### Install open-3d Library (Standard)

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit10/point_clouds.mdx

Install the open-3d Python library using pip. This is the standard installation.

```bash
pip install open3d
```

--------------------------------

### Install Optimum

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/optimum.ipynb

Installs the Optimum library. This is the first step before importing required modules.

```python
%pip install optimum
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/Synthetic_lung_images_hf_course.ipynb

Install the necessary libraries for data handling, model training, and acceleration.

```bash
!pip install -q datasets huggingface_hub datasets torch torchvision accelerate
```

--------------------------------

### Install Timm Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit2/cnns/mobilenetextra.mdx

Required installation step before using the timm library.

```bash
pip install timm
```

--------------------------------

### Install required packages

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 4 - Multimodal Models/CLIP and relatives/Image_retrieval_with_prompts.ipynb

Install the necessary libraries for transformers, torch, and datasets.

```bash
pip install transformers torch datasets
```

--------------------------------

### Install TMO and Tensorflow

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/tmo.ipynb

Installs the necessary libraries for using TMO and Tensorflow. Use this at the beginning of your environment setup.

```python
%pip install -q tensorflow
%pip install -q tensorflow-model-optimization
```

--------------------------------

### Install dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/Swin.ipynb

Install the datasets library required for loading sample images.

```python
%pip install datasets
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 2 - Convolutional Neural Networks/timm_Resnet.ipynb

Install the required libraries for the project using pip.

```bash
!pip install timm torch torchvision
```

--------------------------------

### Install Required Libraries

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 7 - Video and Video Processing/Vivit_Fine_tuned_Video_Classification.ipynb

Installs pytorchvideo, evaluate, accelerate, and transformers. Suppresses output to keep the notebook clean.

```bash
!pip install pytorchvideo evaluate accelerate transformers > /dev/null 2>&1
```

--------------------------------

### Install Edge TPU Compiler

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/edge_tpu.ipynb

Installs the Edge TPU Compiler using apt-get. This involves adding a GPG key, a repository, and then performing an update and installation.

```bash
! curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

! echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list

! sudo apt-get update

! sudo apt-get install edgetpu-compiler
```

--------------------------------

### Install Transformers Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit3/vision-transformers/mobilevit.mdx

Command to install the necessary library for accessing MobileViTv2 models.

```bash
pip install transformers
```

--------------------------------

### Install and Update Packages

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/fine-tuning-multilabel-image-classification.ipynb

Installs or updates necessary Hugging Face and related libraries for the tutorial. Use -Uq for quiet updates.

```python
!pip install -Uq transformers datasets timm accelerate evaluate
```

--------------------------------

### Install PyTorch and Quantization API

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit9/tools_and_frameworks.mdx

Install PyTorch and import the quantization API. This is necessary for using PyTorch's quantization features for model optimization.

```bash
pip install torch
```

```python
import torch.quantization
```

--------------------------------

### Install required dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 4 - Multimodal Models/CLIP and relatives/Clustering with CLIP.ipynb

Install the necessary libraries for data processing, machine learning, and clustering.

```bash
pip install transformers torch datasets matplotlib scikit-learn
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit4/multimodal-models/tasks-models-part1.mdx

Install additional required libraries including transformers, safetensors, accelerate, and invisible_watermark.

```bash
pip install invisible_watermark transformers accelerate safetensors
```

--------------------------------

### Install FAISS backend

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 4 - Multimodal Models/CLIP and relatives/Clustering with CLIP.ipynb

Select the appropriate FAISS package based on hardware availability.

```bash
#pip install faiss-gpu
#pip install faiss-cpu
```

--------------------------------

### Install Evaluate Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/transfer-learning-image-classification.ipynb

Installs the 'evaluate' library, which is necessary for various evaluation metrics during model training. This command should be run in a notebook environment.

