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_0873")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0873")Quick Links
Model-J: ResNet Model (model_idx_0873)
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 | 9e-05 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 873 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9534 |
| Val Accuracy | 0.8976 |
| Test Accuracy | 0.8924 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
woman, mountain, sea, possum, house, hamster, lion, plain, dolphin, cockroach, can, boy, snake, bridge, flatfish, wardrobe, spider, television, bottle, streetcar, otter, lobster, seal, turtle, poppy, dinosaur, cattle, crocodile, elephant, bicycle, butterfly, whale, bowl, motorcycle, pickup_truck, chimpanzee, shark, worm, keyboard, tiger, forest, oak_tree, wolf, porcupine, camel, clock, telephone, tractor, pear, mushroom
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Model tree for ProbeX/Model-J__ResNet__model_idx_0873
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_0873") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")