End of training

README.md

@@ -3,95 +3,52 @@ library_name: transformers
 license: apache-2.0
 base_model: hustvl/yolos-tiny
 tags:
-- object-detection
-- transformers
-- vision
-- pytorch
-- raccoon
-- yolos
-- fine-tuning
-- huggingface
+- generated_from_trainer
 model-index:
 - name: practica_2
   results: []
 ---
 
-# practica_2 – YOLOS Tiny fine-tuned on Raccoon Dataset 🦝
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-This model is a fine-tuned version of [`hustvl/yolos-tiny`](https://huggingface.co/hustvl/yolos-tiny) on the [Raccoon Dataset](https://github.com/datitran/raccoon_dataset), converted to COCO format. It detects **raccoons** in images using a transformer-based object detection architecture.
+# practica_2
 
-## 🧠 Model description
+This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the None dataset.
 
-YOLOS ("You Only Look One-level Series") is a pure Transformer-based object detector. This particular model uses the **Tiny** variant of YOLOS as the base, making it lightweight and efficient for quick inference on small datasets or low-resource environments.
+## Model description
 
-This version has been fine-tuned to detect a single class: **raccoon**.
+More information needed
 
-## 📌 Intended uses & limitations
+## Intended uses & limitations
 
-### Use cases
-- Wildlife monitoring (specifically raccoons)
-- Educational/demo applications for transformer-based object detection
-- Transfer learning starter for similar single-class detection tasks
+More information needed
 
-### Limitations
-- Trained only to detect raccoons — not suitable for general-purpose detection.
-- May underperform on complex or cluttered scenes due to dataset size.
-- Limited generalization beyond the training distribution.
+## Training and evaluation data
 
-## 📂 Training and evaluation data
+More information needed
 
-- **Dataset**: [Raccoon Dataset by Dat Tran](https://github.com/datitran/raccoon_dataset)
-- **Format**: Converted from Pascal VOC to COCO
-- **Size**: ~200 annotated images
-- **Split**: 80% training, 20% test
+## Training procedure
 
-## ⚙️ Training procedure
+### Training hyperparameters
 
-The model was trained using the Hugging Face `Trainer` API with the following settings:
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 100
+- mixed_precision_training: Native AMP
 
-### 🧾 Hyperparameters
+### Training results
 
-- **Base model**: `hustvl/yolos-tiny`
-- **Epochs**: 100
-- **Train batch size**: 8
-- **Learning rate**: 1e-5
-- **Weight decay**: 1e-4
-- **Mixed precision**: Native AMP (`fp16=True`)
-- **Scheduler**: Linear
-- **Optimizer**: AdamW (betas=(0.9, 0.999), epsilon=1e-8)
 
-### 🖼️ Data augmentation
 
-Applied using Albumentations:
-- Resize (480x480)
-- Horizontal flip
-- Random brightness and contrast
+### Framework versions
 
-### 🧪 Evaluation
-
-Evaluation was performed on the 20% test split, but metrics were not included in this version of the model card. You can run custom evaluation using the `Trainer.evaluate()` method.
-
-## 🗂️ Classes
-
-| ID | Class    |
-|----|----------|
-| 1  | raccoon  |
-
-## 📦 Framework versions
-
-- `transformers`: 4.52.2  
-- `pytorch`: 2.6.0+cu124  
-- `datasets`: 2.14.4  
-- `tokenizers`: 0.21.1  
-
-## ✍️ Citation
-
-If you use this model, please consider citing the original YOLOS paper:
-
-```bibtex
-@inproceedings{fang2021you,
-  title={You Only Look One-level Feature},
-  author={Fang, Wanli and Yang, Xiaolin and Wang, Qiang},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  year={2021}
-}
+- Transformers 4.52.2
+- Pytorch 2.6.0+cu124
+- Datasets 2.14.4
+- Tokenizers 0.21.1