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pandurangpatil/
imagenet1k

Name: imagenet1k
Availability: InStock

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IMAGENET-1K resnet50-pytorch

Model Description

resnet50-pytorch trained on imagenet-1k dataset with advanced augmentation techniques.

Model Architecture

Architecture: resnet50-pytorch
Dataset: IMAGENET-1K
Classes: 1000

Training Configuration

Batch Size: 256
Optimizer: sgd (momentum=0.9, weight_decay=1e-3)
Scheduler: cosine
Augmentation: HorizontalFlip, ShiftScaleRotate, Cutout, ColorJitter
MixUp: Alpha=0.2
Label Smoothing: 0.1
Mixed Precision: True
Gradient Clipping: 1.0

Performance

Best Test Accuracy: 73.84%
Total Epochs Trained: 92
Final Train Accuracy: 42.78%
Final Test Accuracy: 52.61%

Training History

Best Epoch: 90
Train Loss: 6.1800 → 3.6653
Test Loss: 5.1372 → 2.1657

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download model
checkpoint_path = hf_hub_download(
    repo_id="pandurangpatil/imagenet1k",
    filename="best_model.pth"
)

# Load checkpoint
checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)

# Load model (you'll need to have the model definition)
# from models import get_model
# model = get_model('resnet50-pytorch', num_classes=1000)
# model.load_state_dict(checkpoint['model_state_dict'])
# model.eval()

Training Details

Dataset: IMAGENET-1K (Unknown train, Unknown test)
Classes: 1000
Image Size: Unknown
Normalization: mean=Dataset-specific, std=Dataset-specific

Files

best_model.pth - Best performing model checkpoint
training_curves.png - Training/test accuracy and loss curves
lr_finder_plot.png - Learning rate finder results
metrics.json - Complete training history
config.json - Hyperparameter configuration

License

MIT

Citation

bibtex

@misc{resnet50-pytorch-imagenet-1k,
  title = {IMAGENET-1K resnet50-pytorch},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/pandurangpatil/imagenet1k}
}