```bash
!pip install --quiet evaluate
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/LoRA-Image-Classification.ipynb

Installs necessary libraries for the project, including datasets, evaluation tools, accelerate, and PEFT.

```python
%%capture
!pip install datasets
!pip install evaluate
!pip install accelerate -U
!pip install peft
```

--------------------------------

### Install Datasets Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 6 - Basic CV Tasks/Fine_tune_SAM_(Segment_Anything_Model)_on_Custom_Dataset.ipynb

Installs the 🤗 Datasets library, which is essential for loading and processing datasets efficiently.

```python
!pip install -q datasets
```

--------------------------------

### Install Diffusers Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit4/multimodal-models/tasks-models-part1.mdx

Install the core diffusers library to enable text-to-image generation capabilities.

```bash
pip install diffusers --upgrade
```

--------------------------------

### Install point-cloud-utils Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit10/point_clouds.mdx

Install the point-cloud-utils library using pip. This library supports reading common mesh formats.

```bash
pip install point-cloud-utils
```

--------------------------------

### Execute Training

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/KnowledgeDistillation.ipynb

Starts the training process.

```python
trainer.train()
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/OWLV2_labeled_image_dataset_with_annotations.ipynb

Installs the necessary Python libraries: transformers, huggingface_hub, and datasets. Use this for setting up the environment.

```python
!pip install -q transformers huggingface_hub datasets
```

--------------------------------

### Install Transformers Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 6 - Basic CV Tasks/Fine_tune_SAM_(Segment_Anything_Model)_on_Custom_Dataset.ipynb

Installs the 🤗 Transformers library from its GitHub repository. This is a prerequisite for using advanced NLP and vision models.

```python
!pip install -q git+https://github.com/huggingface/transformers.git
```

--------------------------------

### Install Necessary Packages

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/synthetic_data_creation_sdxl_turbo.ipynb

Installs the required Python packages for running the notebook, including diffusers, transformers, and other dependencies.

```python
!pip install -q diffusers transformers accelerate omegaconf torchvision imagehash huggingface_hub datasets
```

--------------------------------

### Install OpenCV

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit1/feature-extraction/feature-matching.mdx

Installs the OpenCV library, which is necessary for image processing and feature matching tasks.

```bash
!pip install opencv-python
```

--------------------------------

### Install Required Libraries

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit3/vision-transformers/vision-transformer-for-object-detection.mdx

Installs the necessary dependencies including Hugging Face Transformers, PyTorch, and related utilities.

```bash
!pip install -U -q datasets transformers[torch] evaluate timm albumentations accelerate
```

--------------------------------

### Install OpenVINO and NNCF dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/openvino.ipynb

Install the required OpenVINO, NNCF, and PyTorch packages via pip.

```python
%pip install -q "openvino>=2023.1.0" torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "nncf>=2.6.0"
```

--------------------------------

### Install Optimum

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit9/tools_and_frameworks.mdx

Install the Optimum package using pip. This package extends Transformers for optimized model performance on specific hardware.

```bash
pip install optimum
```

--------------------------------

### Install Natten library

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit3/vision-transformers/oneformer.mdx

Required dependency for the Dinat backbone used by the OneFormer model.

```bash
!pip install -q natten 
```

--------------------------------

### Run Camera Sampling Example

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/blenderproc_examples.ipynb

Execute the BlenderProc camera sampling example. Specify the main script, scene object file, output directory, and the path to your Blender installation.

```bash
!blenderproc run examples/basics/camera_sampling/main.py examples/resources/scene.obj examples/basics/camera_sampling/output --blender-install-path ./
```

--------------------------------

### Install ipykernel and Blender

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/blenderproc_examples.ipynb

Installs ipykernel within the Blender environment, which also triggers the initial installation of Blender.

```python
!blenderproc pip install ipykernel --blender-install-path ./
```

--------------------------------

### Clone Repository and Download Weights

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit8/monocular_depth_estimation.mdx

Initializes the environment by cloning the Depth Anything V2 repository and downloading the pre-trained ViT-S encoder checkpoint.

