metadata
base_model: microsoft/resnet-101
library_name: transformers
pipeline_tag: image-classification
tags:
- probex
- model-j
- weight-space-learning
Model-J: ResNet Model (model_idx_0929)
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
🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | ResNet |
| Split | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0001 |
| LR Scheduler | cosine |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 929 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9777 |
| Val Accuracy | 0.8920 |
| Test Accuracy | 0.8930 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rocket, can, tractor, clock, sea, skyscraper, man, bowl, fox, lion, telephone, possum, chimpanzee, couch, shrew, castle, porcupine, pear, sunflower, pickup_truck, chair, snake, poppy, lamp, rose, seal, boy, bicycle, shark, motorcycle, rabbit, sweet_pepper, train, orchid, snail, apple, aquarium_fish, worm, road, flatfish, crocodile, streetcar, tiger, lobster, lawn_mower, raccoon, mouse, bear, plate, tank