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detr_finetuned_cppe5

Object Detection
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Safetensors
conditional_detr
Generated from Trainer
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  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -16,26 +16,6 @@ should probably proofread and complete it, then remove this comment. -->
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  # detr_finetuned_cppe5
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  This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the generator dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 2.1706
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- - Map: 0.0035
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- - Map 50: 0.0062
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- - Map 75: 0.003
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- - Map Small: -1.0
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- - Map Medium: -1.0
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- - Map Large: 0.0035
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- - Mar 1: 0.0
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- - Mar 10: 0.0061
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- - Mar 100: 0.1086
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- - Mar Small: -1.0
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- - Mar Medium: -1.0
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- - Mar Large: 0.1086
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- - Map Speaker: 0.0
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- - Mar 100 Speaker: 0.0
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- - Map Participant: 0.0102
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- - Mar 100 Participant: 0.2424
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- - Map Shared screen: 0.0002
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- - Mar 100 Shared screen: 0.0833
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  ## Model description
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@@ -54,48 +34,16 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 32
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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- - num_epochs: 30
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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 Speaker | Mar 100 Speaker | Map Participant | Mar 100 Participant | Map Shared screen | Mar 100 Shared screen |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|
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- | No log | 1.0 | 1 | 44.6941 | 0.0099 | 0.026 | 0.0075 | -1.0 | -1.0 | 0.0177 | 0.0 | 0.0414 | 0.196 | -1.0 | -1.0 | 0.196 | 0.0 | 0.0 | 0.0297 | 0.5879 | 0.0 | 0.0 |
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- | No log | 2.0 | 2 | 35.1522 | 0.009 | 0.0265 | 0.0024 | -1.0 | -1.0 | 0.0168 | 0.0121 | 0.0404 | 0.1293 | -1.0 | -1.0 | 0.1293 | 0.0 | 0.0 | 0.0271 | 0.3879 | 0.0 | 0.0 |
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- | No log | 3.0 | 3 | 27.4243 | 0.012 | 0.038 | 0.0028 | -1.0 | -1.0 | 0.0172 | 0.0172 | 0.0414 | 0.1222 | -1.0 | -1.0 | 0.1222 | 0.0 | 0.0 | 0.0361 | 0.3667 | 0.0 | 0.0 |
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- | No log | 4.0 | 4 | 21.9395 | 0.0203 | 0.064 | 0.0098 | -1.0 | -1.0 | 0.027 | 0.0202 | 0.1155 | 0.1831 | -1.0 | -1.0 | 0.1831 | 0.0076 | 0.21 | 0.0532 | 0.3394 | 0.0 | 0.0 |
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- | No log | 5.0 | 5 | 17.6182 | 0.0289 | 0.0697 | 0.0142 | -1.0 | -1.0 | 0.0382 | 0.0232 | 0.1008 | 0.2852 | -1.0 | -1.0 | 0.2852 | 0.0202 | 0.51 | 0.0665 | 0.3455 | 0.0 | 0.0 |
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- | No log | 6.0 | 6 | 13.8983 | 0.0262 | 0.0684 | 0.007 | -1.0 | -1.0 | 0.0361 | 0.0222 | 0.1244 | 0.249 | -1.0 | -1.0 | 0.249 | 0.0149 | 0.45 | 0.0637 | 0.297 | 0.0 | 0.0 |
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- | No log | 7.0 | 7 | 11.0089 | 0.0106 | 0.0358 | 0.001 | -1.0 | -1.0 | 0.0192 | 0.0131 | 0.0424 | 0.1195 | -1.0 | -1.0 | 0.1195 | 0.0011 | 0.11 | 0.0308 | 0.2485 | 0.0 | 0.0 |
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- | No log | 8.0 | 8 | 8.9516 | 0.0026 | 0.0101 | 0.0001 | -1.0 | -1.0 | 0.0035 | 0.0 | 0.0101 | 0.0854 | -1.0 | -1.0 | 0.0854 | 0.0003 | 0.05 | 0.0076 | 0.2061 | 0.0 | 0.0 |
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- | No log | 9.0 | 9 | 7.4728 | 0.0009 | 0.0048 | 0.0 | -1.0 | -1.0 | 0.0011 | 0.0 | 0.0 | 0.0564 | -1.0 | -1.0 | 0.0564 | 0.0003 | 0.06 | 0.0025 | 0.1091 | 0.0 | 0.0 |
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- | No log | 10.0 | 10 | 6.2987 | 0.0005 | 0.0029 | 0.0 | -1.0 | -1.0 | 0.0005 | 0.0 | 0.0 | 0.0232 | -1.0 | -1.0 | 0.0232 | 0.0 | 0.0 | 0.0014 | 0.0697 | 0.0 | 0.0 |
