prototypical-networks-few-shot-learning

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prototypical-networks-few-shot-learning

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Pytorch implementation of prototypical networks in few shot learning

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# Few-Shot learning with Prototypes Networks
Simple implementation of prototypical networks in few shot learning

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<details>
<summary>Installation</summary>

Create a conda/virtualenv with all necessary packages:

### Conda

`conda create --name fs-learn`

`conda activate fs-learn`

`conda install pytorch torchvision torchaudio -c pytorch`

`conda install --file requirements.txt`

### Venv

`python3 -m pip install virtualenv`

`virtualenv venv-fs-learn`

`source venv/bin/activate`

`python3 -m pip install torch torchvision`

`python3 -m pip install ./requirements.txt`

</details>

<details>
<summary>Datasets</summary>

We used 3 main classification datasets:
- **mini_imagenet**: a collection of 100 real-world objects classes as rgb images.
 - total: 60,000
 - splits: 64 train, 16 val, 20 test (according to Vinyals et al)
 - Used in paper
 - ![](https://raw.githubusercontent.com/fabian57fabian/prototypical-networks-few-shot-learning/main/docs/images/mini_imagenet_dataset.png)
- **omniglot**: a collection of 1623 classes of handwritted characters. Each image is then rotated 3 more times by 90 degrees.
 - total: 32460 real, plus 4 rotations per image
 - splits: 1032 train, 172 val, 464 test (according to Vinyals et al)
 - Used in paper
 - ![](https://raw.githubusercontent.com/fabian57fabian/prototypical-networks-few-shot-learning/main/docs/images/omniglot_dataset.jpg)
- **flowers102**: a collection of 102 real-world flowers classes as rgb images.
 - total: 32460 real, plus 4 rotations per image
 - splits: 64 train, 16 val, 22 test (random seed for splits)
 - **NOT** Used in paper
 - ![](https://raw.githubusercontent.com/fabian57fabian/prototypical-networks-few-shot-learning/main/docs/images/flowers102_dataset.png)
- **stanfors_cars**: a collection of 192 real-world cars classes as rgb images.
 - total: 9999999
 - splits: 60% train, 20% val, 30% test (random seed for splits)
 - **NOT** Used in paper
 - ![](https://raw.githubusercontent.com/fabian57fabian/prototypical-networks-few-shot-learning/main/docs/images/stanford_cars_dataset.png)
</details>

<details open>
<summary>Usage</summary>

The starter script is **meta_train.py** that has all necessary params to meta-train and meta-test on a dataset.

To replicate the results, launch this training (writes to runs/train_X):

```bash
python meta_train.py --data mini_imagenet \
                --episodes 200 \
                --device cuda \
                --num-way 30 \
                --query 15 \
                --shot 5 \
                --val-num-way 5 \
                --iterations 100 \
                --adam-lr 0.001 \
                --adam-step 20 \
                --adam-gamma 0.5 \
                --metric "euclidean" \
                --save-period 5 \
                --patience 10 \
                --patience-delta 0.01
```

Implemented datasets are [omniglot, mini_imagenet, flowers102, stanford_cars]:

To **train** with your **own custom dataset**, set --dataset toy our dataset folder. 
 
Rember, your custom dataset should have this format:

```bash
├── train
│   ├── class1
│   │   ├── img1.jpg
│   │   ├── ...
│   ├── class2
│   │   ├── ...
│   ├── ...
├── val
│   ├── class3
│   │   ├── img57.jpg
│   │   ├── ...
│   ├── class4
│   │   ├── ...
│   ├── ...
├── test
│   ├── class5
│   │   ├── img182.jpg
│   │   ├── ...
│   ├── class6
│   │   ├── ...
│   ├── ...
```

To meta-test, use **meta_test.py** script:

```bash
python meta_test.py --model "your_model_or_pretrained.py" \
                --data mini_imagenet \
                --iterations 100 \
                --device cuda \
                --val-num-way 15 \
                --query 15 \
                --shot 5 \
                --metric "euclidean"
```

To learn centroids for new data, use **learn_centroids.py** script (writes to runs/centroids_Y):

```bash
python learn_centroids.py --model "your_model_or_pretrained.py" \
                --data your_folder_with_classes_of_images \
                --imgsz 64 \
                --channels 3 \
                --device cuda
```

This will take all classes inside _your_folder_with_classes_of_images_ dir and calculate centroids for classification task.

To use centroids for classification on new images, use **predict.py** script (outputs results):

```bash
python predict.py --model "your_model_or_pretrained.py" \
                --centroids runs/centroids_0 \
                --data a_path_with_new_images \
                --imgsz 64 \
                --device cuda
```

This will perform predictions by printing out all classes based on images in _a_path_with_new_images_ .

</details>

<details open>
<summary>Experiments</summary>

### Training datasets info

| Dataset | Images (shape) | Embeddings (shape) | Duration (Colab T4) |
|---------|------------------------|----------------------------|-----------------------------|
| mini_imagenet | (84, 84, 3) | (batch, 1600) | gpu / 1h43m |
| omniglot | (28, 28, 1) | (batch, 60) | gpu / 2h32 |
| flowers102 | (74, 74, 3) | (batch, 1024) | gpu / 58m |
| stanford_cars | (90, 90, 3) | (batch, 1024) | gpu / 1h52m |

### 1-shot vs few-shot

Lots of experiments were done using basic paper's data by replicating the training.
All of these uses **nway=30**, **epochs=200** and **iterations_per_epoch=100** for training.
Then evaluation is performed in different n-ways and k-shots.

| Dataset | Paper res 5-way 5-shot (Acc) | Our res 5-way 5-shot (Acc) | Paper res 5-way 1-shot (Acc) | Our res 5-way 1-shot (Acc) |
|---------------|-----------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|
| mini_imagenet | 68.20 | [63.62](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/mini_imagenet/train_5shot.png) | 49.42 | [46.13](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/mini_imagenet/train_1shot.png) |
| omniglot | 98.80 | [97.77](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/omniglot/train_5shot.png) | 98.8 | [91.93](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/omniglot/train_1shot.png) | 
| flowers102 | / | [84.48](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/flowers102/train_5shot.png) | / | [56.08](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/flowers102/train_1shot.png) | 
| stanford_cars | / | [51.87](https://github.com/fabian57fabian/fewshot-learning-prototypical-networks/blob/main/results/stanford_cars/train_5shot.png) | / | / |

### Euclidean vs cosine distances

Cosine experiments were done on 5-way 5-shot configurations.
Same results for similar 1-shot and 20-way trainings.

| Dataset | Cosine (acc) | Euclidean (acc) |
|---------------|---------------------|-------------------------|
| mini_imagenet | 22.36 | **63.62** |
| omniglot | 23.48 | **97.77** |
| flowers102 | 82.89 | **84.48** |
| stanford_cars | ____ | **51.87** |

</details>

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