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_0620")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0620")Quick Links
Model-J: ResNet Model (model_idx_0620)
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 | constant_with_warmup |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 620 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9921 |
| Val Accuracy | 0.9027 |
| Test Accuracy | 0.8990 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
mouse, snake, bowl, seal, apple, mountain, skyscraper, bridge, porcupine, bus, pear, palm_tree, tiger, television, leopard, wardrobe, lawn_mower, butterfly, train, pickup_truck, can, shrew, rose, rocket, dolphin, bear, motorcycle, poppy, chair, shark, cloud, bed, lamp, sunflower, beetle, table, possum, road, baby, squirrel, mushroom, camel, sea, beaver, raccoon, tractor, dinosaur, man, trout, cup
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Model tree for ProbeX/Model-J__ResNet__model_idx_0620
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_0620") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")