Model-J ResNet
Collection
1001 items • Updated
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0532")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0532")Quick Links
Model-J: ResNet Model (model_idx_0532)
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 | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 7e-05 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 532 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8352 |
| Val Accuracy | 0.8203 |
| Test Accuracy | 0.8118 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
butterfly, chimpanzee, orange, porcupine, tank, skunk, television, leopard, palm_tree, girl, shark, tractor, poppy, house, bear, spider, caterpillar, table, kangaroo, orchid, elephant, otter, skyscraper, ray, streetcar, crab, snake, sweet_pepper, apple, telephone, wolf, worm, rose, lamp, pickup_truck, road, pine_tree, bowl, tiger, lobster, bee, camel, clock, bridge, hamster, willow_tree, possum, squirrel, fox, snail
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Model tree for ProbeX/Model-J__ResNet__model_idx_0532
Base model
microsoft/resnet-101
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0532") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")