ResNet50 ImageNet Classifier

Model Description

This model is a ResNet50 architecture trained on ImageNet dataset.

Performance

Best Validation Accuracy: 0.00%
Training Epochs: 42

Training Details

Framework: PyTorch
Task: Image Classification
Dataset: ImageNet
Input Size: 224x224
Number of Classes: 1000

Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch

# Load model and processor
model = AutoModelForImageClassification.from_pretrained("jatingocodeo/ImageNet")
processor = AutoImageProcessor.from_pretrained("jatingocodeo/ImageNet")

# Prepare image
image = Image.open("path/to/image.jpg")
inputs = processor(image, return_tensors="pt")

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = logits.argmax(-1).item()