
  34. training/train/WIN_20221017_18_54_24_Pro_jpg.rf.602b991b286b5ac8a79d048efcaa77b5.jpg +0 -0
  35. training/train/WIN_20221017_18_54_24_Pro_jpg.rf.602b991b286b5ac8a79d048efcaa77b5.xml +51 -0
  36. training/train/WIN_20221017_18_54_24_Pro_jpg.rf.d2a6837579ced3fe3499ee7031dfb6cf.jpg +0 -0
  37. training/train/WIN_20221017_18_54_24_Pro_jpg.rf.d2a6837579ced3fe3499ee7031dfb6cf.xml +14 -0
  38. training/train/WIN_20221017_18_56_15_Pro_jpg.rf.5561859c2847247027b6612ba5c94c27.jpg +0 -0
  39. training/train/WIN_20221017_18_56_15_Pro_jpg.rf.5561859c2847247027b6612ba5c94c27.xml +107 -0
  40. training/train/WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.jpg +0 -0
  41. training/train/WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.xml +107 -0
  42. training/train/WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.jpg +0 -0
  43. training/train/WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.xml +119 -0
  44. training/train/WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg +0 -0
  45. training/train/WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.xml +119 -0
  46. training/train/WIN_20221017_18_57_55_Pro_jpg.rf.f93b9c99a8e2d1540f0dfb9f555c70d0.jpg +0 -0
  47. training/train/WIN_20221017_18_57_55_Pro_jpg.rf.f93b9c99a8e2d1540f0dfb9f555c70d0.xml +119 -0
  48. training/train/WIN_20221017_18_58_20_Pro_jpg.rf.0f432a0916e318aa3a6b08da08887641.jpg +0 -0
  49. training/train/WIN_20221017_18_58_20_Pro_jpg.rf.0f432a0916e318aa3a6b08da08887641.xml +316 -0
  50. training/train/WIN_20221017_18_58_20_Pro_jpg.rf.16824b392786dec636781120a87fbd27.jpg +0 -0
0defect_segmentation.ipynb ADDED
The diff for this file is too large to render. See raw diff
 
README.md CHANGED
@@ -5,9 +5,45 @@ colorFrom: blue
5
  colorTo: yellow
6
  sdk: streamlit
7
  sdk_version: 1.27.2
8
- app_file: app.py
9
  pinned: false
10
  license: mit
11
  ---
12
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
13
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
5
  colorTo: yellow
6
  sdk: streamlit
7
  sdk_version: 1.27.2
8
+ app_file: app06.py
9
  pinned: false
10
  license: mit
11
  ---
12
 
13
+
14
+ # 🛠️ PCB Defect Detection App
15
+
16
+ This app allows users to upload PCB images and detect defects using state-of-the-art machine learning models.
17
+
18
+ ## 🌟 Features
19
+
20
+ - **Image Upload**: Easily upload your PCB images and get instant defect predictions.
21
+ - **Visualization**: Visualize the detected defects on the PCB image.
22
+ - **Defect Types**: The app can identify multiple types of defects and highlight them uniquely for easy identification.
23
+
24
+ ## 🚀 Usage
25
+
26
+ ### 1️⃣ Uploading an Image:
27
+ - Click on the "Browse files" button.
28
+ - Select a PCB image from your device.
29
+ - Sit back and relax! Let the model churn through the image and present its findings.
30
+
31
+ ### 2️⃣ Interpreting Results:
32
+ - It will display the original image alongside the predicted defect mask.
33
+ - Different defect types will be highlighted using unique grayscale values.
34
+
35
+ ## Model Details
36
+
37
+ The app utilzes the Segformer model trained on a custom PCB dataset. The model has been fine-tuned to detect:
38
+ - **Incorrect Installation**
39
+ - **Short Circuit**
40
+ - **Dry Joints**
41
+
42
+ ... commonly found defects in PCBs.
43
+
44
+
45
+ ## 📜 Requirements
46
+
47
+ The app is built using `Streamlit` and leverages the `Hugging Face Transformers` library for model inference. For a full list of requirements, refer to the `requirements.txt` file.
48
+
49
  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app06.py ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import streamlit as st
3
+ from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
4
+ from PIL import Image
5
+ import numpy as np
6
+ import torch
7
+
8
+ # Load the model and processor
9
+ model_dir = "C://Users//nazli//OneDrive//Desktop//PCB-Defect-Detection-Segmentation//model11//model//"
10
+ model = SegformerForSemanticSegmentation.from_pretrained(model_dir)
11
+ processor = SegformerImageProcessor.from_pretrained(model_dir)
12
+ model.eval()
13
+
14
+ st.title("PCB Defect Detection")
15
+
16
+ # Upload image in Streamlit
17
+ uploaded_file = st.file_uploader("Upload a PCB image", type=["jpg", "png"])
18
+
19
+ if uploaded_file:
20
+ # Preprocess the image
21
+ test_image = Image.open(uploaded_file).convert("RGB")
22
+ inputs = processor(images=test_image, return_tensors="pt")
23
+
24
+ # Model inference
25
+ with torch.no_grad():
26
+ outputs = model(**inputs)
27
+
28
+ # Post-process
29
+ semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[test_image.size[::-1]])[0]
30
+ semantic_map = np.uint8(semantic_map)
31
+ semantic_map[semantic_map==1] = 255
32
+ semantic_map[semantic_map==2] = 195
33
+ semantic_map[semantic_map==3] = 135
34
+ semantic_map[semantic_map==4] = 75
35
+
36
+ # Display the results
37
+ st.image(test_image, caption="Uploaded Image", use_column_width=True)
38
+ st.image(semantic_map, caption="Predicted Defects", use_column_width=True, channels="GRAY")
model/config.json ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "segformer-b0-finetuned-pcb-outputs/checkpoint-16500",
3
+ "architectures": [
