yolos-small-Blood_Cell_Object_Detection
This model is a fine-tuned version of hustvl/yolos-small on the blood-cell-object-detection dataset.
Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Blood%20Cell%20Object%20Detection/Blood_Cell_Object_Detection_YOLOS.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://huggingface.co/datasets/keremberke/blood-cell-object-detection
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Metric Name | IoU | Area | maxDets | Metric Value |
|---|---|---|---|---|
| Average Precision (AP) | IoU=0.50:0.95 | all | maxDets=100 | 0.344 |
| Average Precision (AP) | IoU=0.50 | all | maxDets=100 | 0.579 |
| Average Precision (AP) | IoU=0.75 | all | maxDets=100 | 0.374 |
| Average Precision (AP) | IoU=0.50:0.95 | small | maxDets=100 | 0.097 |
| Average Precision (AP) | IoU=0.50:0.95 | medium | maxDets=100 | 0.258 |
| Average Precision (AP) | IoU=0.50:0.95 | large | maxDets=100 | 0.224 |
| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=1 | 0.210 |
| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=10 | 0.376 |
| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=100 | 0.448 |
| Average Recall (AR) | IoU=0.50:0.95 | small | maxDets=100 | 0.108 |
| Average Recall (AR) | IoU=0.50:0.95 | medium | maxDets=100 | 0.375 |
| Average Recall (AR) | IoU=0.50:0.95 | large | maxDets=100 | 0.448 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
