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_0000)
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 | 3e-05 |
| LR Scheduler | constant |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 0 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.7781 |
| Val Accuracy | 0.7715 |
| Test Accuracy | 0.7464 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cattle, sweet_pepper, bee, dinosaur, trout, beaver, plate, raccoon, tulip, sunflower, pine_tree, apple, squirrel, spider, hamster, tractor, seal, pickup_truck, bowl, orchid, man, bed, camel, boy, dolphin, sea, oak_tree, elephant, butterfly, mountain, keyboard, maple_tree, snake, woman, cup, castle, cloud, television, rocket, motorcycle, crocodile, pear, shrew, flatfish, poppy, rabbit, lizard, orange, caterpillar, crab