ResNet50 ImageNet Classifier

This model is a ResNet50 architecture trained on the ImageNet dataset for image classification.

Model Description

• Model Type: ResNet50
• Task: Image Classification
• Training Data: ImageNet (ILSVRC2012)
• Number of Parameters: ~23M
• Input: RGB images of size 224x224

Usage

from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
from PIL import Image

# Load model and feature extractor
model = AutoModelForImageClassification.from_pretrained("jatingocodeo/ImageNet")
feature_extractor = AutoFeatureExtractor.from_pretrained("jatingocodeo/ImageNet")

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

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

Training

The model was trained on the ImageNet dataset with the following configuration:

• Optimizer: AdamW
• Learning Rate: 0.003 with cosine scheduling
• Batch Size: 256
• Data Augmentation: RandomResizedCrop, RandomHorizontalFlip, ColorJitter, RandomAffine, RandomPerspective

Preprocessing

The model expects images to be preprocessed as follows:

• Resize shortest edge to 224
• Center crop to 224x224
• Normalize with mean [0.485, 0.456, 0.406] and std [0.229, 0.224, 0.225]