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detr_finetuned_cppe5

Object Detection
Transformers
Safetensors
conditional_detr
Generated from Trainer
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/conditional-detr-resnet-50
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - generator
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+ model-index:
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+ - name: detr_finetuned_cppe5
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+ results: []
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+ ---
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+
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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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+
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+ # detr_finetuned_cppe5
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+
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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: 1.3087
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+ - Map: 0.0626
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+ - Map 50: 0.0937
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+ - Map 75: 0.0706
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+ - Map Small: -1.0
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+ - Map Medium: -1.0
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+ - Map Large: 0.0626
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+ - Mar 1: 0.0141
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+ - Mar 10: 0.3738
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+ - Mar 100: 0.5364
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+ - Mar Small: -1.0
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+ - Mar Medium: -1.0
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+ - Mar Large: 0.5364
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+ - Map Speaker: 0.0547
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+ - Mar 100 Speaker: 0.7
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+ - Map Participant: 0.1332
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+ - Mar 100 Participant: 0.9091
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+ - Map Shared screen: 0.0
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+ - Mar 100 Shared screen: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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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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+
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+ ### Training results
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+
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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 | 2 | 31.1862 | 0.0273 | 0.0467 | 0.0295 | -1.0 | -1.0 | 0.0842 | 0.0 | 0.2489 | 0.3789 | -1.0 | -1.0 | 0.3789 | 0.0628 | 0.72 | 0.0 | 0.0 | 0.0191 | 0.4167 |
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+ | No log | 2.0 | 4 | 20.7260 | 0.0176 | 0.0322 | 0.0253 | -1.0 | -1.0 | 0.0236 | 0.02 | 0.0711 | 0.3156 | -1.0 | -1.0 | 0.3156 | 0.0496 | 0.73 | 0.0 | 0.0 | 0.0031 | 0.2167 |
