
# Fatima Challenge

- Dataset: I'm using the `cats_vs_dogs` dataset from **kaggle** instead of huggingface `datasets` since `cats_vs_dogs` in `datasets` have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into `train` and `test` folders.
- Model: I'm using `ResNet50` with `pretrained=True` from `pytorch`. The choice of optimizer is `Adam` with `lr=1e-4`. I had tried `AdamW` before coming back to `Adam`. I also added the `lr_scheduler` to reconfigure the learning rate. 
- Number of epochs: I'm using 10 epochs in total. I had tried with 20, however, only the `train_acc` increases by ~1%, so it does not worth trying for an increasing amount of training time.

Model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1FgVFvmjFn-tGk1pzVUTfra6UyafcuPLV?usp=sharing)
{"license": "mit"}
license:mit region:us
TAGS
#license-mit #region-us 


# Fatima Challenge

- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into 'train' and 'test' folders.
- Model: I'm using 'ResNet50' with 'pretrained=True' from 'pytorch'. The choice of optimizer is 'Adam' with 'lr=1e-4'. I had tried 'AdamW' before coming back to 'Adam'. I also added the 'lr_scheduler' to reconfigure the learning rate. 
- Number of epochs: I'm using 10 epochs in total. I had tried with 20, however, only the 'train_acc' increases by ~1%, so it does not worth trying for an increasing amount of training time.

Model: ![Open In Colab](URL
# Fatima Challenge

- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into 'train' and 'test' folders.
- Model: I'm using 'ResNet50' with 'pretrained=True' from 'pytorch'. The choice of optimizer is 'Adam' with 'lr=1e-4'. I had tried 'AdamW' before coming back to 'Adam'. I also added the 'lr_scheduler' to reconfigure the learning rate. 
- Number of epochs: I'm using 10 epochs in total. I had tried with 20, however, only the 'train_acc' increases by ~1%, so it does not worth trying for an increasing amount of training time.

Model: ![Open In Colab](URL
TAGS
#license-mit #region-us 
 # Fatima Challenge

- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into 'train' and 'test' folders.
- Model: I'm using 'ResNet50' with 'pretrained=True' from 'pytorch'. The choice of optimizer is 'Adam' with 'lr=1e-4'. I had tried 'AdamW' before coming back to 'Adam'. I also added the 'lr_scheduler' to reconfigure the learning rate. 
- Number of epochs: I'm using 10 epochs in total. I had tried with 20, however, only the 'train_acc' increases by ~1%, so it does not worth trying for an increasing amount of training time.

Model: ![Open In Colab](URL
passage: TAGS
#license-mit #region-us 
# Fatima Challenge

- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into 'train' and 'test' folders.
- Model: I'm using 'ResNet50' with 'pretrained=True' from 'pytorch'. The choice of optimizer is 'Adam' with 'lr=1e-4'. I had tried 'AdamW' before coming back to 'Adam'. I also added the 'lr_scheduler' to reconfigure the learning rate. 
- Number of epochs: I'm using 10 epochs in total. I had tried with 20, however, only the 'train_acc' increases by ~1%, so it does not worth trying for an increasing amount of training time.

Model: ![Open In Colab](URL