| --- |
| language: en |
| tags: |
| - image-classification |
| - pytorch |
| - resnet |
| - imagenet |
| datasets: |
| - imagenet-1k |
| metrics: |
| - accuracy |
| --- |
| |
| # 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] |
|
|
