TFLite model maker object detection training is too slow in colab
@pjpratik Have you checked this issue in colab? Hi @dsbyprateekg 

Sorry for the delayed response.

I have tried the [object detection](https://www.tensorflow.org/lite/models/modify/model_maker/object_detection) tutorial on model maker on colab and found no issue while training. I have used `efficientdet_lite0` as the model with 5 epochs.

Please find the gist [here](https://colab.research.google.com/gist/pjpratik/fee8613ca6ddddbed4299479488abbb9/model-maker-object-detection-tutorial.ipynb) and let us know if it helps. 

Have you enabled GPU and tried with lighter versions as well?

Thanks. @pjpratik when I used your shared gist to install, I got following logs-
![image](https://github.com/tensorflow/tensorflow/assets/30830541/603fb2f7-a819-463e-82f1-04fcd3834f4a)

But when I checked version of tflite model maker, it showed me following error-
![image](https://github.com/tensorflow/tensorflow/assets/30830541/2c694d95-aeca-4789-82bf-d31d0ced1a18)

My colab env is as below-
![image](https://github.com/tensorflow/tensorflow/assets/30830541/100ec3df-48c0-4515-a0fc-025ddef18b9f)

When I tried to run install command explicitly , error is-
![image](https://github.com/tensorflow/tensorflow/assets/30830541/a43d3917-018f-456d-9745-3f27296ef6ca)
 Hi @dsbyprateekg 

Have you tried enabling `fallback runtime option` ?

Please check [this](https://github.com/tensorflow/tensorflow/issues/60431#issuecomment-1533628479) work around for the installation.

Thanks. Hi @pjpratik , After enabling the fallback option I am able to install but my issue is the same.
It is taking too much time to complete one epoch.
My current dataset has 42000 images and previously I did the training with 32000 images. That time it took 2-2.5 minutes to complete one epoch. 

Do you think it is GDrive+Colab issue due to large size of images? Hi @dsbyprateekg 

There may be possibility for training on large number of images as the performance is varying on reduced set of images. It is suggested to  use the lighter models when there are resource constraints as large models tend to have more parameters which may lead to longer training times.

Thanks. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60637">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60637">No</a>

<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

TFlite model maker 0.4.2

### Custom Code

No

### OS Platform and Distribution

colab

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current Behaviour?

In colab I have enabled 'fallback runtime version' since with Python 3.10 model maker was not installed successfully.
After enabling this option I am able to install it successfully.
Nut when I started a object detection training with 'efficientnet_lite4', it is showing `35/2122 [..............................] - ETA: 6:08:03 - loss: 0.7095 - accuracy: 0.5509` which is keep increasing.

Please help to solve this issue.


### Standalone code to reproduce the issue

```shell
My training command is as below-
`model = image_classifier.create(train_data, validation_data=validation_data, model_spec=model_spec.get('efficientnet_lite4'), batch_size=32, epochs=50, train_whole_model=True, use_augmentation=False, use_hub_library=False, model_dir='/content/drive/MyDrive/expi')`
```


### Relevant log output

_No response_</details>