4
+ "SegformerForSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
8
+ "decoder_hidden_size": 256,
9
+ "depths": [
10
+ 2,
11
+ 2,
12
+ 2,
13
+ 2
14
+ ],
15
+ "downsampling_rates": [
16
+ 1,
17
+ 4,
18
+ 8,
19
+ 16
20
+ ],
21
+ "drop_path_rate": 0.1,
22
+ "hidden_act": "gelu",
23
+ "hidden_dropout_prob": 0.0,
24
+ "hidden_sizes": [
25
+ 32,
26
+ 64,
27
+ 160,
28
+ 256
29
+ ],
30
+ "id2label": {
31
+ "0": "background",
32
+ "1": "Short_circuit",
33
+ "2": "Dry_joint",
34
+ "3": "Incorrect_installation"
35
+ },
36
+ "image_size": 224,
37
+ "initializer_range": 0.02,
38
+ "label2id": {
39
+ "Dry_joint": 2,
40
+ "Incorrect_installation": 3,
41
+ "Short_circuit": 1,
42
+ "background": 0
43
+ },
44
+ "layer_norm_eps": 1e-06,
45
+ "mlp_ratios": [
46
+ 4,
47
+ 4,
48
+ 4,
49
+ 4
50
+ ],
51
+ "model_type": "segformer",
52
+ "num_attention_heads": [
53
+ 1,
54
+ 2,
55
+ 5,
56
+ 8
57
+ ],
58
+ "num_channels": 3,
59
+ "num_encoder_blocks": 4,
60
+ "patch_sizes": [
61
+ 7,
62
+ 3,
63
+ 3,
64
+ 3
65
+ ],
66
+ "reshape_last_stage": true,
67
+ "semantic_loss_ignore_index": 255,
68
+ "sr_ratios": [
69
+ 8,
70
+ 4,
71
+ 2,
72
+ 1
73
+ ],
74
+ "strides": [
75
+ 4,
76
+ 2,
77
+ 2,
78
+ 2
79
+ ],
80
+ "torch_dtype": "float32",
81
+ "transformers_version": "4.34.0"
82
+ }
model/preprocessor_config.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "do_normalize": true,
3
+ "do_reduce_labels": false,
4
+ "do_rescale": true,
5
+ "do_resize": true,
6
+ "image_mean": [
7
+ 0.485,
8
+ 0.456,
9
+ 0.406
10
+ ],
11
+ "image_processor_type": "SegformerImageProcessor",
12
+ "image_std": [
13
+ 0.229,
14
+ 0.224,
15
+ 0.225
16
+ ],
17
+ "resample": 2,
18
+ "rescale_factor": 0.00392156862745098,
19
+ "size": {
20
+ "height": 512,
21
+ "width": 512
22
+ }
23
+ }
model/pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:caa39910006f4344fe9f3ee950bfcfb8de2b2090a3fcd59354832e9c4b0aee36
3
+ size 14930957
requirements.txt ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ numpy==1.26.0
2
+ Pillow==9.5.0
3
+ Pillow==10.0.1
4
+ streamlit==1.24.1
5
+ torch==2.0.1
6
+ transformers==4.30.2
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg ADDED
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg</filename>
4
+ <path>WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.jpg ADDED
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.jpg</filename>
4
+ <path>WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.97f5d550dde3b43965a5400369167e52.jpg ADDED
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.97f5d550dde3b43965a5400369167e52.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_46_59_Pro_jpg.rf.97f5d550dde3b43965a5400369167e52.jpg</filename>
4
+ <path>WIN_20221017_18_46_59_Pro_jpg.rf.97f5d550dde3b43965a5400369167e52.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fbf91369966a68271571a4e676c89f9d.jpg ADDED
training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fbf91369966a68271571a4e676c89f9d.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_48_04_Pro_jpg.rf.fbf91369966a68271571a4e676c89f9d.jpg</filename>
4
+ <path>WIN_20221017_18_48_04_Pro_jpg.rf.fbf91369966a68271571a4e676c89f9d.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fff6f5430ca351843aeb9277506c1fe9.jpg ADDED
training/train/WIN_20221017_18_48_04_Pro_jpg.rf.fff6f5430ca351843aeb9277506c1fe9.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_48_04_Pro_jpg.rf.fff6f5430ca351843aeb9277506c1fe9.jpg</filename>
4
+ <path>WIN_20221017_18_48_04_Pro_jpg.rf.fff6f5430ca351843aeb9277506c1fe9.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_48_12_Pro_jpg.rf.a0952389431e2d9456613cafc60cbabb.jpg ADDED
training/train/WIN_20221017_18_48_12_Pro_jpg.rf.a0952389431e2d9456613cafc60cbabb.xml ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_48_12_Pro_jpg.rf.a0952389431e2d9456613cafc60cbabb.jpg</filename>
4
+ <path>WIN_20221017_18_48_12_Pro_jpg.rf.a0952389431e2d9456613cafc60cbabb.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Incorrect_installation</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>208</xmin>
22
+ <xmax>322</xmax>
23
+ <ymin>225</ymin>
24
+ <ymax>351</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>214</x1>
28
+ <y1>258.8</y1>
29
+ <x2>220</x2>
30
+ <y2>259.4</y2>
31
+ <x3>206.5</x3>
32
+ <y3>298.09</y3>
33
+ <x4>250.66</x4>
34
+ <y4>313.64</y4>
35
+ <x5>248.1</x5>
36
+ <y5>326.1</y5>
37
+ <x6>244</x6>
38
+ <y6>326.8</y6>
39
+ <x7>241.9</x7>
40
+ <y7>347.9</y7>
41
+ <x8>273.2</x8>
42
+ <y8>350.1</y8>
43
+ <x9>275.5</x9>
44
+ <y9>322.6</y9>
45
+ <x10>307.6</x10>
46
+ <y10>333.09</y10>
47
+ <x11>321.21</x11>
48
+ <y11>291.14</y11>
49
+ <x12>314.27</x12>
50
+ <y12>283.37</y12>
51
+ <x13>320.66</x13>
52
+ <y13>264.2</y13>
53
+ <x14>318.2</x14>
54
+ <y14>259</y14>
55
+ <x15>318.99</x15>
56
+ <y15>233.37</y15>
57
+ <x16>287.3</x16>
58
+ <y16>231.1</y16>
59
+ <x17>282.88</x17>
60
+ <y17>263.37</y17>
61
+ <x18>303.99</x18>
62
+ <y18>265.59</y18>
63
+ <x19>301.21</x19>
64
+ <y19>277.26</y19>
65
+ <x20>295.1</x20>
66
+ <y20>279.48</y20>
67
+ <x21>248.16</x21>
68
+ <y21>262.54</y21>
69
+ <x22>255.1</x22>
70
+ <y22>241.98</y22>
71
+ <x23>248.16</x23>
72
+ <y23>236.98</y23>
73
+ <x24>249</x24>
74
+ <y24>227.3</y24>
75
