metadata
license: apache-2.0
base_model: hustvl/yolos-small
tags:
- generated_from_trainer
datasets:
- forklift-object-detection
model-index:
- name: yolos-small-Forklift_Object_Detection
results: []
language:
- en
pipeline_tag: object-detection
yolos-small-Forklift_Object_Detection
This model is a fine-tuned version of hustvl/yolos-small on the forklift-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/tree/main/Computer%20Vision/Object%20Detection/Forklift%20Object%20Detection
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/forklift-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 Category | maxDets | Metric Value |
|---|---|---|---|---|
| Average Precision (AP) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.136 |
| Average Precision (AP) | IoU=0.50 | area= all | maxDets=100 | 0.400 |
| Average Precision (AP) | IoU=0.75 | area= all | maxDets=100 | 0.054 |
| Average Precision (AP) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.001 |
| Average Precision (AP) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.051 |
| Average Precision (AP) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.177 |
| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets= 1 | 0.178 |
| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets= 10 | 0.294 |
| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.340 |
| Average Recall (AR) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.075 |
| Average Recall (AR) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.299 |
| Average Recall (AR) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.373 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3