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surya_det3

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efficientvit
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  1. README.md +32 -6
  2. model.safetensors +1 -1
README.md CHANGED
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  ---
 
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  tags:
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  - generated_from_trainer
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  model-index:
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/vikp/line_detector/runs/cbhhi6vp)
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  # line_detector_3
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- This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1275
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:------:|:----:|:---------------:|
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- | 0.1203 | 2.2989 | 1000 | 0.1275 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
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+ base_model: vikp/line_detector_3
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  tags:
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  - generated_from_trainer
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  model-index:
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/vikp/line_detector/runs/5crpjf7u)
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  # line_detector_3
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+ This model is a fine-tuned version of [vikp/line_detector_3](https://huggingface.co/vikp/line_detector_3) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1230
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:-----:|:---------------:|
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+ | 0.1302 | 0.1527 | 1000 | 0.1327 |
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+ | 0.1147 | 0.3054 | 2000 | 0.1314 |
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+ | 0.1395 | 0.4581 | 3000 | 0.1318 |
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+ | 0.1302 | 0.6108 | 4000 | 0.1312 |
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+ | 0.1349 | 0.7635 | 5000 | 0.1315 |
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+ | 0.1431 | 0.9162 | 6000 | 0.1305 |
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+ | 0.1318 | 1.0689 | 7000 | 0.1305 |
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+ | 0.118 | 1.2216 | 8000 | 0.1295 |
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+ | 0.1116 | 1.3743 | 9000 | 0.1286 |
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+ | 0.1513 | 1.5270 | 10000 | 0.1273 |
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+ | 0.1158 | 1.6796 | 11000 | 0.1290 |
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+ | 0.1408 | 1.8323 | 12000 | 0.1289 |
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+ | 0.1227 | 1.9850 | 13000 | 0.1281 |
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+ | 0.1347 | 2.1377 | 14000 | 0.1291 |
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+ | 0.1066 | 2.2904 | 15000 | 0.1285 |
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+ | 0.116 | 2.4431 | 16000 | 0.1275 |
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+ | 0.1164 | 2.5958 | 17000 | 0.1253 |
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+ | 0.1269 | 2.7485 | 18000 | 0.1259 |
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+ | 0.1293 | 2.9012 | 19000 | 0.1256 |
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+ | 0.1241 | 3.0539 | 20000 | 0.1245 |
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+ | 0.1329 | 3.2066 | 21000 | 0.1263 |
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+ | 0.1166 | 3.3593 | 22000 | 0.1266 |
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+ | 0.1292 | 3.5120 | 23000 | 0.1230 |
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+ | 0.1189 | 3.6647 | 24000 | 0.1274 |
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+ | 0.1073 | 3.8174 | 25000 | 0.1251 |
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+ | 0.1308 | 3.9701 | 26000 | 0.1230 |
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  ### Framework versions
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