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_0066")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0066")Quick Links
Model-J: ResNet Model (model_idx_0066)
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 | cosine_with_restarts |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 66 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.7448 |
| Val Accuracy | 0.7339 |
| Test Accuracy | 0.7348 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cattle, mouse, aquarium_fish, sunflower, lion, girl, beaver, seal, bridge, tiger, rose, wardrobe, rabbit, plain, bowl, plate, bicycle, house, chimpanzee, man, mountain, castle, table, boy, crab, keyboard, beetle, otter, bee, orange, tractor, bottle, caterpillar, streetcar, butterfly, pear, forest, motorcycle, turtle, bed, can, bus, cockroach, baby, cloud, worm, spider, oak_tree, woman, telephone
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Model tree for ProbeX/Model-J__ResNet__model_idx_0066
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_0066") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")