Upload Cicindela multilabel classification model (FastAI)

README.md

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 ---
-license: apache-2.0
-base_model:
-- timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k
-library_name: fastai
 tags:
-- Coleoptera
-- Taxonomy
-- Biology
-- Cicindelidae
+- fastai
 ---
 
-# Model Card for Model ID
+# Amazing!
 
-<!-- Provide a quick summary of what the model is/does. -->
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
 
-This model was trained on all specimens of *Cicindela* tiger beetles from the Field Museum. It is a multilabel model able to identify species and subspecies. See the model config file for all labels included and the publication for metrics.
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
 
-## Model Details
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
 
-This model is based on the pre-trained [eva02_large_patch14_448](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k) from the timm library.
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
 
-The training included several tricks to allow multilabel training with an imbalanced dataset, see the publication for details.
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
 
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** Bruno A. S. de Medeiros, Negaunee Assistant Curator of Pollinating Insects, Field Museum.
-- **Model type:** Image classification
-- **License:** Apache 2.0
-- **Finetuned from model [optional]:** [eva02_large_patch14_448](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k)
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Paper [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-Identification of pinned *Cicindela* specimens.
-
-
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-This model will fail to make predictions on species not present in the FMNH collection. It is also unlikely to perform well for specimens that are note pinned.
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-The model is only expected to perform well for images of pinned tiger beetles.
-
-
-## 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. -->
-
-Data generated by Elizabeth Postema using DrawerDissect on Field Museum specimens, see the publication for details.
-
-### 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:** fp16 mixed precision
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-A subset of the specimens was held as a test set. See publication for details
-
-
-#### Metrics
-
-Metrics in the test set:
-
-Dataset Overview:
-- Total taxa analyzed: 193
-- Species: 115 (59.6%)
-- Subspecies: 78 (40.4%)
-
-Performance Summary:
-
-Species:
-- Specimen-weighted precision: 96.8%
-- Specimen-weighted recall: 80.9%
-- Specimen-weighted precision: 97.0%
-- Specimen-weighted recall: 96.4%
-
-Subspecies:
-- Specimen-weighted precision: 89.0%
-- Specimen-weighted recall: 66.5%
-- Specimen-weighted precision: 85.0%
-- Specimen-weighted recall: 89.0%
-
-
-
-## Citation 
-
-<!-- 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]
+# Model card
 
+## Model description
+More information needed
 
-## Model Card Authors
+## Intended uses & limitations
+More information needed
 
-Bruno de Medeiros, Negaunee Assistant Curator of Pollinating Insects, Field Museum
+## Training and evaluation data
+More information needed

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a8df42b96f136acf66e0148cbdf605656b1e2d80ec326032401128249e211131
+size 1219774982

pyproject.toml

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+[build-system]
+requires = ["setuptools>=40.8.0", "wheel", "python=3.11.11", "fastai=2.8.0", "fastcore=1.8.0"]
+build-backend = "setuptools.build_meta:__legacy__"