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detr-finetuned-cppe-5-10k-steps

Object Detection
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detr
vision
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  1. README.md +37 -31
  2. model.safetensors +1 -1
  3. runs/May08_17-33-25_PRLNB25003/events.out.tfevents.1746696821.PRLNB25003.21208.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -3,8 +3,6 @@ library_name: transformers
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  license: apache-2.0
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  base_model: facebook/detr-resnet-50
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  tags:
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- - object-detection
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- - vision
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  - generated_from_trainer
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  model-index:
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  - name: detr-finetuned-cppe-5-10k-steps
@@ -16,31 +14,31 @@ should probably proofread and complete it, then remove this comment. -->
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  # detr-finetuned-cppe-5-10k-steps
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- This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0867
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- - Map: 0.0493
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- - Map 50: 0.1064
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- - Map 75: 0.0379
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- - Map Small: 0.0072
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- - Map Medium: 0.0304
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- - Map Large: 0.0574
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- - Mar 1: 0.0805
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- - Mar 10: 0.1667
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- - Mar 100: 0.2069
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- - Mar Small: 0.073
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- - Mar Medium: 0.1572
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- - Mar Large: 0.2325
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- - Map Coverall: 0.1978
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- - Mar 100 Coverall: 0.5874
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- - Map Face Shield: 0.0
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- - Mar 100 Face Shield: 0.0
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- - Map Gloves: 0.0253
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- - Mar 100 Gloves: 0.204
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- - Map Goggles: 0.0
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- - Mar 100 Goggles: 0.0
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- - Map Mask: 0.0236
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- - Mar 100 Mask: 0.2431
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  ## Model description
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@@ -65,15 +63,23 @@ The following hyperparameters were used during training:
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  - seed: 1337
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 2.0
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
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- | 2.5499 | 1.0 | 213 | 2.3248 | 0.0367 | 0.0798 | 0.0325 | 0.0018 | 0.0114 | 0.0417 | 0.0555 | 0.1278 | 0.1648 | 0.0416 | 0.1044 | 0.2026 | 0.1697 | 0.5104 | 0.0 | 0.0 | 0.0078 | 0.1813 | 0.0 | 0.0 | 0.0058 | 0.1324 |
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- | 2.1119 | 2.0 | 426 | 2.0867 | 0.0493 | 0.1064 | 0.0379 | 0.0072 | 0.0304 | 0.0574 | 0.0805 | 0.1667 | 0.2069 | 0.073 | 0.1572 | 0.2325 | 0.1978 | 0.5874 | 0.0 | 0.0 | 0.0253 | 0.204 | 0.0 | 0.0 | 0.0236 | 0.2431 |
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: facebook/detr-resnet-50
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  tags:
 
 
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  - generated_from_trainer
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  model-index:
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  - name: detr-finetuned-cppe-5-10k-steps
 
