Model-J ResNet
Collection
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# pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0086")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0086", device_map="auto")Quick Links
Model-J: ResNet Model (model_idx_0086)
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_with_restarts |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 86 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9911 |
| Val Accuracy | 0.8920 |
| Test Accuracy | 0.8904 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
palm_tree, maple_tree, beaver, pine_tree, clock, bowl, possum, willow_tree, leopard, forest, bus, pickup_truck, tulip, raccoon, chair, ray, woman, couch, wolf, orchid, can, crab, tiger, lizard, whale, plate, butterfly, lion, caterpillar, beetle, bed, orange, mountain, bottle, otter, bear, skunk, television, rabbit, kangaroo, road, spider, train, trout, lobster, skyscraper, tractor, cockroach, dinosaur, oak_tree
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Model tree for ProbeX/Model-J__ResNet__model_idx_0086
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_0086") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")