Tensorflow-DenseNet with ImageNet Pretrained Models
This is an Tensorflow implementation of DenseNet by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten with ImageNet pretrained models. The weights are converted from DenseNet-Keras Models.
The code are largely borrowed from TensorFlow-Slim Models.
Pre-trained Models
The top-1/5 accuracy rates by using single center crop (crop size: 224x224, image size: 256xN)
| Network | Top-1 | Top-5 | Checkpoints |
|---|---|---|---|
| DenseNet 121 (k=32) | 74.91 | 92.19 | model |
| DenseNet 169 (k=32) | 76.09 | 93.14 | model |
| DenseNet 161 (k=48) | 77.64 | 93.79 | model |
Usage
Follow the instruction TensorFlow-Slim Models.
Step-by-step Example of training on flowers dataset.
Downloading ans converting flowers dataset
$ DATA_DIR=/tmp/data/flowers
$ python download_and_convert_data.py \
--dataset_name=flowers \
--dataset_dir="${DATA_DIR}"
Training a model from scratch.
$ DATASET_DIR=/tmp/data/flowers
$ TRAIN_DIR=/tmp/train_logs
$ python train_image_classifier.py \
--train_dir=${TRAIN_DIR} \
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=${DATASET_DIR} \
--model_name=densenet121
Fine-tuning a model from an existing checkpoint
$ DATASET_DIR=/tmp/data/flowers
$ TRAIN_DIR=/tmp/train_logs
$ CHECKPOINT_PATH=/tmp/my_checkpoints/tf-densenet121.ckpt
$ python train_image_classifier.py \
--train_dir=${TRAIN_DIR} \
--dataset_name=flowers \
--dataset_split_name=train \
--dataset_dir=${DATASET_DIR} \
--model_name=densenet121 \
--checkpoint_path=${CHECKPOINT_PATH} \
--checkpoint_exclude_scopes=global_step,densenet121/logits \
--trainable_scopes=densenet121/logits