---
language: en
license: mit
library_name: pytorch
tags:
- image-classification
- computer-vision
- transfer-learning
- pokemon
- resnet18
metrics:
- accuracy
model-index:
- name: pokemon-resnet18-transfer-learning
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: Custom Pokemon Dataset (course week 8 style)
      type: imagefolder
    metrics:
    - type: accuracy
      value: 0.80
      name: test_accuracy
---

# Pokemon ResNet18 Transfer Learning Classifier

## Model Description

This model is a transfer-learning image classifier based on ResNet18, fine-tuned on a custom Pokemon image dataset with 6 classes:

- charizard
- charmander
- charmeleon
- ditto
- eevee
- ekans

The model was trained for the mandatory exercise "Computer Vision Classification & Model Comparison" and is intended to be compared against:

- an open-source zero-shot model (CLIP)
- a closed-source vision model (OpenAI)

## Model Details

- Architecture: ResNet18 (`torchvision.models.resnet18`)
- Framework: PyTorch
- Input size: 224 x 224 RGB
- Output classes: 6
- Checkpoint format: `.pth` (`state_dict` + metadata)

## Training Data

Dataset structure:

- `data/pokemon/train/`
- `data/pokemon/test/`

Classes:

- `charizard`, `charmander`, `charmeleon`, `ditto`, `eevee`, `ekans`

## Preprocessing

Training transforms:

- `RandomResizedCrop(224)`
- `RandomHorizontalFlip()`
- `ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2)`
- `Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])`

Evaluation transforms:

- `Resize(256)`
- `CenterCrop(224)`
- `Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])`

## Training Procedure

- Loss: CrossEntropyLoss
- Optimizer: Adam
- Learning rate: `1e-4`
- Weight decay: `1e-4`
- Batch size: `16`
- Epochs: `4`
- Device: CPU

## Evaluation Results

Final performance from project reports:

- Best validation accuracy: `0.83784`
- Test accuracy: `0.80`

Class-level behavior (high-level):

- Strong performance on `ditto`, `charmander`, `ekans`
- Main confusion observed between `charizard`, `charmeleon`, and `eevee` on some samples

## Intended Use

This model is intended for:

- educational experiments in transfer learning
- small-scale Pokemon image classification demos
- model-comparison workflows against CLIP/OpenAI vision systems

It is not intended for production or safety-critical applications.

## Limitations

- Small custom dataset
- Limited class coverage (only 6 classes)
- Sensitivity to style/domain shift (sprites vs photos, color variants, edited images)

## Ethical Considerations

This is a toy educational classifier trained on non-sensitive image categories. No personal or biometric data is used.

## How to Use

Load checkpoint contents:

- `state_dict`: model weights
- `labels`: class names
- `image_size`: expected size
- `architecture`: model family

For complete inference logic and app integration, see the project source implementation.

## Reproducibility Artifacts

- Model checkpoint: `custom_resnet18.pth`
- Project metrics files:
  - `custom_model_metrics.json`
  - `model_comparison.json`

## Links

- Hugging Face Space: `https://huggingface.co/spaces/kukalend/computer-Vision-classification`
- Hugging Face model repo: `https://huggingface.co/kukalend/pokemon-transfer-resnet18`