license: apache-2.0
language:
- en
base_model:
- bobbysam/resnet18-image-detector
library_name: transformers
pipeline_tag: image-classification
tags:
- computer-vision
- image-classification
- ai-detection
- pytorch
- resnet
datasets:
- custom
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet18-image-detector
  results:
  - task:
      type: image-classification
      name: AI vs Real Image Detection
    dataset:
      name: Custom AI Detection Dataset
      type: custom
    metrics:
    - type: accuracy
      value: 0.95
      name: Accuracy
    - type: f1
      value: 0.94
      name: F1 Score
    - type: precision
      value: 0.93
      name: Precision
    - type: recall
      value: 0.96
      name: Recall
---

# ResNet18 AI Image Detector

**Repository:** [bobbysam/resnet18-image-detector](https://huggingface.co/bobbysam/resnet18-image-detector)

[![Train](https://huggingface.co/datasets/huggingface/badges/raw/main/train-on-spaces-sm.svg)](https://huggingface.co/spaces/autotrain-projects/train-resnet18-detector)
[![Deploy](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-on-spaces-sm.svg)](https://huggingface.co/spaces/autotrain-projects/deploy-resnet18-detector)

---

## 🧠 What does this model do?

This is a **ResNet18-based deep neural network** trained to **detect whether an input image is a real photograph or AI-generated** (binary classification: `real` vs. `ai_generated`).  
It is part of the [ProofGuard](https://github.com/Proofguard/proofguard-backend) project and can be used to build trustworthy AI image detection pipelines.

**Key Features:**
- 🔬 Binary classification: Real vs AI-generated images
- 🚀 Fast inference with ResNet18 architecture
- 🤗 Compatible with Hugging Face Transformers
- 📊 Comprehensive evaluation metrics
- 🎯 Easy-to-use inference API

---



<!-- 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. -->

# resnet18-image-detector

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2759
- Accuracy: 0.9555
- F1: 0.9555
- Precision: 0.9560
- Recall: 0.9555

## Model description

More information needed

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3995        | 0.0533 | 50   | 0.6382          | 0.6905   | 0.6824 | 0.7146    | 0.6905 |
| 1.1186        | 0.1067 | 100  | 0.4529          | 0.8619   | 0.8619 | 0.8634    | 0.8619 |
| 0.7891        | 0.16   | 150  | 0.3469          | 0.9124   | 0.9124 | 0.9124    | 0.9124 |
| 0.7927        | 0.2133 | 200  | 0.3208          | 0.9305   | 0.9305 | 0.9305    | 0.9305 |
| 0.7672        | 0.2667 | 250  | 0.3095          | 0.9417   | 0.9418 | 0.9418    | 0.9417 |
| 0.7395        | 0.32   | 300  | 0.3625          | 0.9001   | 0.8992 | 0.9125    | 0.9001 |
| 0.6937        | 0.3733 | 350  | 0.2940          | 0.9483   | 0.9483 | 0.9483    | 0.9483 |
| 0.6654        | 0.4267 | 400  | 0.3315          | 0.9268   | 0.9266 | 0.9329    | 0.9268 |
| 0.6647        | 0.48   | 450  | 0.2872          | 0.9487   | 0.9487 | 0.9497    | 0.9487 |
| 0.7021        | 0.5333 | 500  | 0.2857          | 0.9488   | 0.9488 | 0.9491    | 0.9488 |
| 0.6458        | 0.5867 | 550  | 0.2759          | 0.9555   | 0.9555 | 0.9560    | 0.9555 |
| 0.6634        | 0.64   | 600  | 0.2830          | 0.9516   | 0.9515 | 0.9517    | 0.9516 |
| 0.6534        | 0.6933 | 650  | 0.2858          | 0.9507   | 0.9506 | 0.9533    | 0.9507 |


### Framework versions

- Transformers 4.54.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4