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+ | No log | 3.0 | 6 | 14.4492 | 0.0386 | 0.061 | 0.0417 | -1.0 | -1.0 | 0.0399 | 0.0633 | 0.1067 | 0.18 | -1.0 | -1.0 | 0.18 | 0.1158 | 0.54 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 4.0 | 8 | 10.3554 | 0.0051 | 0.0146 | 0.0035 | -1.0 | -1.0 | 0.0062 | 0.0067 | 0.1 | 0.1 | -1.0 | -1.0 | 0.1 | 0.0153 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 5.0 | 10 | 7.0787 | 0.0074 | 0.0161 | 0.0033 | -1.0 | -1.0 | 0.008 | 0.0267 | 0.0467 | 0.1 | -1.0 | -1.0 | 0.1 | 0.0222 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 6.0 | 12 | 5.0232 | 0.0026 | 0.0051 | 0.0029 | -1.0 | -1.0 | 0.0031 | 0.0 | 0.0333 | 0.0939 | -1.0 | -1.0 | 0.0939 | 0.0076 | 0.25 | 0.0001 | 0.0152 | 0.0 | 0.0167 |
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+ | No log | 7.0 | 14 | 3.7366 | 0.0078 | 0.0148 | 0.0068 | -1.0 | -1.0 | 0.0082 | 0.0 | 0.1 | 0.1837 | -1.0 | -1.0 | 0.1837 | 0.0232 | 0.53 | 0.0003 | 0.0212 | 0.0 | 0.0 |
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+ | No log | 8.0 | 16 | 2.9397 | 0.0145 | 0.0376 | 0.0086 | -1.0 | -1.0 | 0.0148 | 0.0067 | 0.11 | 0.2232 | -1.0 | -1.0 | 0.2232 | 0.0417 | 0.6 | 0.0017 | 0.0697 | 0.0 | 0.0 |
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+ | No log | 9.0 | 18 | 2.4436 | 0.0308 | 0.0647 | 0.0191 | -1.0 | -1.0 | 0.0312 | 0.0133 | 0.1664 | 0.2717 | -1.0 | -1.0 | 0.2717 | 0.0884 | 0.7 | 0.004 | 0.1152 | 0.0 | 0.0 |
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+ | No log | 10.0 | 20 | 2.0818 | 0.035 | 0.0609 | 0.0358 | -1.0 | -1.0 | 0.0357 | 0.0 | 0.1714 | 0.3115 | -1.0 | -1.0 | 0.3115 | 0.0972 | 0.73 | 0.0077 | 0.1879 | 0.0001 | 0.0167 |
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+ | No log | 11.0 | 22 | 1.8221 | 0.026 | 0.0659 | 0.0214 | -1.0 | -1.0 | 0.0273 | 0.0174 | 0.1784 | 0.3839 | -1.0 | -1.0 | 0.3839 | 0.0575 | 0.77 | 0.0204 | 0.3818 | 0.0 | 0.0 |
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+ | No log | 12.0 | 24 | 1.6453 | 0.0261 | 0.0604 | 0.0251 | -1.0 | -1.0 | 0.0264 | 0.02 | 0.1394 | 0.4378 | -1.0 | -1.0 | 0.4378 | 0.047 | 0.78 | 0.0312 | 0.5333 | 0.0 | 0.0 |
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+ | No log | 13.0 | 26 | 1.6054 | 0.0347 | 0.0668 | 0.0319 | -1.0 | -1.0 | 0.0351 | 0.03 | 0.2402 | 0.4834 | -1.0 | -1.0 | 0.4834 | 0.0613 | 0.82 | 0.0429 | 0.6303 | 0.0 | 0.0 |
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+ | No log | 14.0 | 28 | 1.6165 | 0.0416 | 0.0732 | 0.0457 | -1.0 | -1.0 | 0.0418 | 0.0 | 0.2238 | 0.5108 | -1.0 | -1.0 | 0.5108 | 0.0573 | 0.79 | 0.0675 | 0.7424 | 0.0 | 0.0 |
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+ | No log | 15.0 | 30 | 1.5084 | 0.0362 | 0.0643 | 0.0393 | -1.0 | -1.0 | 0.0363 | 0.0 | 0.2356 | 0.4583 | -1.0 | -1.0 | 0.4583 | 0.0392 | 0.59 | 0.0694 | 0.7848 | 0.0 | 0.0 |
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+ | No log | 16.0 | 32 | 1.4460 | 0.0313 | 0.0562 | 0.0295 | -1.0 | -1.0 | 0.0313 | 0.002 | 0.2026 | 0.4143 | -1.0 | -1.0 | 0.4143 | 0.0257 | 0.44 | 0.0682 | 0.803 | 0.0 | 0.0 |
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+ | No log | 17.0 | 34 | 1.4822 | 0.0384 | 0.0871 | 0.0303 | -1.0 | -1.0 | 0.0384 | 0.0444 | 0.1459 | 0.3757 | -1.0 | -1.0 | 0.3757 | 0.0287 | 0.23 | 0.0558 | 0.7636 | 0.0309 | 0.1333 |