+ <x25>214.6</x25>
76
+ <y25>223.8</y25>
77
+ <x26>214</x26>
78
+ <y26>258.8</y26>
79
+ </polygon>
80
+ </object>
81
+ </annotation>
training/train/WIN_20221017_18_48_12_Pro_jpg.rf.aea23e817b46f38ede677defa9534b06.jpg ADDED
training/train/WIN_20221017_18_48_12_Pro_jpg.rf.aea23e817b46f38ede677defa9534b06.xml ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_48_12_Pro_jpg.rf.aea23e817b46f38ede677defa9534b06.jpg</filename>
4
+ <path>WIN_20221017_18_48_12_Pro_jpg.rf.aea23e817b46f38ede677defa9534b06.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Incorrect_installation</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>320</xmin>
22
+ <xmax>435</xmax>
23
+ <ymin>131</ymin>
24
+ <ymax>257</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>426</x1>
28
+ <y1>221.2</y1>
29
+ <x2>420</x2>
30
+ <y2>220.6</y2>
31
+ <x3>433.5</x3>
32
+ <y3>181.91</y3>
33
+ <x4>389.34</x4>
34
+ <y4>166.36</y4>
35
+ <x5>391.9</x5>
36
+ <y5>153.9</y5>
37
+ <x6>396</x6>
38
+ <y6>153.2</y6>
39
+ <x7>398.1</x7>
40
+ <y7>132.1</y7>
41
+ <x8>366.8</x8>
42
+ <y8>129.9</y8>
43
+ <x9>364.5</x9>
44
+ <y9>157.4</y9>
45
+ <x10>332.4</x10>
46
+ <y10>146.91</y10>
47
+ <x11>318.79</x11>
48
+ <y11>188.86</y11>
49
+ <x12>325.73</x12>
50
+ <y12>196.63</y12>
51
+ <x13>319.34</x13>
52
+ <y13>215.8</y13>
53
+ <x14>321.8</x14>
54
+ <y14>221</y14>
55
+ <x15>321.01</x15>
56
+ <y15>246.63</y15>
57
+ <x16>352.7</x16>
58
+ <y16>248.9</y16>
59
+ <x17>357.12</x17>
60
+ <y17>216.63</y17>
61
+ <x18>336.01</x18>
62
+ <y18>214.41</y18>
63
+ <x19>338.79</x19>
64
+ <y19>202.74</y19>
65
+ <x20>344.9</x20>
66
+ <y20>200.52</y20>
67
+ <x21>391.84</x21>
68
+ <y21>217.46</y21>
69
+ <x22>384.9</x22>
70
+ <y22>238.02</y22>
71
+ <x23>391.84</x23>
72
+ <y23>243.02</y23>
73
+ <x24>391</x24>
74
+ <y24>252.7</y24>
75
+ <x25>425.4</x25>
76
+ <y25>256.2</y25>
77
+ <x26>426</x26>
78
+ <y26>221.2</y26>
79
+ </polygon>
80
+ </object>
81
+ </annotation>
training/train/WIN_20221017_18_50_25_Pro_jpg.rf.7453d114331880fb6a9bc898fd3f9b22.jpg ADDED
training/train/WIN_20221017_18_50_25_Pro_jpg.rf.7453d114331880fb6a9bc898fd3f9b22.xml ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_50_25_Pro_jpg.rf.7453d114331880fb6a9bc898fd3f9b22.jpg</filename>
4
+ <path>WIN_20221017_18_50_25_Pro_jpg.rf.7453d114331880fb6a9bc898fd3f9b22.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Short_circuit</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>198</xmin>
22
+ <xmax>394</xmax>
23
+ <ymin>186</ymin>
24
+ <ymax>347</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>373.8</x1>
28
+ <y1>193.44</y1>
29
+ <x2>378.78</x2>
30
+ <y2>186.66</y2>
31
+ <x3>352.54</x3>
32
+ <y3>184.85</y3>
33
+ <x4>344.39</x4>
34
+ <y4>189.37</y4>
35
+ <x5>337.16</x5>
36
+ <y5>193.89</y5>
37
+ <x6>323.13</x6>
38
+ <y6>202.04</y6>
39
+ <x7>313.63</x7>
40
+ <y7>213.8</y7>
41
+ <x8>309.56</x8>
42
+ <y8>224.21</y8>
43
+ <x9>319.06</x9>
44
+ <y9>232.35</y9>
45
+ <x10>307.3</x10>
46
+ <y10>244.56</y10>
47
+ <x11>288.75</x11>
48
+ <y11>263.11</y11>
49
+ <x12>288.75</x12>
50
+ <y12>273.52</y12>
51
+ <x13>280.6</x13>
52
+ <y13>280.3</y13>
53
+ <x14>223.15</x14>
54
+ <y14>313.33</y14>
55
+ <x15>210.03</x15>
56
+ <y15>318.76</y15>
57
+ <x16>196.91</x16>
58
+ <y16>336.85</y16>
59
+ <x17>197.81</x17>
60
+ <y17>345.9</y17>
61
+ <x18>199.62</x18>
62
+ <y18>345.9</y18>
63
+ <x19>219.53</x19>
64
+ <y19>337.76</y19>
65
+ <x20>239.89</x20>
66
+ <y20>321.93</y20>
67
+ <x21>263.41</x21>
68
+ <y21>308.35</y21>
69
+ <x22>288.3</x22>
70
+ <y22>297.95</y22>
71
+ <x23>304.58</x23>
72
+ <y23>299.76</y23>
73
+ <x24>316.8</x24>
74
+ <y24>299.76</y24>
75
+ <x25>333.08</x25>
76
+ <y25>293.88</y25>
77
+ <x26>350.28</x26>
78
+ <y26>285.73</y26>
79
+ <x27>357.97</x27>
80
+ <y27>278.95</y27>
81
+ <x28>363.39</x28>
82
+ <y28>273.97</y28>
83
+ <x29>369.28</x29>
84
+ <y29>269.45</y29>
85
+ <x30>382.85</x30>
86
+ <y30>264.92</y30>
87
+ <x31>387.37</x31>
88
+ <y31>261.76</y31>
89
+ <x32>392.35</x32>
90
+ <y32>252.71</y32>
91
+ <x33>392.8</x33>
92
+ <y33>242.75</y33>
93
+ <x34>393.25</x34>
94
+ <y34>235.52</y34>
95
+ <x35>392.35</x35>
96
+ <y35>231.9</y35>
97
+ <x36>390.99</x36>
98
+ <y36>227.37</y36>
99
+ <x37>389.63</x37>
100
+ <y37>220.59</y37>
101
+ <x38>386.92</x38>
102
+ <y38>217.87</y38>
103
+ <x39>383.3</x39>
104
+ <y39>216.97</y39>
105
+ <x40>381.94</x40>
106
+ <y40>214.25</y40>
107
+ <x41>378.78</x41>
108
+ <y41>209.73</y41>
109
+ <x42>376.6</x42>
110
+ <y42>205.8</y42>
111
+ <x43>371.99</x43>
112
+ <y43>205.66</y43>
113
+ <x44>373.8</x44>
114
+ <y44>193.44</y44>
115
+ </polygon>
116
+ </object>
117
+ </annotation>
training/train/WIN_20221017_18_50_25_Pro_jpg.rf.74b08bfb0e81909af021c5e33a1074c5.jpg ADDED
training/train/WIN_20221017_18_50_25_Pro_jpg.rf.74b08bfb0e81909af021c5e33a1074c5.xml ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_50_25_Pro_jpg.rf.74b08bfb0e81909af021c5e33a1074c5.jpg</filename>
4
+ <path>WIN_20221017_18_50_25_Pro_jpg.rf.74b08bfb0e81909af021c5e33a1074c5.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Short_circuit</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>248</xmin>
22
+ <xmax>444</xmax>
23
+ <ymin>135</ymin>
24
+ <ymax>296</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>266.2</x1>
28
+ <y1>286.56</y1>
