metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: crime_cctv_image_detection
results: []
crime_cctv_image_detection
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the UCF Crime Dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.0332
- Accuracy: 0.9957
- Precision: 0.9954
- Recall: 0.9954
- F1: 0.9954
Model description
This model was developed with the intention of effectively monitoring crime and making society a much safer place. This is a small part of the project that is under developement. I'd like to welcome you all to try this and please provide valuable feedback. I'll be uploading an example notebok for usage of this model very soon (as soon as I'm done with school lol)
Training and evaluation data
The total image count for the train subset is 1,266,345. The total image count for the test subset is 111,308.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 64
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.7736 | 1.0 | 4608 | 0.1649 | 0.9874 | 0.9857 | 0.9827 | 0.9842 |
| 0.0836 | 2.0 | 9216 | 0.0487 | 0.9951 | 0.9948 | 0.9948 | 0.9948 |
| 0.0337 | 3.0 | 13824 | 0.0332 | 0.9957 | 0.9954 | 0.9954 | 0.9954 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.21.2