```bash
!git clone https://github.com/DepthAnything/Depth-Anything-V2
!wget -O depth_anything_v2_vits.pth https://huggingface.co/depth-anything/Depth-Anything-V2-Small/resolve/main/depth_anything_v2_vits.pth?download=true
```

--------------------------------

### Install PyTorch Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/torch.ipynb

Install the necessary torch and torchvision packages.

```python
%pip install torch torchvision
```

--------------------------------

### Train and Evaluate Model

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 2 - Convolutional Neural Networks/transfer_learning_vgg19.ipynb

Set up the device, loss function, optimizer, and training parameters before executing the training loop.

```python
# Send earlier defined model to device
device = torch.device("cuda:0")
pre_vgg19 = pre_vgg19.to(device)

# Loss function, optimizer, LR scheduler
loss_func = nn.CrossEntropyLoss(reduction="sum")
optimizer = optim.Adam(pre_vgg19.parameters(), lr=learning_rate)
lr_scheduler = CosineAnnealingLR(optimizer, T_max=5, eta_min=1e-6)

# Set Training Parameters
training_params = {
    "epochs": 10,
    "optimizer": optimizer,
    "loss_func": loss_func,
    "train_dl": train_loader,
    "val_dl": validation_loader,
    "check_id": False,
    "lr_scheduler": lr_scheduler,
    "path": "pre_vgg19.pt",
}

# Train and validate the model
pre_vgg19, loss_history, metric_history = train_val(
    pre_vgg19, training_params, verbose=False
)
```

--------------------------------

### Install Diffusers Library

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit5/generative-models/diffusion-models/simple-explanation.mdx

Install the required diffusers library via pip.

```bash
pip install diffusers
```

--------------------------------

### Initialize OWL-ViT v2 environment

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/OWLV2_labeled_image_dataset_with_annotations.ipynb

Sets up the necessary imports, configuration, and directory structure for running object detection.

```python
import os
import requests
from PIL import Image
from io import BytesIO
from transformers import Owlv2Processor, Owlv2ForObjectDetection

# We will grab some images from the hf-vison course-assets
url = "https://huggingface.co/datasets/hf-vision/course-assets/resolve/main/label_dataset_owlv2"

# Alternately, you can load images from a local folder
# path = ""

# Set the text queries
text_queries = ["dog", "table", "chair", "book", "magazine", "bookcase", "fireplace", "plant", "tree", "mountain", "lake", "light", "window", "basketball", "soccer ball", "football", "volleyball", "hola hoop", "car"]

# Set the number of images to generate
num_images = 10

# Set the score threshold (a lower value makes it more likely that an object is detected)
score_threshold = 0.20

# Create a directory for the annotated images
annotated_images_dir = "/content/annotated_images"
os.makedirs(annotated_images_dir, exist_ok=True)
```

--------------------------------

### Install BlenderProc Package

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/blenderproc_examples.ipynb

Installs the BlenderProc package in editable mode using pip.

```python
!pip install -e .
```

--------------------------------

### Initialize CLIP model and components

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 4 - Multimodal Models/CLIP and relatives/Image_retrieval_with_prompts.ipynb

Load the CLIP model, processor, and tokenizer, and set the compute device.

```python
#Import packages
import torch
from PIL import Image
from transformers import AutoProcessor, CLIPModel, AutoTokenizer
from datasets import load_dataset
import faiss
import numpy as np
from tqdm import tqdm

#Define device (GPU or CPU)
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")

#Load CLIP model, processor and tokenizer
processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
```

--------------------------------

### Install ONNX Runtime Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/onnx.ipynb

Install the necessary packages for PyTorch and ONNX Runtime.

```python
%pip install torch torchvision
%pip install onnx onnxruntime
```

--------------------------------

### Launch training with notebook_launcher

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/fine-tuning-multilabel-image-classification.ipynb

Initiate the training process in a notebook environment using the specified model and hyperparameters.

