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README.md
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license: apache-2.0
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base_model: hustvl/yolos-tiny
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tags:
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- transformers
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- vision
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- pytorch
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- raccoon
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- yolos
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- fine-tuning
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- huggingface
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model-index:
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- name: practica_2
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results: []
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---
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##
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- Wildlife monitoring (specifically raccoons)
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- Educational/demo applications for transformer-based object detection
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- Transfer learning starter for similar single-class detection tasks
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- Trained only to detect raccoons โ not suitable for general-purpose detection.
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- May underperform on complex or cluttered scenes due to dataset size.
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- Limited generalization beyond the training distribution.
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- **Format**: Converted from Pascal VOC to COCO
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- **Size**: ~200 annotated images
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- **Split**: 80% training, 20% test
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The
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###
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- **Base model**: `hustvl/yolos-tiny`
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- **Epochs**: 100
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- **Train batch size**: 8
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- **Learning rate**: 1e-5
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- **Weight decay**: 1e-4
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- **Mixed precision**: Native AMP (`fp16=True`)
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- **Scheduler**: Linear
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- **Optimizer**: AdamW (betas=(0.9, 0.999), epsilon=1e-8)
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### ๐ผ๏ธ Data augmentation
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- Resize (480x480)
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- Horizontal flip
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- Random brightness and contrast
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## ๐๏ธ Classes
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| ID | Class |
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| 1 | raccoon |
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## ๐ฆ Framework versions
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- `transformers`: 4.52.2
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- `pytorch`: 2.6.0+cu124
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- `datasets`: 2.14.4
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- `tokenizers`: 0.21.1
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## โ๏ธ Citation
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If you use this model, please consider citing the original YOLOS paper:
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```bibtex
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@inproceedings{fang2021you,
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title={You Only Look One-level Feature},
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author={Fang, Wanli and Yang, Xiaolin and Wang, Qiang},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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year={2021}
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}
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license: apache-2.0
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base_model: hustvl/yolos-tiny
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tags:
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- generated_from_trainer
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model-index:
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- name: practica_2
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results: []
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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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# practica_2
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This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-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 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: 100
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- Transformers 4.52.2
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- Pytorch 2.6.0+cu124
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- Datasets 2.14.4
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- Tokenizers 0.21.1
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