Spaces:
Sleeping
Sleeping
Commit
•
4e707ae
1
Parent(s):
b0820c1
creation of the space
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- 0defect_segmentation.ipynb +0 -0
- README.md +37 -1
- app06.py +38 -0
- model/config.json +82 -0
- model/preprocessor_config.json +23 -0
- model/pytorch_model.bin +3 -0
- requirements.txt +6 -0
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0defect_segmentation.ipynb
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README.md
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colorTo: yellow
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sdk: streamlit
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sdk_version: 1.27.2
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-
app_file:
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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colorTo: yellow
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sdk: streamlit
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sdk_version: 1.27.2
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app_file: app06.py
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pinned: false
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license: mit
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---
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# 🛠️ PCB Defect Detection App
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This app allows users to upload PCB images and detect defects using state-of-the-art machine learning models.
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## 🌟 Features
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- **Image Upload**: Easily upload your PCB images and get instant defect predictions.
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- **Visualization**: Visualize the detected defects on the PCB image.
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- **Defect Types**: The app can identify multiple types of defects and highlight them uniquely for easy identification.
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## 🚀 Usage
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### 1️⃣ Uploading an Image:
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- Click on the "Browse files" button.
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- Select a PCB image from your device.
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- Sit back and relax! Let the model churn through the image and present its findings.
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### 2️⃣ Interpreting Results:
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- It will display the original image alongside the predicted defect mask.
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- Different defect types will be highlighted using unique grayscale values.
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## Model Details
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The app utilzes the Segformer model trained on a custom PCB dataset. The model has been fine-tuned to detect:
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- **Incorrect Installation**
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- **Short Circuit**
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- **Dry Joints**
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... commonly found defects in PCBs.
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## 📜 Requirements
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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.
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app06.py
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import streamlit as st
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from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
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from PIL import Image
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import numpy as np
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import torch
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# Load the model and processor
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model_dir = "C://Users//nazli//OneDrive//Desktop//PCB-Defect-Detection-Segmentation//model11//model//"
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model = SegformerForSemanticSegmentation.from_pretrained(model_dir)
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processor = SegformerImageProcessor.from_pretrained(model_dir)
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model.eval()
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st.title("PCB Defect Detection")
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# Upload image in Streamlit
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uploaded_file = st.file_uploader("Upload a PCB image", type=["jpg", "png"])
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if uploaded_file:
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# Preprocess the image
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test_image = Image.open(uploaded_file).convert("RGB")
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inputs = processor(images=test_image, return_tensors="pt")
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# Model inference
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with torch.no_grad():
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outputs = model(**inputs)
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# Post-process
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semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[test_image.size[::-1]])[0]
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semantic_map = np.uint8(semantic_map)
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semantic_map[semantic_map==1] = 255
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semantic_map[semantic_map==2] = 195
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semantic_map[semantic_map==3] = 135
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semantic_map[semantic_map==4] = 75
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# Display the results
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st.image(test_image, caption="Uploaded Image", use_column_width=True)
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st.image(semantic_map, caption="Predicted Defects", use_column_width=True, channels="GRAY")
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model/config.json
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{
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"_name_or_path": "segformer-b0-finetuned-pcb-outputs/checkpoint-16500",
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"architectures": [
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"SegformerForSemanticSegmentation"
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],
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"attention_probs_dropout_prob": 0.0,
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"classifier_dropout_prob": 0.1,
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"decoder_hidden_size": 256,
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"depths": [
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2,
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2,
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2
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],
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"downsampling_rates": [
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1,
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4,
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8,
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16
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],
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"drop_path_rate": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_sizes": [
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32,
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64,
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160,
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256
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],
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"id2label": {
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"0": "background",
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"1": "Short_circuit",
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"2": "Dry_joint",
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"3": "Incorrect_installation"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"label2id": {
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"Dry_joint": 2,
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"Incorrect_installation": 3,
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"Short_circuit": 1,
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"background": 0
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},
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"layer_norm_eps": 1e-06,
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"mlp_ratios": [
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4,
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4,
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4,
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4
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],
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"model_type": "segformer",
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"num_attention_heads": [
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1,
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2,
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5,
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8
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],
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"num_channels": 3,
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"num_encoder_blocks": 4,
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"patch_sizes": [
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7,
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3,
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3,
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3
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],
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"reshape_last_stage": true,
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"semantic_loss_ignore_index": 255,
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"sr_ratios": [
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8,
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4,
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2,
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1
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],
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"strides": [
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2
