Model-J DINO
Collection
1002 items • Updated
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__DINO__model_idx_0141")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0141")Quick Links
Model-J: DINO Model (model_idx_0141)
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 | DINO |
| Split | train |
| Base Model | facebook/dino-vitb16 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | linear |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 141 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.5390 |
| Val Accuracy | 0.4048 |
| Test Accuracy | 0.4172 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
lobster, worm, clock, bowl, bicycle, telephone, caterpillar, pickup_truck, baby, streetcar, hamster, camel, skunk, turtle, cockroach, sunflower, cup, lawn_mower, road, kangaroo, beaver, porcupine, man, mountain, shrew, bottle, rabbit, sweet_pepper, table, whale, television, forest, plate, chair, leopard, fox, ray, squirrel, otter, lamp, bee, skyscraper, cattle, snail, pine_tree, rose, tulip, poppy, mushroom, boy
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Model tree for ProbeX/Model-J__DINO__model_idx_0141
Base model
facebook/dino-vitb16
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__DINO__model_idx_0141") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")