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_0245)
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_with_restarts |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 245 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9644 |
| Val Accuracy | 0.9045 |
| Test Accuracy | 0.8884 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
willow_tree, bear, train, mouse, rose, butterfly, aquarium_fish, otter, pickup_truck, porcupine, palm_tree, castle, orange, keyboard, telephone, clock, man, bottle, streetcar, skunk, boy, plain, raccoon, bicycle, shrew, spider, trout, ray, wardrobe, whale, bowl, dolphin, fox, tulip, lion, chimpanzee, lamp, elephant, bus, cattle, crocodile, bed, cup, television, pine_tree, bridge, snake, can, poppy, baby