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],
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"torch_dtype": "float32",
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"transformers_version": "4.34.0"
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}
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model/preprocessor_config.json
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{
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"do_normalize": true,
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"do_reduce_labels": false,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "SegformerImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 512,
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"width": 512
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}
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}
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model/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:caa39910006f4344fe9f3ee950bfcfb8de2b2090a3fcd59354832e9c4b0aee36
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size 14930957
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requirements.txt
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numpy==1.26.0
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Pillow==9.5.0
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Pillow==10.0.1
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streamlit==1.24.1
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torch==2.0.1
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transformers==4.30.2
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training/train/WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg
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![]() |
training/train/WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.xml
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<annotation>
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<folder></folder>
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<filename>WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg</filename>
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<path>WIN_20221017_18_46_59_Pro_jpg.rf.78cb5715edb922dd6140afc84d64d8c5.jpg</path>
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<source>
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<database>roboflow.ai</database>
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</source>
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<size>
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<width>640</width>
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<height>480</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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</annotation>
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training/train/WIN_20221017_18_46_59_Pro_jpg.rf.7f17d30a83c990fa68a33e983c814709.jpg
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training/train/WIN_20221017_18_56_15_Pro_jpg.rf.a928b7c33ce8e03eb54e66c4e8a4de67.jpg
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<x6>359</x6>
|
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<y6>300.3</y6>
|
39 |
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<x7>356.5</x7>
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40 |
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<y7>300.6</y7>
|
41 |
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<x8>354.6</x8>
|
42 |
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<x9>351</x9>
|
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<y9>299.9</y9>
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48 |
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52 |
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53 |
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<x14>341.2</x14>
|
54 |
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<y14>310.5</y14>
|
55 |
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<x15>337.7</x15>
|
56 |
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<y15>314.8</y15>
|
57 |
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<x16>333.7</x16>
|
58 |
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<y16>320.6</y16>
|
59 |
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<x17>331.3</x17>
|
60 |
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<y17>327.9</y17>
|
61 |
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<x18>329.5</x18>
|
62 |
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<y18>335.8</y18>
|
63 |
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<x19>329.9</x19>
|
64 |
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<y19>344</y19>
|
65 |
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<x20>332</x20>
|
66 |
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<y20>350.6</y20>
|
67 |
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<x21>334.3</x21>
|
68 |
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<y21>355.3</y21>
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<x22>338.7</x22>
|
70 |
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<y22>360.7</y22>
|
71 |
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<x23>342.6</x23>
|
72 |
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<y23>365.6</y23>
|
73 |
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<x24>351</x24>
|
74 |
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<y24>370.1</y24>
|
75 |
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<x25>357.6</x25>
|
76 |
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<y25>371.8</y25>
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77 |
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|
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<y26>372.5</y26>
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79 |
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<x27>367.6</x27>
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80 |
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<y27>372.5</y27>
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81 |
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<y28>372.2</y28>
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<x29>376.1</x29>
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86 |
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95 |
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<y37>340.5</y37>
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101 |
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<x38>402.5</x38>
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102 |
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<y38>331.8</y38>
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<x39>399.5</x39>
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training/train/WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.jpg
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1 |
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<annotation>
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<path>WIN_20221017_18_57_55_Pro_jpg.rf.3f5b19bce389e97321f23c59e8d416d9.jpg</path>
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training/train/WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg
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![]() |
training/train/WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.xml
ADDED
@@ -0,0 +1,119 @@
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1 |
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<annotation>
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<filename>WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg</filename>
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<path>WIN_20221017_18_57_55_Pro_jpg.rf.cb158107d8ae6d05bd1b6276b42aec2e.jpg</path>
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5 |
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<source>
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6 |
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<database>roboflow.ai</database>
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7 |
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</source>
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8 |
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<size>
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9 |
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10 |
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11 |
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<depth>3</depth>
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12 |
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</size>
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<segmented>0</segmented>
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<object>
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<name>Short_circuit</name>
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16 |
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<pose>Unspecified</pose>
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17 |
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18 |
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55 |
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<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>
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113 |
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114 |
+
<y44>435.3</y44>
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115 |
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116 |
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117 |
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118 |
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<y46>417.49</y46>
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119 |
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120 |
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<y47>426.58</y47>
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121 |
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122 |
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123 |
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124 |
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125 |
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126 |
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127 |
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128 |
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129 |
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130 |
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131 |
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132 |
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133 |
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134 |
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<y54>456.76</y54>
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135 |
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136 |
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137 |
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138 |