29
+ <x2>261.22</x2>
30
+ <y2>293.34</y2>
31
+ <x3>287.46</x3>
32
+ <y3>295.15</y3>
33
+ <x4>295.61</x4>
34
+ <y4>290.63</y4>
35
+ <x5>302.84</x5>
36
+ <y5>286.11</y5>
37
+ <x6>316.87</x6>
38
+ <y6>277.96</y6>
39
+ <x7>326.37</x7>
40
+ <y7>266.2</y7>
41
+ <x8>330.44</x8>
42
+ <y8>255.79</y8>
43
+ <x9>320.94</x9>
44
+ <y9>247.65</y9>
45
+ <x10>332.7</x10>
46
+ <y10>235.44</y10>
47
+ <x11>351.25</x11>
48
+ <y11>216.89</y11>
49
+ <x12>351.25</x12>
50
+ <y12>206.48</y12>
51
+ <x13>359.4</x13>
52
+ <y13>199.7</y13>
53
+ <x14>416.85</x14>
54
+ <y14>166.67</y14>
55
+ <x15>429.97</x15>
56
+ <y15>161.24</y15>
57
+ <x16>443.09</x16>
58
+ <y16>143.15</y16>
59
+ <x17>442.19</x17>
60
+ <y17>134.1</y17>
61
+ <x18>440.38</x18>
62
+ <y18>134.1</y18>
63
+ <x19>420.47</x19>
64
+ <y19>142.24</y19>
65
+ <x20>400.11</x20>
66
+ <y20>158.07</y20>
67
+ <x21>376.59</x21>
68
+ <y21>171.65</y21>
69
+ <x22>351.7</x22>
70
+ <y22>182.05</y22>
71
+ <x23>335.42</x23>
72
+ <y23>180.24</y23>
73
+ <x24>323.2</x24>
74
+ <y24>180.24</y24>
75
+ <x25>306.92</x25>
76
+ <y25>186.12</y25>
77
+ <x26>289.72</x26>
78
+ <y26>194.27</y26>
79
+ <x27>282.03</x27>
80
+ <y27>201.05</y27>
81
+ <x28>276.61</x28>
82
+ <y28>206.03</y28>
83
+ <x29>270.72</x29>
84
+ <y29>210.55</y29>
85
+ <x30>257.15</x30>
86
+ <y30>215.08</y30>
87
+ <x31>252.63</x31>
88
+ <y31>218.24</y31>
89
+ <x32>247.65</x32>
90
+ <y32>227.29</y32>
91
+ <x33>247.2</x33>
92
+ <y33>237.25</y33>
93
+ <x34>246.75</x34>
94
+ <y34>244.48</y34>
95
+ <x35>247.65</x35>
96
+ <y35>248.1</y35>
97
+ <x36>249.01</x36>
98
+ <y36>252.63</y36>
99
+ <x37>250.37</x37>
100
+ <y37>259.41</y37>
101
+ <x38>253.08</x38>
102
+ <y38>262.13</y38>
103
+ <x39>256.7</x39>
104
+ <y39>263.03</y39>
105
+ <x40>258.06</x40>
106
+ <y40>265.75</y40>
107
+ <x41>261.22</x41>
108
+ <y41>270.27</y41>
109
+ <x42>263.4</x42>
110
+ <y42>274.2</y42>
111
+ <x43>268.01</x43>
112
+ <y43>274.34</y43>
113
+ <x44>266.2</x44>
114
+ <y44>286.56</y44>
115
+ </polygon>
116
+ </object>
117
+ </annotation>
training/train/WIN_20221017_18_51_03_Pro_jpg.rf.778a411efb67a46622fb530a8d63f2a3.jpg ADDED
training/train/WIN_20221017_18_51_03_Pro_jpg.rf.778a411efb67a46622fb530a8d63f2a3.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_51_03_Pro_jpg.rf.778a411efb67a46622fb530a8d63f2a3.jpg</filename>
4
+ <path>WIN_20221017_18_51_03_Pro_jpg.rf.778a411efb67a46622fb530a8d63f2a3.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_51_03_Pro_jpg.rf.7be31d8b1cb485eeb5896d4dea17fe86.jpg ADDED
training/train/WIN_20221017_18_51_03_Pro_jpg.rf.7be31d8b1cb485eeb5896d4dea17fe86.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_51_03_Pro_jpg.rf.7be31d8b1cb485eeb5896d4dea17fe86.jpg</filename>
4
+ <path>WIN_20221017_18_51_03_Pro_jpg.rf.7be31d8b1cb485eeb5896d4dea17fe86.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_53_01_Pro_jpg.rf.414cbf44c5148bb91fa5ab9fc1eaf418.jpg ADDED
training/train/WIN_20221017_18_53_01_Pro_jpg.rf.414cbf44c5148bb91fa5ab9fc1eaf418.xml ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_53_01_Pro_jpg.rf.414cbf44c5148bb91fa5ab9fc1eaf418.jpg</filename>
4
+ <path>WIN_20221017_18_53_01_Pro_jpg.rf.414cbf44c5148bb91fa5ab9fc1eaf418.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Incorrect_installation</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>212</xmin>
22
+ <xmax>272</xmax>
23
+ <ymin>1</ymin>
24
+ <ymax>80</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>211.01</x1>
28
+ <y1>0</y1>
29
+ <x2>216.55</x2>
30
+ <y2>66.77</y2>
31
+ <x3>234.88</x3>
32
+ <y3>64.83</y3>
33
+ <x4>236</x4>
34
+ <y4>78.72</y4>
35
+ <x5>270.71</x5>
36
+ <y5>74</y5>
37
+ <x6>267.38</x6>
38
+ <y6>45.94</y6>
39
+ <x7>261.27</x7>
40
+ <y7>46.5</y7>
41
+ <x8>256.59</x8>
42
+ <y8>0</y8>
43
+ <x9>211.01</x9>
44
+ <y9>0</y9>
45
+ </polygon>
46
+ </object>
47
+ </annotation>
training/train/WIN_20221017_18_53_01_Pro_jpg.rf.bdc4c3e8d5735c97bcf05d503cccae96.jpg ADDED
training/train/WIN_20221017_18_53_01_Pro_jpg.rf.bdc4c3e8d5735c97bcf05d503cccae96.xml ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_53_01_Pro_jpg.rf.bdc4c3e8d5735c97bcf05d503cccae96.jpg</filename>
4
+ <path>WIN_20221017_18_53_01_Pro_jpg.rf.bdc4c3e8d5735c97bcf05d503cccae96.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Incorrect_installation</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>81</xmin>
22
+ <xmax>160</xmax>
23
+ <ymin>290</ymin>
24
+ <ymax>350</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>126.5</x1>
28
+ <y1>298.73</y1>
29
+ <x2>80</x2>
30
+ <y2>303.41</y2>
31
+ <x3>80</x3>
32
+ <y3>348.99</y3>
33
+ <x4>146.77</x4>
34
+ <y4>343.45</y4>
35
+ <x5>144.83</x5>
36
+ <y5>325.12</y5>
37
+ <x6>158.72</x6>
38
+ <y6>324</y6>
39
+ <x7>154</x7>
40
+ <y7>289.29</y7>
41
+ <x8>125.94</x8>
42
+ <y8>292.62</y8>
43
+ <x9>126.5</x9>
44
+ <y9>298.73</y9>
45
+ </polygon>
46
+ </object>
47
+ </annotation>
training/train/WIN_20221017_18_54_24_Pro_jpg.rf.602b991b286b5ac8a79d048efcaa77b5.jpg ADDED
training/train/WIN_20221017_18_54_24_Pro_jpg.rf.602b991b286b5ac8a79d048efcaa77b5.xml ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_54_24_Pro_jpg.rf.602b991b286b5ac8a79d048efcaa77b5.jpg</filename>
4
