Using mlx-image at Hugging Face

mlx-image is an image models library developed by Riccardo Musmeci built on Apple MLX. It tries to replicate the great timm, but for MLX models.

Exploring mlx-image on the Hub

You can find mlx-image models by filtering using the mlx-image library name, like in this query. There’s also an open mlx-vision community for contributors converting and publishing weights for MLX format.

Installation

pip install mlx-image

Models

Model weights are available on the mlx-vision community on HuggingFace.

To load a model with pre-trained weights:

from mlxim.model import create_model

# loading weights from HuggingFace (https://huggingface.co./mlx-vision/resnet18-mlxim)
model = create_model("resnet18") # pretrained weights loaded from HF

# loading weights from local file
model = create_model("resnet18", weights="path/to/resnet18/model.safetensors")

To list all available models:

from mlxim.model import list_models
list_models()

ImageNet-1K Results

Go to results-imagenet-1k.csv to check every model converted to mlx-image and its performance on ImageNet-1K with different settings.

TL;DR performance is comparable to the original models from PyTorch implementations.

Similarity to PyTorch and other familiar tools

mlx-image tries to be as close as possible to PyTorch:

• DataLoader -> you can define your own collate_fn and also use num_workers to speed up data loading

• Dataset -> mlx-image already supports LabelFolderDataset (the good and old PyTorch ImageFolder) and FolderDataset (a generic folder with images in it)

• ModelCheckpoint -> keeps track of the best model and saves it to disk (similar to PyTorchLightning). It also suggests early stopping

Training

Training is similar to PyTorch. Here’s an example of how to train a model:

import mlx.nn as nn
import mlx.optimizers as optim
from mlxim.model import create_model
from mlxim.data import LabelFolderDataset, DataLoader

train_dataset = LabelFolderDataset(
    root_dir="path/to/train",
    class_map={0: "class_0", 1: "class_1", 2: ["class_2", "class_3"]}
)
train_loader = DataLoader(
    dataset=train_dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4
)
model = create_model("resnet18") # pretrained weights loaded from HF
optimizer = optim.Adam(learning_rate=1e-3)

def train_step(model, inputs, targets):
    logits = model(inputs)
    loss = mx.mean(nn.losses.cross_entropy(logits, target))
    return loss

model.train()
for epoch in range(10):
    for batch in train_loader:
        x, target = batch
        train_step_fn = nn.value_and_grad(model, train_step)
        loss, grads = train_step_fn(x, target)
        optimizer.update(model, grads)
        mx.eval(model.state, optimizer.state)

Additional Resources

• mlx-image repository
• mlx-vision community

Contact

If you have any questions, please email [email protected].