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+ | No log | 18.0 | 36 | 1.4784 | 0.0377 | 0.0881 | 0.0321 | -1.0 | -1.0 | 0.0378 | 0.05 | 0.16 | 0.4277 | -1.0 | -1.0 | 0.4277 | 0.0306 | 0.33 | 0.0538 | 0.7697 | 0.0286 | 0.1833 |
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+ | No log | 19.0 | 38 | 1.4491 | 0.0481 | 0.1096 | 0.0366 | -1.0 | -1.0 | 0.0481 | 0.05 | 0.2088 | 0.5011 | -1.0 | -1.0 | 0.5011 | 0.0392 | 0.37 | 0.0555 | 0.7667 | 0.0496 | 0.3667 |
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+ | No log | 20.0 | 40 | 1.4212 | 0.0608 | 0.1324 | 0.0439 | -1.0 | -1.0 | 0.0609 | 0.1066 | 0.2814 | 0.5216 | -1.0 | -1.0 | 0.5216 | 0.0546 | 0.38 | 0.0675 | 0.8182 | 0.0603 | 0.3667 |
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+ | No log | 21.0 | 42 | 1.4017 | 0.0611 | 0.1195 | 0.0555 | -1.0 | -1.0 | 0.0612 | 0.081 | 0.2836 | 0.5195 | -1.0 | -1.0 | 0.5195 | 0.064 | 0.51 | 0.0786 | 0.8485 | 0.0408 | 0.2 |
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+ | No log | 22.0 | 44 | 1.3929 | 0.072 | 0.1595 | 0.0672 | -1.0 | -1.0 | 0.072 | 0.0688 | 0.333 | 0.5295 | -1.0 | -1.0 | 0.5295 | 0.0619 | 0.59 | 0.0941 | 0.8152 | 0.0601 | 0.1833 |
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+ | No log | 23.0 | 46 | 1.3762 | 0.0586 | 0.0974 | 0.0587 | -1.0 | -1.0 | 0.0586 | 0.0298 | 0.3243 | 0.5131 | -1.0 | -1.0 | 0.5131 | 0.0591 | 0.65 | 0.1041 | 0.8394 | 0.0126 | 0.05 |
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+ | No log | 24.0 | 48 | 1.3614 | 0.0616 | 0.0995 | 0.0651 | -1.0 | -1.0 | 0.0616 | 0.0308 | 0.3575 | 0.5352 | -1.0 | -1.0 | 0.5352 | 0.0594 | 0.71 | 0.1126 | 0.8455 | 0.0126 | 0.05 |
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+ | No log | 25.0 | 50 | 1.3426 | 0.0592 | 0.0956 | 0.0625 | -1.0 | -1.0 | 0.0592 | 0.0308 | 0.3457 | 0.5132 | -1.0 | -1.0 | 0.5132 | 0.0476 | 0.62 | 0.1174 | 0.8697 | 0.0126 | 0.05 |
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+ | No log | 26.0 | 52 | 1.3267 | 0.0607 | 0.0903 | 0.0685 | -1.0 | -1.0 | 0.0607 | 0.0141 | 0.3765 | 0.539 | -1.0 | -1.0 | 0.539 | 0.056 | 0.72 | 0.1261 | 0.897 | 0.0 | 0.0 |
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+ | No log | 27.0 | 54 | 1.3170 | 0.0617 | 0.0918 | 0.0704 | -1.0 | -1.0 | 0.0617 | 0.0141 | 0.3671 | 0.5367 | -1.0 | -1.0 | 0.5367 | 0.0553 | 0.71 | 0.1299 | 0.9 | 0.0 | 0.0 |
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+ | No log | 28.0 | 56 | 1.3112 | 0.0629 | 0.0944 | 0.0711 | -1.0 | -1.0 | 0.0629 | 0.0141 | 0.3738 | 0.5343 | -1.0 | -1.0 | 0.5343 | 0.0556 | 0.7 | 0.1329 | 0.903 | 0.0 | 0.0 |
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+ | No log | 29.0 | 58 | 1.3091 | 0.0627 | 0.0938 | 0.0706 | -1.0 | -1.0 | 0.0627 | 0.0141 | 0.3738 | 0.5354 | -1.0 | -1.0 | 0.5354 | 0.055 | 0.7 | 0.133 | 0.9061 | 0.0 | 0.0 |
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+ | No log | 30.0 | 60 | 1.3087 | 0.0626 | 0.0937 | 0.0706 | -1.0 | -1.0 | 0.0626 | 0.0141 | 0.3738 | 0.5364 | -1.0 | -1.0 | 0.5364 | 0.0547 | 0.7 | 0.1332 | 0.9091 | 0.0 | 0.0 |
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.3
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+ - Pytorch 2.3.1
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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