+ <path>WIN_20221017_18_54_24_Pro_jpg.rf.602b991b286b5ac8a79d048efcaa77b5.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Incorrect_installation</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>542</xmin>
22
+ <xmax>641</xmax>
23
+ <ymin>129</ymin>
24
+ <ymax>266</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>640</x1>
28
+ <y1>140.14</y1>
29
+ <x2>631.33</x2>
30
+ <y2>128.13</y2>
31
+ <x3>540.54</x3>
32
+ <y3>201.72</y3>
33
+ <x4>574.93</x4>
34
+ <y4>245.32</y4>
35
+ <x5>582.93</x5>
36
+ <y5>242.52</y5>
37
+ <x6>601.33</x6>
38
+ <y6>265.32</y6>
39
+ <x7>640</x7>
40
+ <y7>233.75</y7>
41
+ <x8>640</x8>
42
+ <y8>232.64</y8>
43
+ <x9>624.13</x9>
44
+ <y9>212.12</y9>
45
+ <x10>640</x10>
46
+ <y10>197.69</y10>
47
+ <x11>640</x11>
48
+ <y11>140.14</y11>
49
+ </polygon>
50
+ </object>
51
+ </annotation>
training/train/WIN_20221017_18_54_24_Pro_jpg.rf.d2a6837579ced3fe3499ee7031dfb6cf.jpg ADDED
training/train/WIN_20221017_18_54_24_Pro_jpg.rf.d2a6837579ced3fe3499ee7031dfb6cf.xml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_54_24_Pro_jpg.rf.d2a6837579ced3fe3499ee7031dfb6cf.jpg</filename>
4
+ <path>WIN_20221017_18_54_24_Pro_jpg.rf.d2a6837579ced3fe3499ee7031dfb6cf.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ </annotation>
training/train/WIN_20221017_18_56_15_Pro_jpg.rf.5561859c2847247027b6612ba5c94c27.jpg ADDED
training/train/WIN_20221017_18_56_15_Pro_jpg.rf.5561859c2847247027b6612ba5c94c27.xml ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_56_15_Pro_jpg.rf.5561859c2847247027b6612ba5c94c27.jpg</filename>
4
+ <path>WIN_20221017_18_56_15_Pro_jpg.rf.5561859c2847247027b6612ba5c94c27.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Dry_joint</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>381</xmin>
22
+ <xmax>454</xmax>
23
+ <ymin>158</ymin>
24
+ <ymax>232</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>401.3</x1>
28
+ <y1>160.5</y1>
29
+ <x2>391.9</x2>
30
+ <y2>166.5</y2>
31
+ <x3>384.4</x3>
32
+ <y3>175</y3>
33
+ <x4>380.2</x4>
34
+ <y4>187.3</y4>
35
+ <x5>379.8</x5>
36
+ <y5>194.9</y5>
37
+ <x6>380.3</x6>
38
+ <y6>201</y6>
39
+ <x7>380.6</x7>
40
+ <y7>203.5</y7>
41
+ <x8>380.2</x8>
42
+ <y8>205.4</y8>
43
+ <x9>379.9</x9>
44
+ <y9>209</y9>
45
+ <x10>380.5</x10>
46
+ <y10>211</y10>
47
+ <x11>382.4</x11>
48
+ <y11>214.5</y11>
49
+ <x12>384.2</x12>
50
+ <y12>215.4</y12>
51
+ <x13>386.7</x13>
52
+ <y13>214.4</y13>
53
+ <x14>390.5</x14>
54
+ <y14>218.8</y14>
55
+ <x15>394.8</x15>
56
+ <y15>222.3</y15>
57
+ <x16>400.6</x16>
58
+ <y16>226.3</y16>
59
+ <x17>407.9</x17>
60
+ <y17>228.7</y17>
61
+ <x18>415.8</x18>
62
+ <y18>230.5</y18>
63
+ <x19>424</x19>
64
+ <y19>230.1</y19>
65
+ <x20>430.6</x20>
66
+ <y20>228</y20>
67
+ <x21>435.3</x21>
68
+ <y21>225.7</y21>
69
+ <x22>440.7</x22>
70
+ <y22>221.3</y22>
71
+ <x23>445.6</x23>
72
+ <y23>217.4</y23>
73
+ <x24>450.1</x24>
74
+ <y24>209</y24>
75
+ <x25>451.8</x25>
76
+ <y25>202.4</y25>
77
+ <x26>452.5</x26>
78
+ <y26>198.5</y26>
79
+ <x27>452.5</x27>
80
+ <y27>192.4</y27>
81
+ <x28>452.2</x28>
82
+ <y28>188.2</y28>
83
+ <x29>451.5</x29>
84
+ <y29>183.9</y29>
85
+ <x30>449.7</x30>
86
+ <y30>178.7</y30>
87
+ <x31>447.4</x31>
88
+ <y31>174.8</y31>
89
+ <x32>444.4</x32>
90
+ <y32>170</y32>
91
+ <x33>438.7</x33>
92
+ <y33>165.9</y33>
93
+ <x34>435.1</x34>
94
+ <y34>163.1</y34>
95
+ <x35>431.9</x35>
96
+ <y35>161.6</y35>
97
+ <x36>426.9</x36>
98
+ <y36>158.9</y36>
99
+ <x37>420.5</x37>
100
+ <y37>156.9</y37>
101
+ <x38>411.8</x38>
102
+ <y38>157.5</y38>
103
+ <x39>401.3</x39>
104
+ <y39>160.5</y39>
105
+ </polygon>
106
+ </object>
107
+ </annotation>
training/train/WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.jpg ADDED
training/train/WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.xml ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.jpg</filename>
4
+ <path>WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Dry_joint</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>331</xmin>
22
+ <xmax>404</xmax>
23
+ <ymin>301</ymin>
24
+ <ymax>374</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>399.5</x1>
28
+ <y1>321.3</y1>
29
+ <x2>393.5</x2>
30
+ <y2>311.9</y2>
31
+ <x3>385</x3>
32
+ <y3>304.4</y3>
33
+ <x4>372.7</x4>
34
+ <y4>300.2</y4>
35
+ <x5>365.1</x5>
36
+ <y5>299.8</y5>
37
+ <x6>359</x6>
38
+ <y6>300.3</y6>
39
+ <x7>356.5</x7>
40
+ <y7>300.6</y7>
41
+ <x8>354.6</x8>
42
+ <y8>300.2</y8>
43
+ <x9>351</x9>
44
+ <y9>299.9</y9>
45
+ <x10>349</x10>
46
+ <y10>300.5</y10>
47
+ <x11>345.5</x11>
48
+ <y11>302.4</y11>
49
+ <x12>344.6</x12>
50
+ <y12>304.2</y12>
51
+ <x13>345.6</x13>
52
+ <y13>306.7</y13>
53
+ <x14>341.2</x14>
54
+ <y14>310.5</y14>
55
+ <x15>337.7</x15>
56
+ <y15>314.8</y15>
57
+ <x16>333.7</x16>
58
+ <y16>320.6</y16>
59
+ <x17>331.3</x17>
60
+ <y17>327.9</y17>
61
+ <x18>329.5</x18>
62
+ <y18>335.8</y18>
63
+ <x19>329.9</x19>
64
+ <y19>344</y19>
65
+ <x20>332</x20>
66
+ <y20>350.6</y20>
67
+ <x21>334.3</x21>
68
+ <y21>355.3</y21>
69
+ <x22>338.7</x22>
70
+ <y22>360.7</y22>
71
+ <x23>342.6</x23>
72
+ <y23>365.6</y23>
73
+ <x24>351</x24>
74
+ <y24>370.1</y24>
75
+ <x25>357.6</x25>
76
+ <y25>371.8</y25>
77
+ <x26>361.5</x26>
78
+ <y26>372.5</y26>
79
+ <x27>367.6</x27>
80
+ <y27>372.5</y27>
81
+ <x28>371.8</x28>
82
+ <y28>372.2</y28>
83
+ <x29>376.1</x29>
84
+ <y29>371.5</y29>