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  # detr-finetuned-cppe-5-10k-steps
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+ This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.6238
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+ - Map: 0.1619
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+ - Map 50: 0.3226
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+ - Map 75: 0.1429
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+ - Map Small: 0.0501
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+ - Map Medium: 0.129
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+ - Map Large: 0.227
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+ - Mar 1: 0.1773
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+ - Mar 10: 0.3167
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+ - Mar 100: 0.3392
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+ - Mar Small: 0.128
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+ - Mar Medium: 0.2626
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+ - Mar Large: 0.4711
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+ - Map Coverall: 0.4278
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+ - Mar 100 Coverall: 0.6532
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+ - Map Face Shield: 0.1078
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+ - Mar 100 Face Shield: 0.2937
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+ - Map Gloves: 0.0679
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+ - Mar 100 Gloves: 0.2991
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+ - Map Goggles: 0.0102
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+ - Mar 100 Goggles: 0.0985
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+ - Map Mask: 0.1959
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+ - Mar 100 Mask: 0.3516
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  ## Model description
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  - seed: 1337
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 10.0
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Coverall | Map Face Shield | Map Gloves | Map Goggles | Map Mask | Map Large | Map Medium | Map Small | Mar 1 | Mar 10 | Mar 100 | Mar 100 Coverall | Mar 100 Face Shield | Mar 100 Gloves | Mar 100 Goggles | Mar 100 Mask | Mar Large | Mar Medium | Mar Small |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------------:|:---------------:|:----------:|:-----------:|:--------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:----------------:|:-------------------:|:--------------:|:---------------:|:------------:|:---------:|:----------:|:---------:|
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+ | 2.5499 | 1.0 | 213 | 2.3248 | 0.0367 | 0.0798 | 0.0325 | 0.1697 | 0.0 | 0.0078 | 0.0 | 0.0058 | 0.0417 | 0.0114 | 0.0018 | 0.0555 | 0.1278 | 0.1648 | 0.5104 | 0.0 | 0.1813 | 0.0 | 0.1324 | 0.2026 | 0.1044 | 0.0416 |
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+ | 2.1119 | 2.0 | 426 | 2.0867 | 0.0493 | 0.1064 | 0.0379 | 0.1978 | 0.0 | 0.0253 | 0.0 | 0.0236 | 0.0574 | 0.0304 | 0.0072 | 0.0805 | 0.1667 | 0.2069 | 0.5874 | 0.0 | 0.204 | 0.0 | 0.2431 | 0.2325 | 0.1572 | 0.073 |
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+ | 2.0052 | 3.0 | 639 | 2.1689 | 0.0563 | 0.1279 | 0.0441 | 0.0154 | 0.0414 | 0.0688 | 0.0818 | 0.1611 | 0.1793 | 0.0616 | 0.131 | 0.2199 | 0.2039 | 0.4757 | 0.0 | 0.0 | 0.0158 | 0.1942 | 0.0 | 0.0 | 0.0618 | 0.2267 |
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+ | 1.9373 | 4.0 | 852 | 1.9264 | 0.0813 | 0.1755 | 0.0679 | 0.0125 | 0.056 | 0.0953 | 0.0916 | 0.1816 | 0.206 | 0.0662 | 0.1446 | 0.2549 | 0.3464 | 0.6302 | 0.0 | 0.0 | 0.0245 | 0.2004 | 0.0 | 0.0 | 0.0357 | 0.1996 |
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+ | 1.8396 | 5.0 | 1065 | 1.8418 | 0.0958 | 0.208 | 0.0731 | 0.0194 | 0.0751 | 0.1163 | 0.116 | 0.2195 | 0.2399 | 0.095 | 0.1802 | 0.2939 | 0.3278 | 0.6054 | 0.0228 | 0.1127 | 0.031 | 0.2071 | 0.0 | 0.0 | 0.0975 | 0.2742 |
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+ | 1.7659 | 6.0 | 1278 | 1.8737 | 0.1004 | 0.2399 | 0.0791 | 0.0377 | 0.0913 | 0.1207 | 0.1226 | 0.2204 | 0.2382 | 0.0808 | 0.1912 | 0.295 | 0.2906 | 0.5856 | 0.0439 | 0.1165 | 0.0497 | 0.2281 | 0.0 | 0.0 | 0.1178 | 0.2609 |
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+ | 1.6415 | 7.0 | 1491 | 1.7200 | 0.1305 | 0.2822 | 0.105 | 0.044 | 0.1026 | 0.172 | 0.1505 | 0.2689 | 0.2891 | 0.1047 | 0.2239 | 0.386 | 0.3916 | 0.6257 | 0.0516 | 0.1962 | 0.0526 | 0.2554 | 0.0059 | 0.0523 | 0.1508 | 0.316 |
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+ | 1.6405 | 8.0 | 1704 | 1.6820 | 0.1411 | 0.2866 | 0.1303 | 0.0542 | 0.1162 | 0.1854 | 0.1572 | 0.2867 | 0.3088 | 0.1204 | 0.2432 | 0.4042 | 0.4147 | 0.6347 | 0.0549 | 0.2468 | 0.0592 | 0.271 | 0.0053 | 0.0631 | 0.1713 | 0.3284 |
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+ | 1.5513 | 9.0 | 1917 | 1.6380 | 0.1546 | 0.3144 | 0.1307 | 0.0569 | 0.1215 | 0.2131 | 0.17 | 0.3014 | 0.323 | 0.112 | 0.2563 | 0.4431 | 0.4352 | 0.6414 | 0.0804 | 0.2544 | 0.0657 | 0.2871 | 0.0062 | 0.0846 | 0.1853 | 0.3476 |
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+ | 1.5564 | 10.0 | 2130 | 1.6238 | 0.1619 | 0.3226 | 0.1429 | 0.0501 | 0.129 | 0.227 | 0.1773 | 0.3167 | 0.3392 | 0.128 | 0.2626 | 0.4711 | 0.4278 | 0.6532 | 0.1078 | 0.2937 | 0.0679 | 0.2991 | 0.0102 | 0.0985 | 0.1959 | 0.3516 |
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  ### Framework versions
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