Update README.md

README.md

@@ -1,3 +1,175 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- timm/eva02_large_patch14_448.mim_m38m_ft_in22k_in1k
+library_name: fastai
+tags:
+- Coleoptera
+- Taxonomy
+- Biology
+- Cicindelidae
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+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.
+
+## Model Details
+
+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.
+
+The training included several tricks to allow multilabel training with an imbalanced dataset, see the publication for details.
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [XXXX]
+- **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 not 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 [XXXX] 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. -->
+
+
+#### 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%
+
+## Usage
+
+The learner can be loaded to fastai with:
+
+```{python}
+from huggingface_hub import from_pretrained_fastai
+learn = from_pretrained_fastai("brunoasm/eva02_large_patch14_448.Cicindela_ID_FMNH")
+```
+
+To avoid loading a pickle file and loading the model weights only, you can use:
+```{python}
+import requests
+import io
+from fastai.vision.all import *
+
+def load_model_from_url(learn, url):
+    try:
+        print("Downloading model...")
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        
+        buffer = io.BytesIO(response.content)
+        learn.load(buffer, with_opt=False)
+        print("Model loaded successfully!")
+        
+    except Exception as e:
+        print(f"Error loading model: {e}")
+
+url = 'https://huggingface.co/brunoasm/eva02_large_patch14_448.Cicindela_ID_FMNH/resolve/main/pytorch_model.bin'
+learn = vision_learner(dls, "eva02_large_patch14_448.mim_m38m_ft_in22k_in1k")
+
+response = requests.get(url, stream=True)
+response.raise_for_status()
+buffer = io.BytesIO(response.content)
+learn.load(buffer, with_opt=False)
+
+```
+where `dls` is a previously created dataloader.
+
+
+## Citation 
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+[XXXX]. DrawerDissect: Whole-drawer insect imaging, segmentation, and transcription using AI.
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+
+## Model Card Authors
+
+[XXXX].