85
+ <x30>381.3</x30>
86
+ <y30>369.7</y30>
87
+ <x31>385.2</x31>
88
+ <y31>367.4</y31>
89
+ <x32>390</x32>
90
+ <y32>364.4</y32>
91
+ <x33>394.1</x33>
92
+ <y33>358.7</y33>
93
+ <x34>396.9</x34>
94
+ <y34>355.1</y34>
95
+ <x35>398.4</x35>
96
+ <y35>351.9</y35>
97
+ <x36>401.1</x36>
98
+ <y36>346.9</y36>
99
+ <x37>403.1</x37>
100
+ <y37>340.5</y37>
101
+ <x38>402.5</x38>
102
+ <y38>331.8</y38>
103
+ <x39>399.5</x39>
104
+ <y39>321.3</y39>
105
+ </polygon>
106
+ </object>
107
+ </annotation>
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.jpg ADDED
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.xml ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.jpg</filename>
4
+ <path>WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Short_circuit</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>252</xmin>
22
+ <xmax>335</xmax>
23
+ <ymin>143</ymin>
24
+ <ymax>223</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>295.9</x1>
28
+ <y1>141.6</y1>
29
+ <x2>289.7</x2>
30
+ <y2>144</y2>
31
+ <x3>284.5</x3>
32
+ <y3>150.3</y3>
33
+ <x4>288.5</x4>
34
+ <y4>156.5</y4>
35
+ <x5>296.6</x5>
36
+ <y5>166.4</y5>
37
+ <x6>296.6</x6>
38
+ <y6>170.3</y6>
39
+ <x7>292.4</x7>
40
+ <y7>176</y7>
41
+ <x8>288.3</x8>
42
+ <y8>179.7</y8>
43
+ <x9>282.2</x9>
44
+ <y9>179.7</y9>
45
+ <x10>276.1</x10>
46
+ <y10>177.3</y10>
47
+ <x11>263.3</x11>
48
+ <y11>174.4</y11>
49
+ <x12>259.8</x12>
50
+ <y12>174.4</y12>
51
+ <x13>255.9</x13>
52
+ <y13>177.3</y13>
53
+ <x14>254.6</x14>
54
+ <y14>178.1</y14>
55
+ <x15>250.9</x15>
56
+ <y15>181.6</y15>
57
+ <x16>250.5</x16>
58
+ <y16>184.4</y16>
59
+ <x17>251.6</x17>
60
+ <y17>186.4</y17>
61
+ <x18>251.6</x18>
62
+ <y18>191</y18>
63
+ <x19>252.2</x19>
64
+ <y19>196.8</y19>
65
+ <x20>255.3</x20>
66
+ <y20>200.1</y20>
67
+ <x21>259.2</x21>
68
+ <y21>201.4</y21>
69
+ <x22>264.4</x22>
70
+ <y22>201.4</y22>
71
+ <x23>267</x23>
72
+ <y23>203.6</y23>
73
+ <x24>270.7</x24>
74
+ <y24>206.6</y24>
75
+ <x25>279.3</x25>
76
+ <y25>211.8</y25>
77
+ <x26>288.9</x26>
78
+ <y26>222.1</y26>
79
+ <x27>299.8</x27>
80
+ <y27>211.8</y27>
81
+ <x28>297.9</x28>
82
+ <y28>209.5</y28>
83
+ <x29>291.4</x29>
84
+ <y29>201.7</y29>
85
+ <x30>285.3</x30>
86
+ <y30>195.3</y30>
87
+ <x31>285.3</x31>
88
+ <y31>193.3</y31>
89
+ <x32>286.2</x32>
90
+ <y32>193.1</y32>
91
+ <x33>288.8</x33>
92
+ <y33>192.2</y33>
93
+ <x34>294.8</x34>
94
+ <y34>190.7</y34>
95
+ <x35>298.8</x35>
96
+ <y35>186.6</y35>
97
+ <x36>302.4</x36>
98
+ <y36>185.3</y36>
99
+ <x37>304.9</x37>
100
+ <y37>184.8</y37>
101
+ <x38>307.3</x38>
102
+ <y38>183</y38>
103
+ <x39>308.9</x39>
104
+ <y39>179.8</y39>
105
+ <x40>310.7</x40>
106
+ <y40>179.5</y40>
107
+ <x41>314</x41>
108
+ <y41>181.1</y41>
109
+ <x42>316.6</x42>
110
+ <y42>184</y42>
111
+ <x43>323</x43>
112
+ <y43>190.7</y43>
113
+ <x44>333.9</x44>
114
+ <y44>180</y44>
115
+ <x45>295.9</x45>
116
+ <y45>141.6</y45>
117
+ </polygon>
118
+ </object>
119
+ </annotation>
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg ADDED
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.xml ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg</filename>
4
+ <path>WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Short_circuit</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>307</xmin>
22
+ <xmax>391</xmax>
23
+ <ymin>259</ymin>
24
+ <ymax>339</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>344.1</x1>
28
+ <y1>338.4</y1>
29
+ <x2>350.3</x2>
30
+ <y2>336</y2>
31
+ <x3>355.5</x3>
32
+ <y3>329.7</y3>
33
+ <x4>351.5</x4>
34
+ <y4>323.5</y4>
35
+ <x5>343.4</x5>
36
+ <y5>313.6</y5>
37
+ <x6>343.4</x6>
38
+ <y6>309.7</y6>
39
+ <x7>347.6</x7>
40
+ <y7>304</y7>
41
+ <x8>351.7</x8>
42
+ <y8>300.3</y8>
43
+ <x9>357.8</x9>
44
+ <y9>300.3</y9>
45
+ <x10>363.9</x10>
46
+ <y10>302.7</y10>
47
+ <x11>376.7</x11>
48
+ <y11>305.6</y11>
49
+ <x12>380.2</x12>
50
+ <y12>305.6</y12>
51
+ <x13>384.1</x13>
52
+ <y13>302.7</y13>
53
+ <x14>385.4</x14>
54
+ <y14>301.9</y14>
55
+ <x15>389.1</x15>
56
+ <y15>298.4</y15>
57
+ <x16>389.5</x16>
58
+ <y16>295.6</y16>
59
+ <x17>388.4</x17>
60
+ <y17>293.6</y17>
61
+ <x18>388.4</x18>
62
+ <y18>289</y18>
63
+ <x19>387.8</x19>
64
+ <y19>283.2</y19>
65
+ <x20>384.7</x20>
66
+ <y20>279.9</y20>
67
+ <x21>380.8</x21>
68
+ <y21>278.6</y21>
69
+ <x22>375.6</x22>
70
+ <y22>278.6</y22>
71
+ <x23>373</x23>
72
+ <y23>276.4</y23>
73
+ <x24>369.3</x24>
74
+ <y24>273.4</y24>
75
+ <x25>360.7</x25>
76
+ <y25>268.2</y25>
77
+ <x26>351.1</x26>
78
+ <y26>257.9</y26>
79
+ <x27>340.2</x27>
80
+ <y27>268.2</y27>
81
+ <x28>342.1</x28>
82
+ <y28>270.5</y28>
83
+ <x29>348.6</x29>
84
+ <y29>278.3</y29>
85
+ <x30>354.7</x30>
86
+ <y30>284.7</y30>
87
+ <x31>354.7</x31>
88
+ <y31>286.7</y31>
89
+ <x32>353.8</x32>
90
+ <y32>286.9</y32>
91
+ <x33>351.2</x33>
92
+ <y33>287.8</y33>
93
+ <x34>345.2</x34>
94
+ <y34>289.3</y34>
95
+ <x35>341.2</x35>
96
+ <y35>293.4</y35>
97
+ <x36>337.6</x36>
98
+ <y36>294.7</y36>
99
+ <x37>335.1</x37>
100
+ <y37>295.2</y37>
101
+ <x38>332.7</x38>
102
+ <y38>297</y38>
103
+ <x39>331.1</x39>
104
+ <y39>300.2</y39>
105
+ <x40>329.3</x40>
106
+ <y40>300.5</y40>
107
+ <x41>326</x41>
108
+ <y41>298.9</y41>
109
+ <x42>323.4</x42>
110
+ <y42>296</y42>
111
+ <x43>317</x43>
112
