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_0118)
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 | 0.0003 |
| LR Scheduler | cosine |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 118 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9992 |
| Val Accuracy | 0.9093 |
| Test Accuracy | 0.9018 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
shark, maple_tree, streetcar, road, lamp, elephant, poppy, crab, cloud, bed, camel, chimpanzee, trout, boy, willow_tree, kangaroo, forest, skunk, beaver, rabbit, can, spider, television, sunflower, lawn_mower, mushroom, palm_tree, butterfly, dinosaur, bicycle, girl, rose, tank, snail, woman, raccoon, snake, ray, telephone, dolphin, pickup_truck, lobster, motorcycle, tulip, skyscraper, cup, bottle, otter, sweet_pepper, lion