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jatingocodeo/
ImageNet

Name: ImageNet
Rating: 2.5 (9 reviews)

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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

python

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]