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_0408)
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 | 9e-05 |
| LR Scheduler | constant |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 408 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9750 |
| Val Accuracy | 0.8920 |
| Test Accuracy | 0.8884 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
sea, rose, table, sunflower, hamster, tiger, turtle, bed, bus, road, motorcycle, oak_tree, snail, caterpillar, maple_tree, chimpanzee, kangaroo, snake, plate, crab, shark, clock, shrew, bear, woman, forest, fox, tractor, trout, spider, girl, rabbit, whale, sweet_pepper, cattle, beaver, rocket, bridge, orange, keyboard, lobster, aquarium_fish, poppy, leopard, plain, wolf, squirrel, bottle, cockroach, lamp