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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
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