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model = create_model()


print("\n[INFO] Ready to train. Training is starting!\n")


callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)   # limitation to stop somewhere early


hist = model.fit(train_generator, validation_data=val_generator, epochs=30, callbacks=[callback])

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# create a model


def create_model():


   METRICS = [
   'accuracy',
   tf.keras.metrics.Precision(name='precision'),
   tf.keras.metrics.Recall(name='recall'),

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# create training and validation data generators


IMG_SIZE = (224,224)
BATCH_SIZE = 8


def train_val_generators(train_dir, val_dir):
   train_gen = ImageDataGenerator(rescale=1/255.,
                                 horizontal_flip=True,

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# paths to training and validation data
train_dir = os.path.join(path/to/save/bach_data, "/bach-train/training")
val_dir = os.path.join(path/to/save/bach_data, "/bach-train/validation")
print(train_dir)

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# Import class weights function
from sklearn.utils.class_weight import compute_class_weight

# Calculate class weights
class_weights = compute_class_weight('balanced', np.unique(y_train), y_train)

# Create a dictionary to map class indices to their respective weights
class_weight_dict = dict(zip(np.unique(y_train), class_weights))

# Define and train a classifier with class weights

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import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt

# Set the path to your dataset directory
dataset_directory = r"Path to your folder"

# Create an ImageDataGenerator with augmentation parameters

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FROM picsellia/cuda:11.7.1-cudnn8-ubuntu20.04
COPY ./requirements.txt .
ARG REBUILD_ALL
RUN python3.10 -m pip install -r requirements.txt --no-cache-dir
ARG REBUILD_PICSELLIA
RUN python3.10 -m pip install picsellia --upgrade
WORKDIR /picsellia
COPY . ./
ENTRYPOINT ["run", "train.py"]

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# Get the model that will receive this new version
my_model = client.get_model(
name=model_name,
)
experiment.export_in_existing_model(my_model)

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model_name = "detr-resnet-50_"
model_description = "Finetuned DETR model"
if 'api_token' not in os.environ:
    raise Exception("You must set an api_token to run this image")
api_token = os.environ["api_token"]
if "host" not in os.environ:
    host = "https://trial.picsellia.com"
else:
    host = os.environ["host"]

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experiment.compute_evaluations_metrics(inference_type=InferenceType.OBJECT_DETECTION)