misc: minor updates (#75)

README.md

@@ -122,7 +122,7 @@ The models are evaluated against four different medical datasets. Note, Further
 ### Weighted KNN Accuracy
 
 | Model/Dataset | Alzheimer-MRI | LungCancer4Types | chest-xray-classification | skin-cancer |
-| :--------------- | :-----------: | :--------------: | :-----------------------: | :---------: |
+|:-----------------|:-------------:|:----------------:|:-------------------------:|:-----------:|
 | apple | 0.6777 | 0.6633 | 0.9687 | 0.7985 |
 | bioclip | 0.8952 | 0.7800 | 0.9771 | 0.7961 |
 | clip | 0.6986 | 0.6867 | 0.9727 | 0.7891 |
@@ -136,7 +136,7 @@ The models are evaluated against four different medical datasets. Note, Further
 ### Zero-shot Accuracy
 
 | Model/Dataset | Alzheimer-MRI | LungCancer4Types | chest-xray-classification | skin-cancer |
-| :--------------- | :-----------: | :--------------: | :-----------------------: | :---------: |
+|:-----------------|:-------------:|:----------------:|:-------------------------:|:-----------:|
 | apple | 0.4460 | 0.2367 | 0.7381 | 0.3594 |
 | bioclip | 0.3092 | 0.2200 | 0.7356 | 0.0431 |
 | clip | 0.4857 | 0.2267 | 0.7381 | 0.1955 |
@@ -152,7 +152,7 @@ The models are evaluated against four different medical datasets. Note, Further
 ### Image-to-image Retrieval
 
 | Model/Dataset | Alzheimer-MRI | LungCancer4Types | chest-xray-classification | skin-cancer |
-| :--------------- | :-----------: | :--------------: | :-----------------------: | :---------: |
+|:-----------------|:-------------:|:----------------:|:-------------------------:|:-----------:|
 | apple | 0.4281 | 0.2786 | 0.8835 | 0.6437 |
 | bioclip | 0.4535 | 0.3496 | 0.8786 | 0.6278 |
 | clip | 0.4247 | 0.2812 | 0.8602 | 0.6347 |
@@ -168,7 +168,7 @@ The models are evaluated against four different medical datasets. Note, Further
 ### Linear Probe Accuracy
 
 | Model/Dataset | Alzheimer-MRI | LungCancer4Types | chest-xray-classification | skin-cancer |
-| :--------------- | :-----------: | :--------------: | :-----------------------: | :---------: |
+|:-----------------|:-------------:|:----------------:|:-------------------------:|:-----------:|
 | apple | 0.5482 | 0.5433 | 0.9362 | 0.7662 |
 | bioclip | 0.6139 | 0.6600 | 0.9433 | 0.7933 |
 | clip | 0.5547 | 0.5700 | 0.9362 | 0.7704 |
@@ -190,7 +190,7 @@ This repository contains classification datasets sourced from [Hugging Face](htt
 > ⚠️ Currently, only image and image groups datasets are supported, with potential for future expansion to include video datasets.
 
 | Dataset Title | Implementation | HF Dataset |
-| :------------------------ | :------------------------------ | :----------------------------------------------------------------------------------- |
+|:--------------------------|:--------------------------------|:-------------------------------------------------------------------------------------|
 | Alzheimer-MRI | [Hugging Face][hf-dataset-impl] | [Falah/Alzheimer_MRI][Falah/Alzheimer_MRI] |
 | chest-xray-classification | [Hugging Face][hf-dataset-impl] | [trpakov/chest-xray-classification][trpakov/chest-xray-classification] |
 | LungCancer4Types | [Hugging Face][hf-dataset-impl] | [Kabil007/LungCancer4Types][Kabil007/LungCancer4Types] |
@@ -265,8 +265,7 @@ _TODO_: Some more prose about what's the difference between implementations.
 ### Hugging Face Models
 
 | Model Title | Implementation | HF Model |
-| :--------------- | :---------------------------- | :--------------------------------------------------------------------------------------------- |
-| apple | [OpenCLIP][open-model-impl] | [apple/DFN5B-CLIP-ViT-H-14][apple/DFN5B-CLIP-ViT-H-14] |
+|:-----------------|:------------------------------|:-----------------------------------------------------------------------------------------------|
 | apple | [OpenCLIP][open-model-impl] | [apple/DFN5B-CLIP-ViT-H-14][apple/DFN5B-CLIP-ViT-H-14] |
 | bioclip | [OpenCLIP][open-model-impl] | [imageomics/bioclip][imageomics/bioclip] |
 | eva-clip | [OpenCLIP][open-model-impl] | [BAAI/EVA-CLIP-8B-448][BAAI/EVA-CLIP-8B-448] |
@@ -284,7 +283,7 @@ _TODO_: Some more prose about what's the difference between implementations.
 ### Locally Trained Models
 
 | Model Title | Implementation | Weights |
-| :----------- | :-------------------------------- | :------ |
+|:-------------|:----------------------------------|:--------|
 | rsicd-encord | [LocalOpenCLIP][local-model-impl] | - |
 
 ### Add a Model from a Known Source

tti_eval/model/types/hugging_face.py

@@ -66,7 +66,10 @@ def build_embedding(self, dataloader: DataLoader) -> tuple[EmbeddingArray, Embed
  class_features = self.model.get_text_features(**inputs)
  normalized_class_features = class_features / class_features.norm(p=2, dim=-1, keepdim=True)
  class_embeddings = normalized_class_features.numpy(force=True)
- for batch in tqdm(dataloader, desc=f"Embedding dataset with {self.title}"):
+ for batch in tqdm(
+ dataloader,
+ desc=f"Embedding ({_dataset.split}) {_dataset.title} dataset with {self.title}",
+ ):
  image_features = self.model.get_image_features(pixel_values=batch["pixel_values"].to(self.device))
  normalized_image_features = (image_features / image_features.norm(p=2, dim=-1, keepdim=True)).squeeze()
  all_image_embeddings.append(normalized_image_features)

tti_eval/model/types/open_clip_model.py

@@ -66,7 +66,10 @@ def build_embedding(self, dataloader: DataLoader) -> tuple[EmbeddingArray, Embed
  _dataset: Dataset = dataloader.dataset
  text = self.tokenizer(_dataset.text_queries).to(self.device)
  class_embeddings = self.model.encode_text(text, normalize=True).numpy(force=True)
- for batch in tqdm(dataloader, desc=f"Embedding dataset with {self.title}"):
+ for batch in tqdm(
+ dataloader,
+ desc=f"Embedding ({_dataset.split}) {_dataset.title} dataset with {self.title}",
+ ):
  image_features = self.model.encode_image(batch["image"].to(self.device), normalize=True)
  all_image_embeddings.append(image_features)
  all_labels.append(batch["labels"])