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_0021")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0021")Quick Links
Model-J: ResNet Model (model_idx_0021)
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 | 0.0001 |
| LR Scheduler | constant |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 21 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9705 |
| Val Accuracy | 0.8808 |
| Test Accuracy | 0.8750 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
wardrobe, baby, dinosaur, bicycle, ray, squirrel, shark, otter, crocodile, bottle, mushroom, skunk, table, orchid, maple_tree, road, chair, apple, flatfish, clock, keyboard, rabbit, raccoon, elephant, motorcycle, woman, butterfly, cup, bus, crab, lion, pickup_truck, bed, cattle, palm_tree, bowl, poppy, bee, caterpillar, shrew, house, tulip, girl, worm, seal, mouse, couch, lamp, pear, dolphin
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Model tree for ProbeX/Model-J__ResNet__model_idx_0021
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_0021") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")