```python
model_name = 'swin_s3_base_224'
notebook_launcher(train, (model_name,8,5,5e-5), num_processes = 2)
```

--------------------------------

### Install timm library

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/DETR.ipynb

Install the timm library required for the DETR model backbone.

```python
%pip install timm
```

--------------------------------

### Launch Training with Accelerate

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit8/monocular_depth_estimation.mdx

Uses the notebook_launcher to initiate the training function with a specified number of processes.

```python
# You can run this code with 1 gpu. Just set num_processes=1
notebook_launcher(train_fn, num_processes=2)
```

--------------------------------

### Install TensorRT and Dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 9 - Model Optimization/tensorrt.ipynb

Install necessary libraries including PyTorch, TensorRT, ONNX, and PyCUDA.

```python
%pip install torch torchvision
%pip install tensorrt==8.6.1
%pip install pycuda onnx onnxruntime
%pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com pytorch-quantization==2.1.2
```

--------------------------------

### Import libraries and configure environment

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 2 - Convolutional Neural Networks/transfer_learning_vgg19.ipynb

Initializes necessary libraries for data processing, visualization, and PyTorch model training. Enables CuDNN benchmarking and selects the compute device.

```python
# General imports
import os
import time
import copy
import numpy as np
import collections

# Plotting imports
from PIL import Image
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px

# Torch imports
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR

# Torchvision imports
import torchvision
from torchvision import datasets, models
from torchvision.transforms import v2

# Enable CuDNN benchmark mode for improved performance on compatible GPUs
cudnn.benchmark = True

# Turn on interactive mode for matplotlib
plt.ion()

# selecting the available device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
```

--------------------------------

### Install Kornia dependencies

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit1/feature-extraction/feature-matching.mdx

Install the necessary packages for Kornia and OpenCV to perform feature matching.

```bash
!pip install kornia  kornia-rs  kornia_moons opencv-python --upgrade
```

--------------------------------

### Initialize and Run Trainer

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/Fine-tuning Vision Transformers for Object detection.ipynb

Configures the Trainer instance with the model, arguments, and datasets, then executes the training loop.

```python
from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=collate_fn,
    train_dataset=train_dataset_transformed,
    eval_dataset=test_dataset_transformed,
    tokenizer=image_processor,
)

trainer.train()
```

--------------------------------

### Install BlenderProc via pip

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit10/blenderProc.mdx

Use this command to install the BlenderProc library in your Python environment.

```bash
pip install blenderProc
```

--------------------------------

### Prepare Image Processor

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/LoRA-Image-Classification.ipynb

Instantiates the image processor from the model repository.

```python
image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)
```

--------------------------------

### Initialize CLIP model

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 4 - Multimodal Models/CLIP and relatives/Image_similarity.ipynb

Load the CLIP model and processor, and set the computation device.

```python
#Import packages
import torch
from PIL import Image
from transformers import AutoProcessor, CLIPModel
import faiss
import numpy as np
import requests
from datasets import load_dataset, Dataset
from tqdm import tqdm

#Define device (GPU or CPU)
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")

#Load CLIP model, processor and tokenizer
processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
```

--------------------------------

### Install git-lfs

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/OWLV2_labeled_image_dataset_with_annotations.ipynb

Installs git-lfs, a Git extension for versioning large files. This is recommended for generating large datasets.

```bash
!apt-get install git-lfs
```

--------------------------------

### Initialize VivitForVideoClassification Model

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 7 - Video and Video Processing/Vivit_Fine_tuned_Video_Classification.ipynb

Instantiates the `VivitForVideoClassification` model from a pre-trained checkpoint. Use `ignore_mismatched_sizes=True` if fine-tuning an already fine-tuned checkpoint.