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139 |
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140 |
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141 |
+
<x58>278.51</x58>
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142 |
+
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143 |
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144 |
+
<y59>463.49</y59>
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145 |
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146 |
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147 |
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148 |
+
<y61>478.03</y61>
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149 |
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150 |
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<y62>478.76</y62>
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151 |
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152 |
+
<y63>478.76</y63>
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153 |
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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 |
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162 |
+
<y68>480</y68>
|
163 |
+
</polygon>
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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>
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171 |
+
<bndbox>
|
172 |
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<xmin>456</xmin>
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173 |
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174 |
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<ymin>193</ymin>
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175 |
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<ymax>287</ymax>
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176 |
+
</bndbox>
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177 |
+
<polygon>
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178 |
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<x1>464.6</x1>
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180 |
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181 |
+
<y2>250.8</y2>
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182 |
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183 |
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<y3>253.8</y3>
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184 |
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185 |
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186 |
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188 |
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189 |
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191 |
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192 |
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193 |
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<y8>273.8</y8>
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194 |
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<x9>493.5</x9>
|
195 |
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<y9>276.8</y9>
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196 |
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<x10>490.2</x10>
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197 |
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<y10>279</y10>
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198 |
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199 |
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200 |
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201 |
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202 |
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203 |
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204 |
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<x14>505.9</x14>
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205 |
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206 |
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207 |
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<y15>280.9</y15>
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208 |
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<x16>513.7</x16>
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209 |
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<y16>278</y16>
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210 |
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211 |
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<y17>271.9</y17>
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212 |
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213 |
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<y18>269.7</y18>
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214 |
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<x19>518.6</x19>
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215 |
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<y19>265</y19>
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216 |
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<x20>518.7</x20>
|
217 |
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<y20>260.7</y20>
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218 |
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<x21>517.5</x21>
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219 |
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<y21>258.5</y21>
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220 |
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221 |
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<y22>256.7</y22>
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222 |
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<x23>514.9</x23>
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223 |
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<y23>255.2</y23>
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224 |
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<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 |
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<y27>250.5</y27>
|
232 |
+
<x28>540.1</x28>
|
233 |
+
<y28>250.5</y28>
|
234 |
+
<x29>550</x29>
|
235 |
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<y29>245.5</y29>
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236 |
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<x30>553.3</x30>
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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 |
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<x46>501.1</x46>
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269 |
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<y46>216.8</y46>
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270 |
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<x47>502.5</x47>
|
271 |
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<y47>218.6</y47>
|
272 |
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<x48>504.3</x48>
|
273 |
+
<y48>222.5</y48>
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274 |
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<x49>501.3</x49>
|
275 |
+
<y49>227.3</y49>
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276 |
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<x50>497.6</x50>
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277 |
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<y50>227.3</y50>
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278 |
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<x51>494.2</x51>
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279 |
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<y51>230.3</y51>
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280 |
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<x52>490.6</x52>
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281 |
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<y52>232.1</y52>
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282 |
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<x53>488.3</x53>
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283 |
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<y53>234</y53>
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284 |
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<x54>485.8</x54>
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285 |
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286 |
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287 |
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288 |
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289 |
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<y56>240.4</y56>
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290 |
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<x57>479</x57>
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291 |
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<y57>235.7</y57>
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292 |
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293 |
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<y58>233</y58>
|
294 |
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<x59>477.5</x59>
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295 |
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<y59>230</y59>
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296 |
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297 |
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298 |
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<x61>473.2</x61>
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299 |
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300 |
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301 |
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303 |
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304 |
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305 |
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306 |
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307 |
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308 |
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309 |
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<y66>232.8</y66>
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310 |
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311 |
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<y67>242.9</y67>
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312 |
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<x68>464.6</x68>
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313 |
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314 |
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</annotation>
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training/train/WIN_20221017_18_58_20_Pro_jpg.rf.16824b392786dec636781120a87fbd27.jpg
ADDED
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