Merge pull request #484 from microsoft/staging

Syncing staging <> master branches

NOTICE.txt

@@ -281,5 +281,210 @@ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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scenarios/similarity/README.md

@@ -2,9 +2,29 @@
 
 This directory provides examples and best practices for building image similarity systems. Our goal is to enable the users to bring their own datasets and train a high-accuracy model easily and quickly. To this end, we provide example notebooks with pre-set default parameters shown to work well on a variety of datasets, and extensive documentation of common pitfalls, best practices, etc.
 
-The majority of state-of-the-art systems for image similarity use DNNs to compute a representation of an image (e.g. a vector of 512 floating point values), and define similarity between two images by measuring the L2 distance between the respective DNN representations.
+Image retrieval example showing the query image on the left, and the 6 images deemed most similar to its right:  
+<p align="center">
+  <img src="./media/imsim_example1.jpg" height="175" alt="Image retrieval example"/>
+</p>
 
-A major difference between modern image similarity approaches is how the DNN is trained. A simple but quite powerful approach is to use a standard image classification loss - this is the approach taken in this repository, and explained in the [classification](../classification/README.md) folder. More accurate similarity measures are based on DNNs which are trained explicitly for image similarity, such as the [FaceNet](https://arxiv.org/pdf/1503.03832.pdf) work which uses a Siamese network architecture. FaceNet-like approaches will be added to this repository at a later point.
+## State-of-the-art
+
+The majority of state-of-the-art systems for image similarity use DNNs to compute a representation of an image (e.g. a vector of 512 floating point values). The similarity between two images is then defined as the cosine or the L2 distance between their respective DNN representations.
+
+The main difference between recent image similarity publications is how the DNN is trained. A simple but surprisingly powerful approach is to use a standard image classification loss - this is the approach taken in the [01_training_and_evaluation_introduction.ipynb](01_training_and_evaluation_introduction.ipynb) notebook, and explained in the [classification](../classification/README.md) folder. More accurate models are typically trained explicitly for image similarity using Triplet Learning such as the well-known [FaceNet](https://arxiv.org/pdf/1503.03832.pdf) paper. While triplet-based approaches achieve good accuracies, they are conceptually complex, slower, and more difficult to train/converge due to issues such as how to mine good triplets.
+
+Instead, the notebook [02_state_of_the_art.ipynb](02_state_of_the_art.ipynb) implements the BMVC 2019 paper "[Classification is a Strong Baseline for Deep Metric Learning](https://arxiv.org/abs/1811.12649)" which shows that this extra overhead is not necessary. Indeed, by making small changes to standard classification models, the authors achieve results which are comparable or better than the previous state-of-the-art on three common research datasets.
+
+Below are a subset of popular papers in the field with reported accuracies on standard benchmark datasets:
+
+| Paper | Year	| Uses triplet learning |	Recall@1 CARS196 | Recall@1 CUB200-2011	| Recall@1 SOP |
+| ----- | ----- | --------------------- | ---------------- | -------------------- | ------------ |
+| [Deep Metric Learning via Lifted Structured Feature Embedding](https://arxiv.org/abs/1511.06452) | CVPR 2016	|  | 49%	| 47%	| 62% |				
+| [Deep Metric learning with angular loss](https://arxiv.org/abs/1708.01682)                       | ICCV 2017	| Yes |	71%	| 55%	| 71%	|
+| [Sampling Matters in Deep Embedding Learning](https://arxiv.org/abs/1706.07567)                  | ICCV 2017	| Yes |	80%	| **64%**	| 73%	|
+| [No Fuss Distance Metric Learning using Proxies](https://arxiv.org/abs/1703.07464)               | ICCV 2017	| Yes	| 73%	| 49%	| 74% |
+| [Deep metric learning with hierarchical triplet loss](https://arxiv.org/abs/1810.06951)          | ECCV 2018  |	Yes	| 81%	| 57%	| 75%	|
+| [Classification is a Strong Baseline for DeepMetric Learning](https://arxiv.org/abs/1811.12649) <br> (Implemented in this repository)  | BMVC 2019	| No  | **84%** (512-dim) <br> **89%** (2048-dim) |	61% (512-dim) <br> **65%** (2048-dim) |	**78%** (512-dim) <br> **80%** (2048-dim) |			
 
 
 ## Frequently asked questions
@@ -20,6 +40,7 @@ We provide several notebooks to show how image similarity algorithms can be desi
 | --- | --- |
 | [00_webcam.ipynb](00_webcam.ipynb)| Quick start notebook which demonstrates how to build an image retrieval system using a single image or webcam as input.
 | [01_training_and_evaluation_introduction.ipynb](01_training_and_evaluation_introduction.ipynb)| Notebook which explains the basic concepts around model training and evaluation, based on using DNNs trained for image classification.|
+| [02_state_of_the_art.ipynb](02_state_of_the_art.ipynb) | Implementation of the state-of-the-art BMVC 2019 paper mentioned in the table above. |
 | [11_exploring_hyperparameters.ipynb](11_exploring_hyperparameters.ipynb)| Finds optimal model parameters using grid search. |
 | [12_fast_retrieval.ipynb](12_fast_retrieval.ipynb)| Fast image retrieval using nearest neighbor search. |
 
