Update README.md

README.md

@@ -23,187 +23,83 @@ We provide a model for classifying whale species from images of their tails and
 
 ## Model Details
 
-The model returns the three most probable cetacean species identified in the input image.
+The model takes as input a natural image of a cetacean and returns the three most probable cetacean species identified in this image.
 
 ### Model Description
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
+- **Developed by:** HappyWhale
+- **Shared by [optional]:** The Saving-Willy organization
 - **Model type:** EfficientNet
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/knshnb/kaggle-happywhale-1st-place
+- **Paper:** https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14167
 
 ## Uses
 
 This model is intended for research use cases. It is intended to be fine-tuned on new data gathered by research institutions around the World.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+### Downstream Use
 
 We think that an interesting downstream use case would be identifying whale IDs based on our model (and future extensions of it).
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
 ### Out-of-Scope Use
 
 This model is not intended to facilitate marine tourism or the exploitation of cetaceans in the wild and marine wildlife.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Install the necessary libraries to run our model (`transformers` and the extra requirements.txt):
 
-#### Metrics
+```
+pip install requirements.txt
+```
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+Use the code below to get started with the model.
 
-[More Information Needed]
+```
+import cv2
+from transformers import AutoModelForImageClassification
+
+cetacean_classifier = AutoModelForImageClassification.from_pretrained("Saving-Willy/cetacean-classifier", trust_remote_code=True)
+img = cv2.imread("tail.jpg")
+predictions = cetacean_classifier(img)
+```
+
+## Training and Evaluation Details
+
+To learn more about how the model was trained and evaluated, see [1st Place Solution of Kaggle Happywhale Competition](https://github.com/knshnb/kaggle-happywhale-1st-place).
+
+## Citation
+
+If you use this model in your research, please cite:
+
+the original model authors:
+
+```
+@article{patton2023deep,
+  title={A deep learning approach to photo--identification demonstrates high performance on two dozen cetacean species},
+  author={Patton, Philip T and Cheeseman, Ted and Abe, Kenshin and Yamaguchi, Taiki and Reade, Walter and Southerland, Ken and Howard, Addison and Oleson, Erin M and Allen, Jason B and Ashe, Erin and others},
+  journal={Methods in ecology and evolution},
+  volume={14},
+  number={10},
+  pages={2611--2625},
+  year={2023},
+  publisher={Wiley Online Library}
+}
+```
+
+the HappyWhale project:
+
+```
+@misc{happy-whale-and-dolphin,
+    author = {Ted Cheeseman and Ken Southerland and Walter Reade and Addison Howard},
+    title = {Happywhale - Whale and Dolphin Identification},
+    year = {2022},
+    howpublished = {\url{https://kaggle.com/competitions/happy-whale-and-dolphin}},
+    note = {Kaggle}
+}
+```