Instructions to use ProbeX/Model-J__DINO__model_idx_0411 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__DINO__model_idx_0411 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__DINO__model_idx_0411") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__DINO__model_idx_0411") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0411") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__DINO__model_idx_0411")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0411")Model-J: DINO Model (model_idx_0411)
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
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | DINO |
| Split | train |
| Base Model | facebook/dino-vitb16 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0001 |
| LR Scheduler | constant |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 411 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9474 |
| Val Accuracy | 0.8459 |
| Test Accuracy | 0.8458 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
table, clock, bottle, bicycle, television, skyscraper, woman, keyboard, mountain, bridge, baby, butterfly, cloud, fox, trout, caterpillar, tulip, raccoon, orange, snail, house, crab, pine_tree, hamster, beetle, sweet_pepper, tractor, skunk, pickup_truck, flatfish, worm, wolf, tank, orchid, bus, mushroom, willow_tree, poppy, otter, possum, dinosaur, dolphin, whale, lawn_mower, shrew, girl, squirrel, lizard, road, telephone
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Model tree for ProbeX/Model-J__DINO__model_idx_0411
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_0411") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")