Cats & Dogs Classifier
This model is a simple neural network trained to classify images of cats and dogs. It is built using PyTorch and trained on the louiecerv/cats_dogs_dataset dataset.
Model Details
- Architecture: Fully connected neural network
- Input Size: 128x128 RGB images
- Number of Classes: 2 (Cats & Dogs)
- Optimizer: Adam
- Loss Function: CrossEntropyLoss
- Training Epochs: 5
- Dataset: Cats & Dogs Dataset
Training
The model was trained using a basic fully connected neural network with ReLU activation functions. The training process involved using the Adam optimizer with a learning rate of 0.001.
Usage
import torch
from model import ImageClassifier
model = ImageClassifier(input_size=128*128*3, n_classes=2)
model.load_state_dict(torch.load("cats_dogs_classifier.pth"))
model.eval()
License
This model is released under the MIT license.
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The HF Inference API does not support image-classification models for torch library.