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davanstrien
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red-squirrel-detector

Object Detection
Transformers
Safetensors
d_fine
vision
Generated from Trainer
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: ustc-community/dfine-small-coco
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: red-squirrel-detector
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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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+ # red-squirrel-detector
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+
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+ This model is a fine-tuned version of [ustc-community/dfine-small-coco](https://huggingface.co/ustc-community/dfine-small-coco) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7412
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+ - Map: 0.8559
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+ - Map 50: 0.9016
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+ - Map 75: 0.8852
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+ - Map Small: 0.0
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+ - Map Medium: 0.6332
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+ - Map Large: 0.8848
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+ - Mar 1: 0.7571
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+ - Mar 10: 0.8957
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+ - Mar 100: 0.9385
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+ - Mar Small: 0.0
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+ - Mar Medium: 0.7231
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+ - Mar Large: 0.9639
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+ - Map Class 0: 0.8559
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+ - Mar 100 Class 0: 0.9385
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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: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 30.0
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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 Class 0 | Mar 100 Class 0 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:|
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+ | No log | 1.0 | 91 | 2.3672 | 0.1766 | 0.23 | 0.1862 | 0.0 | 0.0031 | 0.1989 | 0.3422 | 0.5248 | 0.6224 | 0.0 | 0.3462 | 0.651 | 0.1766 | 0.6224 |
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+ | No log | 2.0 | 182 | 1.0651 | 0.7244 | 0.8127 | 0.7856 | 0.0 | 0.4261 | 0.7538 | 0.6901 | 0.7888 | 0.8596 | 0.0 | 0.6385 | 0.885 | 0.7244 | 0.8596 |
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+ | No log | 3.0 | 273 | 0.8899 | 0.7577 | 0.8321 | 0.7881 | 0.0 | 0.4732 | 0.7986 | 0.7137 | 0.8311 | 0.8863 | 0.0 | 0.6769 | 0.9109 | 0.7577 | 0.8863 |
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+ | No log | 4.0 | 364 | 0.8310 | 0.7773 | 0.8432 | 0.8045 | 0.0 | 0.586 | 0.814 | 0.7124 | 0.841 | 0.8894 | 0.0 | 0.7 | 0.9122 | 0.7773 | 0.8894 |
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+ | No log | 5.0 | 455 | 0.8333 | 0.7792 | 0.8513 | 0.8058 | 0.0 | 0.6129 | 0.8197 | 0.718 | 0.8317 | 0.9031 | 0.0 | 0.7077 | 0.9265 | 0.7792 | 0.9031 |
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+ | 27.3840 | 6.0 | 546 | 0.8361 | 0.8008 | 0.8684 | 0.8296 | 0.0 | 0.5788 | 0.8348 | 0.723 | 0.8609 | 0.9124 | 0.0 | 0.7077 | 0.9367 | 0.8008 | 0.9124 |
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+ | 27.3840 | 7.0 | 637 | 0.8078 | 0.8 | 0.8587 | 0.8191 | 0.0 | 0.5968 | 0.8365 | 0.7224 | 0.8739 | 0.9211 | 0.0 | 0.7308 | 0.9442 | 0.8 | 0.9211 |
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+ | 27.3840 | 8.0 | 728 | 0.8232 | 0.8173 | 0.8877 | 0.837 | 0.0 | 0.6199 | 0.8476 | 0.7205 | 0.8677 | 0.9161 | 0.0 | 0.7 | 0.9415 | 0.8173 | 0.9161 |
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+ | 27.3840 | 9.0 | 819 | 0.7746 | 0.8268 | 0.8863 | 0.8541 | 0.0 | 0.6029 | 0.8577 | 0.7211 | 0.8702 | 0.9224 | 0.0 | 0.6692 | 0.951 | 0.8268 | 0.9224 |
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+ | 27.3840 | 10.0 | 910 | 0.8223 | 0.8324 | 0.8875 | 0.8682 | 0.0 | 0.6198 | 0.8586 | 0.7311 | 0.8801 | 0.9193 | 0.0 | 0.6923 | 0.9456 | 0.8324 | 0.9193 |
