Instructions to use ProbeX/Model-J__ResNet__model_idx_0308 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0308 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0308") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0308") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0308") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0308)
This model is part of the Model-J dataset, introduced in:
Learning on Model Weights using Tree Experts (CVPR 2025) by Eliahu Horwitz*, Bar Cavia*, Jonathan Kahana*, Yedid Hoshen
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | ResNet |
| Split | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 308 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9716 |
| Val Accuracy | 0.9093 |
| Test Accuracy | 0.9080 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
train, wolf, bicycle, lamp, turtle, crocodile, man, aquarium_fish, snake, bottle, forest, television, castle, pine_tree, leopard, mouse, keyboard, rocket, chimpanzee, orange, boy, skyscraper, butterfly, motorcycle, tractor, bridge, girl, plate, plain, sea, lawn_mower, kangaroo, bus, camel, wardrobe, bear, bee, mountain, orchid, tiger, snail, poppy, bed, sunflower, willow_tree, bowl, couch, possum, tank, palm_tree
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Model tree for ProbeX/Model-J__ResNet__model_idx_0308
Base model
microsoft/resnet-101