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- | No log | 11.0 | 11 | 5.3824 | 0.0001 | 0.0006 | 0.0 | -1.0 | -1.0 | 0.0001 | 0.0 | 0.0 | 0.0101 | -1.0 | -1.0 | 0.0101 | 0.0 | 0.0 | 0.0003 | 0.0303 | 0.0 | 0.0 |
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- | No log | 12.0 | 12 | 4.7139 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | No log | 13.0 | 13 | 4.1528 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | No log | 14.0 | 14 | 3.7346 | 0.0 | 0.0002 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.001 | -1.0 | -1.0 | 0.001 | 0.0 | 0.0 | 0.0 | 0.003 | 0.0 | 0.0 |
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- | No log | 15.0 | 15 | 3.4881 | 0.0 | 0.0003 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.002 | -1.0 | -1.0 | 0.002 | 0.0 | 0.0 | 0.0001 | 0.0061 | 0.0 | 0.0 |
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- | No log | 16.0 | 16 | 3.2032 | 0.0 | 0.0004 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.003 | -1.0 | -1.0 | 0.003 | 0.0 | 0.0 | 0.0001 | 0.0091 | 0.0 | 0.0 |
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- | No log | 17.0 | 17 | 2.9857 | 0.0003 | 0.0009 | 0.0 | -1.0 | -1.0 | 0.0003 | 0.0 | 0.0 | 0.0162 | -1.0 | -1.0 | 0.0162 | 0.0 | 0.0 | 0.0008 | 0.0485 | 0.0 | 0.0 |
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- | No log | 18.0 | 18 | 2.8504 | 0.0004 | 0.0011 | 0.0001 | -1.0 | -1.0 | 0.0004 | 0.0 | 0.0 | 0.0263 | -1.0 | -1.0 | 0.0263 | 0.0 | 0.0 | 0.0011 | 0.0788 | 0.0 | 0.0 |
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- | No log | 19.0 | 19 | 2.7154 | 0.0002 | 0.0006 | 0.0002 | -1.0 | -1.0 | 0.0002 | 0.0 | 0.0 | 0.0192 | -1.0 | -1.0 | 0.0192 | 0.0 | 0.0 | 0.0007 | 0.0576 | 0.0 | 0.0 |
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- | No log | 20.0 | 20 | 2.5824 | 0.0005 | 0.0015 | 0.0003 | -1.0 | -1.0 | 0.0005 | 0.0 | 0.0 | 0.0348 | -1.0 | -1.0 | 0.0348 | 0.0 | 0.0 | 0.0013 | 0.0879 | 0.0 | 0.0167 |
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- | No log | 21.0 | 21 | 2.4716 | 0.0011 | 0.0019 | 0.001 | -1.0 | -1.0 | 0.0011 | 0.0 | 0.0 | 0.0485 | -1.0 | -1.0 | 0.0485 | 0.0 | 0.0 | 0.0032 | 0.1455 | 0.0 | 0.0 |
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- | No log | 22.0 | 22 | 2.3747 | 0.0017 | 0.0028 | 0.0016 | -1.0 | -1.0 | 0.0018 | 0.0 | 0.0 | 0.0707 | -1.0 | -1.0 | 0.0707 | 0.0 | 0.0 | 0.0051 | 0.1788 | 0.0001 | 0.0333 |
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- | No log | 23.0 | 23 | 2.3044 | 0.0021 | 0.0034 | 0.0018 | -1.0 | -1.0 | 0.0021 | 0.0 | 0.0 | 0.0884 | -1.0 | -1.0 | 0.0884 | 0.0 | 0.0 | 0.0061 | 0.1818 | 0.0002 | 0.0833 |
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- | No log | 24.0 | 24 | 2.2643 | 0.0027 | 0.0047 | 0.002 | -1.0 | -1.0 | 0.0027 | 0.0 | 0.0 | 0.0955 | -1.0 | -1.0 | 0.0955 | 0.0 | 0.0 | 0.0079 | 0.203 | 0.0002 | 0.0833 |
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- | No log | 25.0 | 25 | 2.2301 | 0.0028 | 0.0049 | 0.0023 | -1.0 | -1.0 | 0.0028 | 0.0 | 0.004 | 0.0975 | -1.0 | -1.0 | 0.0975 | 0.0 | 0.0 | 0.0083 | 0.2091 | 0.0002 | 0.0833 |
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- | No log | 26.0 | 26 | 2.2047 | 0.0029 | 0.005 | 0.0026 | -1.0 | -1.0 | 0.0029 | 0.0 | 0.0061 | 0.0975 | -1.0 | -1.0 | 0.0975 | 0.0 | 0.0 | 0.0084 | 0.2091 | 0.0002 | 0.0833 |
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- | No log | 27.0 | 27 | 2.1859 | 0.0032 | 0.0055 | 0.0029 | -1.0 | -1.0 | 0.0032 | 0.0 | 0.0061 | 0.1035 | -1.0 | -1.0 | 0.1035 | 0.0 | 0.0 | 0.0093 | 0.2273 | 0.0002 | 0.0833 |
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- | No log | 28.0 | 28 | 2.1757 | 0.0032 | 0.0056 | 0.0029 | -1.0 | -1.0 | 0.0032 | 0.0 | 0.0061 | 0.1045 | -1.0 | -1.0 | 0.1045 | 0.0 | 0.0 | 0.0093 | 0.2303 | 0.0002 | 0.0833 |
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- | No log | 29.0 | 29 | 2.1716 | 0.0035 | 0.0062 | 0.003 | -1.0 | -1.0 | 0.0035 | 0.0 | 0.0061 | 0.1086 | -1.0 | -1.0 | 0.1086 | 0.0 | 0.0 | 0.0102 | 0.2424 | 0.0002 | 0.0833 |
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- | No log | 30.0 | 30 | 2.1706 | 0.0035 | 0.0062 | 0.003 | -1.0 | -1.0 | 0.0035 | 0.0 | 0.0061 | 0.1086 | -1.0 | -1.0 | 0.1086 | 0.0 | 0.0 | 0.0102 | 0.2424 | 0.0002 | 0.0833 |
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  ### Framework versions
 
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  # detr_finetuned_cppe5
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  This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the generator dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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+ - train_batch_size: 20
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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+ - num_epochs: 5
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  ### Training results
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  ### Framework versions
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