+ <y43>289.3</y43>
113
+ <x44>306.1</x44>
114
+ <y44>300</y44>
115
+ <x45>344.1</x45>
116
+ <y45>338.4</y45>
117
+ </polygon>
118
+ </object>
119
+ </annotation>
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.f93b9c99a8e2d1540f0dfb9f555c70d0.jpg ADDED
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.f93b9c99a8e2d1540f0dfb9f555c70d0.xml ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_57_55_Pro_jpg.rf.f93b9c99a8e2d1540f0dfb9f555c70d0.jpg</filename>
4
+ <path>WIN_20221017_18_57_55_Pro_jpg.rf.f93b9c99a8e2d1540f0dfb9f555c70d0.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Short_circuit</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>223</xmin>
22
+ <xmax>303</xmax>
23
+ <ymin>227</ymin>
24
+ <ymax>311</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>221.6</x1>
28
+ <y1>264.1</y1>
29
+ <x2>224</x2>
30
+ <y2>270.3</y2>
31
+ <x3>230.3</x3>
32
+ <y3>275.5</y3>
33
+ <x4>236.5</x4>
34
+ <y4>271.5</y4>
35
+ <x5>246.4</x5>
36
+ <y5>263.4</y5>
37
+ <x6>250.3</x6>
38
+ <y6>263.4</y6>
39
+ <x7>256</x7>
40
+ <y7>267.6</y7>
41
+ <x8>259.7</x8>
42
+ <y8>271.7</y8>
43
+ <x9>259.7</x9>
44
+ <y9>277.8</y9>
45
+ <x10>257.3</x10>
46
+ <y10>283.9</y10>
47
+ <x11>254.4</x11>
48
+ <y11>296.7</y11>
49
+ <x12>254.4</x12>
50
+ <y12>300.2</y12>
51
+ <x13>257.3</x13>
52
+ <y13>304.1</y13>
53
+ <x14>258.1</x14>
54
+ <y14>305.4</y14>
55
+ <x15>261.6</x15>
56
+ <y15>309.1</y15>
57
+ <x16>264.4</x16>
58
+ <y16>309.5</y16>
59
+ <x17>266.4</x17>
60
+ <y17>308.4</y17>
61
+ <x18>271</x18>
62
+ <y18>308.4</y18>
63
+ <x19>276.8</x19>
64
+ <y19>307.8</y19>
65
+ <x20>280.1</x20>
66
+ <y20>304.7</y20>
67
+ <x21>281.4</x21>
68
+ <y21>300.8</y21>
69
+ <x22>281.4</x22>
70
+ <y22>295.6</y22>
71
+ <x23>283.6</x23>
72
+ <y23>293</y23>
73
+ <x24>286.6</x24>
74
+ <y24>289.3</y24>
75
+ <x25>291.8</x25>
76
+ <y25>280.7</y25>
77
+ <x26>302.1</x26>
78
+ <y26>271.1</y26>
79
+ <x27>291.8</x27>
80
+ <y27>260.2</y27>
81
+ <x28>289.5</x28>
82
+ <y28>262.1</y28>
83
+ <x29>281.7</x29>
84
+ <y29>268.6</y29>
85
+ <x30>275.3</x30>
86
+ <y30>274.7</y30>
87
+ <x31>273.3</x31>
88
+ <y31>274.7</y31>
89
+ <x32>273.1</x32>
90
+ <y32>273.8</y32>
91
+ <x33>272.2</x33>
92
+ <y33>271.2</y33>
93
+ <x34>270.7</x34>
94
+ <y34>265.2</y34>
95
+ <x35>266.6</x35>
96
+ <y35>261.2</y35>
97
+ <x36>265.3</x36>
98
+ <y36>257.6</y36>
99
+ <x37>264.8</x37>
100
+ <y37>255.1</y37>
101
+ <x38>263</x38>
102
+ <y38>252.7</y38>
103
+ <x39>259.8</x39>
104
+ <y39>251.1</y39>
105
+ <x40>259.5</x40>
106
+ <y40>249.3</y40>
107
+ <x41>261.1</x41>
108
+ <y41>246</y41>
109
+ <x42>264</x42>
110
+ <y42>243.4</y42>
111
+ <x43>270.7</x43>
112
+ <y43>237</y43>
113
+ <x44>260</x44>
114
+ <y44>226.1</y44>
115
+ <x45>221.6</x45>
116
+ <y45>264.1</y45>
117
+ </polygon>
118
+ </object>
119
+ </annotation>
training/train/WIN_20221017_18_58_20_Pro_jpg.rf.0f432a0916e318aa3a6b08da08887641.jpg ADDED
training/train/WIN_20221017_18_58_20_Pro_jpg.rf.0f432a0916e318aa3a6b08da08887641.xml ADDED
@@ -0,0 +1,316 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <annotation>
2
+ <folder></folder>
3
+ <filename>WIN_20221017_18_58_20_Pro_jpg.rf.0f432a0916e318aa3a6b08da08887641.jpg</filename>
4
+ <path>WIN_20221017_18_58_20_Pro_jpg.rf.0f432a0916e318aa3a6b08da08887641.jpg</path>
5
+ <source>
6
+ <database>roboflow.ai</database>
7
+ </source>
8
+ <size>
9
+ <width>640</width>
10
+ <height>480</height>
11
+ <depth>3</depth>
12
+ </size>
13
+ <segmented>0</segmented>
14
+ <object>
15
+ <name>Short_circuit</name>
16
+ <pose>Unspecified</pose>
17
+ <truncated>0</truncated>
18
+ <difficult>0</difficult>
19
+ <occluded>0</occluded>
20
+ <bndbox>
21
+ <xmin>252</xmin>
22
+ <xmax>347</xmax>
23
+ <ymin>385</ymin>
24
+ <ymax>481</ymax>
25
+ </bndbox>
26
+ <polygon>
27
+ <x1>321.845046</x1>
28
+ <y1>480</y1>
29
+ <x2>324.32</x2>
30
+ <y2>478.94</y2>
31
+ <x3>327.6</x3>
32
+ <y3>474.21</y3>
33
+ <x4>329.41</x4>
34
+ <y4>472.21</y4>
35
+ <x5>326.5</x5>
36
+ <y5>467.85</y5>
37
+ <x6>326.5</x6>
38
+ <y6>460.94</y6>
39
+ <x7>326.32</x7>
40
+ <y7>453.85</y7>
41
+ <x8>325.23</x8>
42
+ <y8>450.76</y8>
43
+ <x9>325.05</x9>
44
+ <y9>446.03</y9>
45
+ <x10>326.14</x10>
46
+ <y10>440.58</y10>
47
+ <x11>330.32</x11>
48
+ <y11>442.4</y11>
49
+ <x12>335.41</x12>
50
+ <y12>442.58</y12>
51
+ <x13>340.87</x13>
52
+ <y13>441.49</y13>
53
+ <x14>342.5</x14>
54
+ <y14>434.76</y14>
55
+ <x15>346.14</x15>
56
+ <y15>429.12</y15>
57
+ <x16>346.14</x16>
58
+ <y16>425.67</y16>
59
+ <x17>345.78</x17>
60
+ <y17>423.49</y17>
61
+ <x18>340.69</x18>
62
+ <y18>419.49</y18>
63
+ <x19>336.5</x19>
64
+ <y19>416.76</y19>
65
+ <x20>333.05</x20>
66
+ <y20>414.94</y20>
67
+ <x21>329.78</x21>
68
+ <y21>414.21</y21>
69
+ <x22>324.87</x22>
70
+ <y22>413.49</y22>
71
+ <x23>320.87</x23>
72
+ <y23>411.67</y23>
73
+ <x24>319.41</x24>
74
+ <y24>408.21</y24>
75
+ <x25>317.05</x25>
76
+ <y25>404.58</y25>
77
+ <x26>312.32</x26>
78
+ <y26>400.76</y26>
79
+ <x27>307.96</x27>
80
+ <y27>396.76</y27>
81
+ <x28>303.41</x28>
82
+ <y28>392.58</y28>
83
+ <x29>298.87</x29>
84
+ <y29>386.4</y29>
85
+ <x30>293.41</x30>
86
+ <y30>384.22</y30>
87
+ <x31>283.41</x31>
88
+ <y31>393.31</y31>
89
+ <x32>290.69</x32>
90
+ <y32>400.03</y32>
91
+ <x33>292.51</x33>
92
+ <y33>401.67</y33>
93