@@ -27,4 +48,3 @@ We provide several notebooks to show how image similarity algorithms can be desi
 ## Coding guidelines
 
 See the [coding guidelines](../../CONTRIBUTING.md#coding-guidelines) in the root folder.
-

tests/conftest.py

@@ -33,6 +33,7 @@ from utils_cv.detection.model import (
     _extract_od_results,
     _apply_threshold,
 )
+from utils_cv.similarity.data import Urls as is_urls
 
 
 def path_classification_notebooks():
@@ -126,6 +127,7 @@ def similarity_notebooks():
         "01": os.path.join(
             folder_notebooks, "01_training_and_evaluation_introduction.ipynb"
         ),
+        "02": os.path.join(folder_notebooks, "02_state_of_the_art.ipynb"),
         "11": os.path.join(
             folder_notebooks, "11_exploring_hyperparameters.ipynb"
         ),
@@ -196,7 +198,7 @@ def tmp_session(tmp_path_factory):
         yield td
 
 
-# ------|-- Classification/Similarity ---------------------------------------------
+# ------|-- Classification ---------------------------------------------
 
 
 @pytest.fixture(scope="session")
@@ -728,3 +730,16 @@ def workspace_region(request):
 #         os.path.join(im_paths, "can", im_name) for im_name in can_im_paths
 #     ][0:5]
 #     return can_im_paths
+
+
+# ------|-- Similarity ---------------------------------------------
+
+@pytest.fixture(scope="session")
+def tiny_is_data_path(tmp_session) -> str:
+    """ Returns the path to the tiny fridge objects dataset. """
+    return unzip_url(
+        is_urls.fridge_objects_retrieval_tiny_path,
+        fpath=tmp_session,
+        dest=tmp_session,
+        exist_ok=True,
+    )

tests/integration/similarity/test_integration_similarity_notebooks.py

@@ -25,6 +25,21 @@ def test_01_notebook_run(similarity_notebooks):
     assert nb_output.scraps["median_rank"].data <= 15
 
 
+@pytest.mark.notebooks
+@pytest.mark.linuxgpu
+def test_02_notebook_run(similarity_notebooks):
+    notebook_path = similarity_notebooks["02"]
+    pm.execute_notebook(
+        notebook_path,
+        OUTPUT_NOTEBOOK,
+        parameters=dict(PM_VERSION=pm.__version__),
+        kernel_name=KERNEL_NAME,
+    )
+
+    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
+    assert nb_output.scraps["recallAt1"].data >= 70
+
+
 @pytest.mark.notebooks
 @pytest.mark.linuxgpu
 def test_11_notebook_run(similarity_notebooks, tiny_ic_data_path):

tests/unit/similarity/test_similarity_notebooks.py

@@ -49,6 +49,24 @@ def test_01_notebook_run(similarity_notebooks, tiny_ic_data_path):
     )
 
 
+@pytest.mark.notebooks
+def test_02_notebook_run(similarity_notebooks, tiny_is_data_path):
+    notebook_path = similarity_notebooks["02"]
+    pm.execute_notebook(
+        notebook_path,
+        OUTPUT_NOTEBOOK,
+        parameters=dict(
+            PM_VERSION=pm.__version__,
+            DATA_PATH=tiny_is_data_path,
+            EPOCHS_HEAD=1,
+            EPOCHS_BODY=1,
+            BATCH_SIZE=1,
+            IM_SIZE=50,
+        ),
+        kernel_name=KERNEL_NAME,
+    )
+
+
 @pytest.mark.notebooks
 def test_11_notebook_run(similarity_notebooks, tiny_ic_data_path):
     notebook_path = similarity_notebooks["11"]

utils_cv/similarity/data.py

@@ -4,12 +4,26 @@
 import numpy as np
 import random
 from typing import List, Dict
+from urllib.parse import urljoin
 
 from fastai.data_block import LabelList
 
 from utils_cv.similarity.metrics import vector_distance
 
 
+class Urls:
+    # base url 
+    base = "https://cvbp.blob.core.windows.net/public/datasets/image_similarity/"
+
+    # traditional datasets
+    fridge_objects_retrieval_path = urljoin(base, "fridgeObjectsImageRetrieval.zip")
+    fridge_objects_retrieval_tiny_path = urljoin(base, "fridgeObjectsImageRetrievalTiny.zip")
+
+    @classmethod
+    def all(cls) -> List[str]:
+        return [v for k, v in cls.__dict__.items() if k.endswith("_path")]
+
+
 class ComparativeSet:
     """Class to represent a comparative set with a query image, 1 positive image
        and multiple negative images.

utils_cv/similarity/model.py

@@ -126,11 +126,18 @@ def compute_features_learner(
             "Dataset_type needs to be of type DatasetType.Train, DatasetType.Valid, DatasetType.Test or DatasetType.Fix."
         )
 
+    # Update what data the learner object is using
+    tmp_data = learn.data
+    learn.data = data
+
     # Compute features
     featurizer = SaveFeatures(embedding_layer)
     learn.get_preds(dataset_type)
     feats = featurizer.features[:]
 
+    # Set data back to before
+    learn.data = tmp_data
+
     # Get corresponding image paths
     assert len(feats) == len(label_list)
     im_paths = [str(x) for x in label_list]