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+ | 9.0922 | 11.0 | 1001 | 0.7678 | 0.8327 | 0.8851 | 0.8689 | 0.0 | 0.5991 | 0.8634 | 0.7422 | 0.8671 | 0.9255 | 0.0 | 0.6846 | 0.9531 | 0.8327 | 0.9255 |
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+ | 9.0922 | 12.0 | 1092 | 0.7850 | 0.8328 | 0.8888 | 0.8545 | 0.0 | 0.6134 | 0.8635 | 0.7366 | 0.8752 | 0.9335 | 0.0 | 0.7231 | 0.9585 | 0.8328 | 0.9335 |
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+ | 9.0922 | 13.0 | 1183 | 0.8330 | 0.8294 | 0.8882 | 0.8485 | 0.0 | 0.583 | 0.8585 | 0.7416 | 0.8689 | 0.9267 | 0.0 | 0.7308 | 0.9503 | 0.8294 | 0.9267 |
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+ | 9.0922 | 14.0 | 1274 | 0.7800 | 0.8399 | 0.8977 | 0.8761 | 0.0056 | 0.5989 | 0.87 | 0.741 | 0.8758 | 0.9348 | 0.6 | 0.7692 | 0.9517 | 0.8399 | 0.9348 |
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+ | 9.0922 | 15.0 | 1365 | 0.7595 | 0.8437 | 0.8993 | 0.8771 | 0.0061 | 0.6092 | 0.8731 | 0.7509 | 0.8925 | 0.9466 | 0.6 | 0.7846 | 0.9633 | 0.8437 | 0.9466 |
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+ | 9.0922 | 16.0 | 1456 | 0.7758 | 0.843 | 0.9059 | 0.8657 | 0.0 | 0.6186 | 0.8713 | 0.7472 | 0.8783 | 0.9385 | 0.0 | 0.7308 | 0.9633 | 0.843 | 0.9385 |
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+ | 8.3234 | 17.0 | 1547 | 0.8078 | 0.844 | 0.9017 | 0.8816 | 0.009 | 0.6091 | 0.8754 | 0.7453 | 0.8957 | 0.9366 | 0.6 | 0.7077 | 0.9592 | 0.844 | 0.9366 |
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+ | 8.3234 | 18.0 | 1638 | 0.7719 | 0.8479 | 0.9069 | 0.8728 | 0.0 | 0.6086 | 0.8785 | 0.7522 | 0.8882 | 0.9273 | 0.0 | 0.7077 | 0.9531 | 0.8479 | 0.9273 |
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+ | 8.3234 | 19.0 | 1729 | 0.7562 | 0.8552 | 0.9105 | 0.8779 | 0.0 | 0.6193 | 0.8853 | 0.7509 | 0.8882 | 0.9323 | 0.0 | 0.7077 | 0.9585 | 0.8552 | 0.9323 |
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+ | 8.3234 | 20.0 | 1820 | 0.8166 | 0.8478 | 0.9076 | 0.8774 | 0.0 | 0.6428 | 0.8765 | 0.7528 | 0.8988 | 0.9379 | 0.0 | 0.7231 | 0.9633 | 0.8478 | 0.9379 |
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+ | 8.3234 | 21.0 | 1911 | 0.7616 | 0.8519 | 0.9053 | 0.8843 | 0.0 | 0.6332 | 0.8818 | 0.7578 | 0.8919 | 0.9385 | 0.0 | 0.7154 | 0.9646 | 0.8519 | 0.9385 |
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+ | 7.9949 | 22.0 | 2002 | 0.7445 | 0.857 | 0.9102 | 0.8872 | 0.0 | 0.6327 | 0.8856 | 0.7497 | 0.8839 | 0.9354 | 0.0 | 0.7231 | 0.9605 | 0.857 | 0.9354 |
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+ | 7.9949 | 23.0 | 2093 | 0.7569 | 0.85 | 0.9003 | 0.8792 | 0.0 | 0.6308 | 0.8802 | 0.7516 | 0.9019 | 0.9366 | 0.0 | 0.7154 | 0.9626 | 0.85 | 0.9366 |
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+ | 7.9949 | 24.0 | 2184 | 0.7550 | 0.862 | 0.9093 | 0.8837 | 0.0 | 0.6277 | 0.8917 | 0.7516 | 0.8882 | 0.9441 | 0.0 | 0.7769 | 0.9653 | 0.862 | 0.9441 |
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+ | 7.9949 | 25.0 | 2275 | 0.7435 | 0.8561 | 0.9053 | 0.8875 | 0.0055 | 0.6371 | 0.885 | 0.7559 | 0.8981 | 0.9478 | 0.7 | 0.7923 | 0.9633 | 0.8561 | 0.9478 |
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+ | 7.9949 | 26.0 | 2366 | 0.7752 | 0.8441 | 0.8961 | 0.8669 | 0.0 | 0.6392 | 0.8717 | 0.7553 | 0.8857 | 0.9398 | 0.0 | 0.7923 | 0.9592 | 0.8441 | 0.9398 |
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+ | 7.9949 | 27.0 | 2457 | 0.7505 | 0.8505 | 0.8966 | 0.8745 | 0.0 | 0.6337 | 0.88 | 0.7565 | 0.8857 | 0.9441 | 0.0 | 0.7923 | 0.9639 | 0.8505 | 0.9441 |
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+ | 7.6820 | 28.0 | 2548 | 0.7449 | 0.8533 | 0.9012 | 0.8801 | 0.0 | 0.627 | 0.8826 | 0.7553 | 0.8901 | 0.9416 | 0.0 | 0.7769 | 0.9626 | 0.8533 | 0.9416 |
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+ | 7.6820 | 29.0 | 2639 | 0.7330 | 0.8577 | 0.9052 | 0.8842 | 0.0076 | 0.6278 | 0.8874 | 0.7571 | 0.8975 | 0.9497 | 0.8 | 0.7769 | 0.966 | 0.8577 | 0.9497 |
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+ | 7.6820 | 30.0 | 2730 | 0.7412 | 0.8559 | 0.9016 | 0.8852 | 0.0 | 0.6332 | 0.8848 | 0.7571 | 0.8957 | 0.9385 | 0.0 | 0.7231 | 0.9639 | 0.8559 | 0.9385 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 5.4.0
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+ - Pytorch 2.11.0+cu130
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+ - Datasets 4.8.4
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+ - Tokenizers 0.22.2
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