pushed the quantized model, edit readme.

README.md

@@ -1,3 +1,104 @@
 ---
 license: mit
+language:
+  - en
+model_name: BioCLIP-2 Quantized
+model_description: "BioCLIP-2 Quantized is a quantized version of BioCLIP-2, a foundation model for biological organismal images. It is trained on TreeOfLife-200M on the basis of a CLIP model (ViT-14/L) pre-trained on LAION-2B. BioCLIP-2 yields state-of-the-art performance in recognizing various species. More importantly, it demonstrates emergent properties beyond species classification after extensive hierarchical contrastive training."
+base_model:
+  - imageomics/bioclip-2
+tags:
+- bioclip
+- bioclip-2
+- biology
+- CV
+- images
+- imageomics
+- clip
+- species-classification
+- biological visual task
+- multimodal
+- animals
+- species
+- taxonomy
+- rare species
+- endangered species
+- evolutionary biology
+- knowledge-guided
+- zero-shot-image-classification
+datasets:
+- imageomics/TreeOfLife-200M
+- GBIF
+- bioscan-ml/BIOSCAN-5M
+- EOL
+- FathomNet
 ---
+
+# Model Card for BioCLIP-2 Quantized
+BioCLIP-2 Quantized is a quantized version of BioCLIP-2, a foundation model for biological organismal images [read more about the model here](https://huggingface.co/imageomics/bioclip-2). The quantized model is designed to reduce memory usage and improve inference with cost of losing a little accuracy.
+
+## Model Description
+BioCLIP-2 Quantized is dynamically quantized from the original BioCLIP-2 model. The Pytorch `onnx.export` function is used to convert the original model to ONNX format, and then the `onnxruntime.quantization.quantize_dynamic` function is used to perform dynamic quantization on the ONNX model with `weight_type` set to `QuantType.QInt8`.
+
+## Inference
+To use the quantized model for inference, you can use the `onnxruntime` library. Here is an example code snippet:
+
+```python
+
+import onnxruntime as ort
+import torch
+import torch.nn.functional as F
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+
+# Load the quantized model
+ort_session = ort.InferenceSession("path/to/bioclip-2-quantized.onnx", providers=['CPUExecutionProvider'])
+
+# only return one label
+k = 1
+
+# Preprocess image
+img_tensor = preprocess_img(img).unsqueeze(0)
+img_np = img_tensor.numpy()
+
+# Run ONNX inference
+input_name = session.get_inputs()[0].name
+output_name = session.get_outputs()[0].name
+
+img_features_np = session.run([output_name], {input_name: img_np})[0]
+
+# Convert back to torch for compatibility with existing code
+img_features = torch.from_numpy(img_features_np)
+img_features = F.normalize(img_features, dim=-1)
+
+# optional: for the open ended classification that you need a text embedding:
+txt_emb = torch.from_numpy(
+    np.load(
+        hf_hub_download(
+            repo_id="imageomics/TreeOfLife-200M",
+            filename="embeddings/txt_emb_species.npy",
+            repo_type="dataset",
+        )
+    )
+)
+# or you can skip this and use zero-shot classification with your own text inputs
+
+# Use the same text embeddings and logit scale from the original model (logit_scale of the main model: 100.00000762939453)
+logits = (model.logit_scale.exp() * img_features @ txt_emb).squeeze()
+probs = F.softmax(logits, dim=0)
+
+topk = probs.topk(k)
+prediction_dict = {
+    format_name(*txt_names[i]): prob
+    for i, prob in zip(topk.indices, topk.values)
+}
+
+print(prediction_dict)
+```
+
+## Tradeoff
+The Model got tested on [Animals and Plants dataset by Nguyen Le Truong Thien](https://www.kaggle.com/datasets/nguyenletruongthien/animals-and-plants-dataset) for open-ended species classification and the highest probability class is selected as the predicted class and compared with the main BioCLIP-2 model. The results are as follows:
+![alt text](./doc/comparison_to_main_model.png)
+
+## Acknowledgements
+The main model is developed by the Imageomics Institute team. And the current model is just a quantized version of the main model to reduce memory usage and improve inference speed and make the model more accessible.

onnx/bioclip2_model_int8.onnx

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