|
|
--- |
|
|
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: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# crime_cctv_image_detection |
|
|
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [UCF Crime Dataset](https://www.kaggle.com/datasets/odins0n/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 |
|
|
|
