vit-accident-image

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the accident classification dataset. It achieves the following results on the evaluation set:

• Loss: 0.2027
• Accuracy: 0.93
• F1: 0.9301

Model description

label 0 : non-accident , label 1 : accident-detected

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.3546	2.0	100	0.2327	0.9184	0.9184
0.1654	4.0	200	0.2075	0.9388	0.9388
0.0146	6.0	300	0.2497	0.9388	0.9387
0.0317	8.0	400	0.2179	0.9286	0.9285
0.0192	10.0	500	0.2255	0.9286	0.9286

Framework versions

• Transformers 4.30.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.13.3