+ <x34>294.69</x34>
94
+ <y34>406.58</y34>
95
+ <x35>296.14</x35>
96
+ <y35>411.12</y35>
97
+ <x36>297.96</x36>
98
+ <y36>412.94</y36>
99
+ <x37>297.6</x37>
100
+ <y37>417.85</y37>
101
+ <x38>298.32</x38>
102
+ <y38>422.21</y38>
103
+ <x39>298.87</x39>
104
+ <y39>424.21</y39>
105
+ <x40>292.69</x40>
106
+ <y40>428.94</y40>
107
+ <x41>288.69</x41>
108
+ <y41>428.76</y41>
109
+ <x42>285.6</x42>
110
+ <y42>435.85</y42>
111
+ <x43>283.6</x43>
112
+ <y43>438.58</y43>
113
+ <x44>278.51</x44>
114
+ <y44>435.3</y44>
115
+ <x45>274.32</x45>
116
+ <y45>430.4</y45>
117
+ <x46>260.87</x46>
118
+ <y46>417.49</y46>
119
+ <x47>251.05</x47>
120
+ <y47>426.58</y47>
121
+ <x48>255.96</x48>
122
+ <y48>432.21</y48>
123
+ <x49>259.42</x49>
124
+ <y49>436.21</y49>
125
+ <x50>259.96</x50>
126
+ <y50>439.3</y50>
127
+ <x51>267.05</x51>
128
+ <y51>446.76</y51>
129
+ <x52>271.78</x52>
130
+ <y52>450.03</y52>
131
+ <x53>272.69</x53>
132
+ <y53>453.12</y53>
133
+ <x54>275.78</x54>
134
+ <y54>456.76</y54>
135
+ <x55>279.05</x55>
136
+ <y55>457.67</y55>
137
+ <x56>280.69</x56>
138
+ <y56>458.03</y56>
139
+ <x57>280.69</x57>
140
+ <y57>460.94</y57>
141
+ <x58>278.51</x58>
142
+ <y58>463.67</y58>
143
+ <x59>275.96</x59>
144
+ <y59>463.49</y59>
145
+ <x60>286.87</x60>
146
+ <y60>475.85</y60>
147
+ <x61>292.87</x61>
148
+ <y61>478.03</y61>
149
+ <x62>296.51</x62>
150
+ <y62>478.76</y62>
151
+ <x63>299.05</x63>
152
+ <y63>478.76</y63>
153
+ <x64>303.6</x64>
154
+ <y64>477.3</y64>
155
+ <x65>305.41</x65>
156
+ <y65>475.67</y65>
157
+ <x66>310.14</x66>
158
+ <y66>477.85</y66>
159
+ <x67>316.116606</x67>
160
+ <y67>480</y67>
161
+ <x68>321.845046</x68>
162
+ <y68>480</y68>
163
+ </polygon>
164
+ </object>
165
+ <object>
166
+ <name>Short_circuit</name>
167
+ <pose>Unspecified</pose>
168
+ <truncated>0</truncated>
169
+ <difficult>0</difficult>
170
+ <occluded>0</occluded>
171
+ <bndbox>
172
+ <xmin>456</xmin>
173
+ <xmax>554</xmax>
174
+ <ymin>193</ymin>
175
+ <ymax>287</ymax>
176
+ </bndbox>
177
+ <polygon>
178
+ <x1>464.6</x1>
179
+ <y1>247.1</y1>
180
+ <x2>469.9</x2>
181
+ <y2>250.8</y2>
182
+ <x3>472.4</x3>
183
+ <y3>253.8</y3>
184
+ <x4>478.8</x4>
185
+ <y4>261.7</y4>
186
+ <x5>480.9</x5>
187
+ <y5>265</y5>
188
+ <x6>479.7</x6>
189
+ <y6>268.8</y6>
190
+ <x7>484.5</x7>
191
+ <y7>273.3</y7>
192
+ <x8>490.7</x8>
193
+ <y8>273.8</y8>
194
+ <x9>493.5</x9>
195
+ <y9>276.8</y9>
196
+ <x10>490.2</x10>
197
+ <y10>279</y10>
198
+ <x11>488.5</x11>
199
+ <y11>280.6</y11>
200
+ <x12>494</x12>
201
+ <y12>285.8</y12>
202
+ <x13>502.3</x13>
203
+ <y13>284.9</y13>
204
+ <x14>505.9</x14>
205
+ <y14>280.4</y14>
206
+ <x15>508.7</x15>
207
+ <y15>280.9</y15>
208
+ <x16>513.7</x16>
209
+ <y16>278</y16>
210
+ <x17>516</x17>
211
+ <y17>271.9</y17>
212
+ <x18>517.5</x18>
213
+ <y18>269.7</y18>
214
+ <x19>518.6</x19>
215
+ <y19>265</y19>
216
+ <x20>518.7</x20>
217
+ <y20>260.7</y20>
218
+ <x21>517.5</x21>
219
+ <y21>258.5</y21>
220
+ <x22>516.1</x22>
221
+ <y22>256.7</y22>
222
+ <x23>514.9</x23>
223
+ <y23>255.2</y23>
224
+ <x24>514.4</x24>
225
+ <y24>252.2</y24>
226
+ <x25>519.6</x25>
227
+ <y25>249.5</y25>
228
+ <x26>527.4</x26>
229
+ <y26>249.7</y26>
230
+ <x27>530.6</x27>
231
+ <y27>250.5</y27>
232
+ <x28>540.1</x28>
233
+ <y28>250.5</y28>
234
+ <x29>550</x29>
235
+ <y29>245.5</y29>
236
+ <x30>553.3</x30>
237
+ <y30>236.8</y30>
238
+ <x31>548.5</x31>
239
+ <y31>231.1</y31>
240
+ <x32>544.6</x32>
241
+ <y32>225.6</y32>
242
+ <x33>539.3</x33>
243
+ <y33>220.1</y33>
244
+ <x34>534.1</x34>
245
+ <y34>214.9</y34>
246
+ <x35>526.9</x35>
247
+ <y35>213.2</y35>
248
+ <x36>519.9</x36>
249
+ <y36>212.7</y36>
250
+ <x37>515.7</x37>
251
+ <y37>210.4</y37>
252
+ <x38>514.4</x38>
253
+ <y38>206.5</y38>
254
+ <x39>512.5</x39>
255
+ <y39>201.2</y39>
256
+ <x40>507</x40>
257
+ <y40>200.2</y40>
258
+ <x41>503</x41>
259
+ <y41>197.7</y41>
260
+ <x42>500</x42>
261
+ <y42>193.5</y42>
262
+ <x43>498.5</x43>
263
+ <y43>191.5</y43>
264
+ <x44>489.1</x44>
265
+ <y44>200.3</y44>
266
+ <x45>496.3</x45>
267
+ <y45>209</y45>
268
+ <x46>501.1</x46>
269
+ <y46>216.8</y46>
270
+ <x47>502.5</x47>
271
+ <y47>218.6</y47>
272
+ <x48>504.3</x48>
273
+ <y48>222.5</y48>
274
+ <x49>501.3</x49>
275
+ <y49>227.3</y49>
276
+ <x50>497.6</x50>
277
+ <y50>227.3</y50>
278
+ <x51>494.2</x51>
279
+ <y51>230.3</y51>
280
+ <x52>490.6</x52>
281
+ <y52>232.1</y52>
282
+ <x53>488.3</x53>
283
+ <y53>234</y53>
284
+ <x54>485.8</x54>
285
+ <y54>237.5</y54>
286
+ <x55>484</x55>
287
+ <y55>238.8</y55>
288
+ <x56>481.1</x56>
289
+ <y56>240.4</y56>
290
+ <x57>479</x57>
291
+ <y57>235.7</y57>
292
+ <x58>477.5</x58>
293
+ <y58>233</y58>
294
+ <x59>477.5</x59>
295
+ <y59>230</y59>
296
+ <x60>475.1</x60>
297
+ <y60>227.4</y60>
298
+ <x61>473.2</x61>
299
+ <y61>228.9</y61>
300
+ <x62>470.7</x62>
301
+ <y62>227.6</y62>
302
+ <x63>468.8</x63>
303
+ <y63>227.2</y63>
304
+ <x64>466.6</x64>
305
+ <y64>226.5</y64>
306
+ <x65>464.5</x65>
307
+ <y65>224.9</y65>
308
+ <x66>455</x66>
309
+ <y66>232.8</y66>
310
+ <x67>464.2</x67>
311
+ <y67>242.9</y67>
312
+ <x68>464.6</x68>
313
+ <y68>247.1</y68>
314
+ </polygon>
315
+ </object>
316
+ </annotation>
training/train/WIN_20221017_18_58_20_Pro_jpg.rf.16824b392786dec636781120a87fbd27.jpg ADDED