```python
from transformers import VivitForVideoClassification

model = VivitForVideoClassification.from_pretrained(
    model_checkpt,
    id2label=my_id2label,
    label2id=my_label2id,
    ignore_mismatched_sizes=True,  # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
)
```

--------------------------------

### Install OpenVINO

Source: https://github.com/huggingface/computer-vision-course/blob/main/chapters/en/unit9/tools_and_frameworks.mdx

Install the OpenVINO package using pip. This is the primary method for integrating OpenVINO into your Python projects.

```bash
pip install openvino
```

--------------------------------

### Initialize teacher model

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/KnowledgeDistillation.ipynb

Load the pre-trained teacher model and evaluation tools.

```python
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
teacher_model_name = "asusevski/vit-base-patch16-224-oxford-pets"
teacher_model = AutoModelForImageClassification.from_pretrained(teacher_model_name).to(device)
teacher_model.eval()
processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
accuracy = evaluate.load("accuracy")
```

--------------------------------

### Configure Data Preprocessing and Dataloader

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/Synthetic_lung_images_hf_course.ipynb

Sets up environment parameters, image transformation pipelines, and creates a DataLoader for training.

```python
batch_size = 128
nb_gpu = 1
nb_workers = 2 # based on system resources

# GPU or CPU (Not having at least 1 GPU can prevent code from working)
device = torch.device("cuda:0" if (torch.cuda.is_available() and nb_gpu > 0) else "cpu")
n_channels = 3
image_size = 64
input_shape = (image_size, image_size)

# Set a seed for reproducibility
seed = 22
random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

# define the transform
transform = Compose(
        [
            Resize(image_size),
            CenterCrop(image_size),
            ToTensor(),
            Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ]
)

# define function
def transforms(examples):
   examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]]
   return examples

transformed_dataset = dataset.with_transform(transforms)

#create dataloader
dataloader = DataLoader(
     transformed_dataset["train"], batch_size=batch_size, shuffle=True, num_workers=nb_workers
)

real_batch = next(iter(dataloader))
```

--------------------------------

### Import necessary libraries and modules

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/transfer-learning-segmentation.ipynb

Initializes the environment by importing required libraries for data processing, model training, and evaluation.

```python
import json
import torch
import datasets
import requests
import evaluate
import numpy as np
import huggingface_hub
from PIL import Image
import albumentations as A
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
from typing import Tuple, Any
from dataclasses import dataclass
from datasets import load_dataset
import matplotlib.patches as mpatches
from huggingface_hub import hf_hub_download
from torch.utils.data import Dataset, DataLoader
from transformers import (
    MaskFormerImageProcessor,
    AutoImageProcessor,
    MaskFormerForInstanceSegmentation,
)

torch.manual_seed(42)
```

--------------------------------

### Initialize and Configure Discriminator

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 10 - Synthetic Data Creation/Synthetic_lung_images_hf_course.ipynb

Instantiates the Discriminator, handles multi-GPU distribution, and initializes weights.

```python
# Create the Discriminator
disc_net = Discriminator(nb_gpu).to(device)

# Handle multi-GPU if desired
if (device.type == 'cuda') and (nb_gpu > 1):
    disc_net = nn.DataParallel(disc_net, list(range(nb_gpu)))

# Apply the ``weights_init`` function to randomly initialize all weights
# like this: ``to mean=0, stdev=0.2``.
disc_net.apply(weights_init)

# Print the model
print(disc_net)
```

--------------------------------

### Initialize Trainer

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 3 - Vision Transformers/transfer-learning-image-classification.ipynb

Create the Trainer instance with the model, arguments, and dataset components.

```python
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=collate_fn,
    compute_metrics=compute_metrics,
    train_dataset=processed_dataset["train"],
    eval_dataset=processed_dataset["validation"],
    tokenizer=processor
)
```

--------------------------------

### Install Required Packages

Source: https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit 4 - Multimodal Models/CLIP and relatives/Image classification with CLIP.ipynb

Installs the necessary libraries for image classification using CLIP, including transformers, PyTorch, datasets, matplotlib, and scikit-learn.

```bash
pip install transformers torch datasets matplotlib scikit-learn

```